Retail Mortgage Backed Securities, Commercial Asset Backed Securities and Corporate Bonds: a Credit Spread Comparison +

Size: px
Start display at page:

Download "Retail Mortgage Backed Securities, Commercial Asset Backed Securities and Corporate Bonds: a Credit Spread Comparison +"

Transcription

1 Retal Mortgage Backed Securtes, Commercal Asset Backed Securtes and Corporate Bonds: a Credt Spread Comparson + LORIANA PELIZZON * Unversty of Padua ENRICO RETTORE Unversty of Padua EMANUELA SOTTANA Fnanzara Internazonale Abstract In ths paper we nvestgate whether and how Retal Mortgage Backed Securtes (RMBS), Commercal Asset Backed Securtes (CABS) n the European market dffer from other more tradtonal nvestment products, n partcular n terms of the trade-off between rsk and return. Therefore n ths paper we compared credt spread on RMBS and CABS to credt spread on corporate bonds wthn the same ratng class and fnd some explanaton of the dfferences between them. We fnd that the market requres the same remuneraton to nvest n a bond of a sngle frm and n a bond on a portfolo wth the same ratng, f ths ratng s hgh. On the contrary, the market requres a dfferent remuneraton f the ratng s low. Moreover, the spread s not statstcally sgnfcant between Retal Mortgage Backed Securtes and Commercal Asset backed securtes when the ratng s hgh, but s hgher for Commercal Asset Backed Securtes f the ratng s low. Potental explanatons are provded. December We gratefully acknowledge conversatons wth Stephen Schaefer, Rajv Guha and Davde Menn. * Dpartmento d Scenze Economche, Unversty of Padua, Va del Santo 33, Padova, Italy, Phone , Fax , pelzzon@decon.unpd.t

2 INTRODUCTION Securtzaton s one of the most nnovatve fnancal technques; t conssts of a transformaton of llqud assets nto negotable securtes. The structure of securtzaton may dffer sgnfcantly from one ssue to another, but the bass scheme s always the same: the orgnator sells part of the assets he has n hs balance sheet to a Specal Purpose Vehcle (SPV), whch s a company created ad hoc n order to securtze the assets. Then the SPV ssues bonds, the Asset-Backed Securtes (ABS), n order to fnance the asset purchase. Securtzaton has been spreadng n Europe very fast snce the last years: n 1996 ABS ssues accounted for 37.2 bllons, whle n 2001 they reached bllons, almost doubled compared to 2000 level 1. A reason for ths exponental growth can be found n the fact that many European countres ntroduced new securtzaton laws durng the 1990s, gvng a steady legal framework to ths knd of transactons. In partcular Italan ABS market has grown quckly and became the second largest n Europe, snce the ntroducton of law 130/99 namely the Securtzaton Law. The advantages that make securtzaton attractve to orgnators are several: captal relef, lqudty, assets-labltes management, fundng, for example. Moreover securtzaton can be a useful tool for transferrng credt rsk because the orgnator may reduce the geographcal and ndustry concentraton of hs assets as well as concentraton of ndvdual exposure. Consderng the spread of securtzaton and thus the growth of ABS market n Europe n the last few years, t seems nterestng to nvestgate whether and how Retal Mortgage Backed Securtes (RMBS), Commercal Asset Backed Securtes (CABS) dffer from other more tradtonal nvestment products, n partcular n terms of the trade-off between rsk and return. Therefore n ths paper we compared credt spread on RMBS and CABS to credt spread on corporate bonds wthn the same ratng class and we try fnd some explanaton of the dfferences between them. 1 Data are from European Securtzaton Forum (2002). 2

3 The paper s organsed as follows: secton 1 deals wth securtzaton as a credt rsk management tool; secton 2 overvews economc lterature about credt spread both on corporate bonds and on Asset-Backed Securtes. Secton 3 presents the emprcal analyss developed n ths paper: frst, data set s brefly descrbed; secondly, a descrptve analyss s shown n order to underlne the excess spread of RMBS and CABS compared to corporate bonds; then some possble explanatons for the excess spread are dscussed; later on results of the regresson analyss s presented. Concludng remarks follows n secton Credt rsk management through securtzaton As sad above, securtzaton can be used to manage credt rsk. In fact sellng assets through a true sale the orgnator can transfer credt rsk, whch s, thus, shared by other agents n the securtzaton process, especally nvestors and credt enhancers. In ths way orgnator s balance sheet s more dversfed, snce geographcal, ndustry and ndvdual exposure concentraton s reduced. Moreover, usng synthetc securtzaton, the orgnator can buy protecton aganst credt rsk and, at the same tme, mantan the assets on hs balance sheet. So securtzaton assures from rsk fragmentaton; t s worth to underlyng that t s not convenent for the orgnator to behave n an opportunstc way. In fact, f he wanted to clean-up hs balance sheet by sellng only the worst assets and so damagng nvestors, he wouldn t have other chances to rse funds n the future snce hs reputaton gets bad. So orgnator has an ncentve to behave correctly to keep hs reputaton good. Standard and Poor s (2002) recently publshed a study on Italan bank-orgnators, n whch they assert that lttle rsk s transferred outsde the orgnatng group. In fact t s common for the orgnator to retan the subordnated tranche to credt enhance the ssue. Ths practce causes the entre rsk to be concentrated n the subscrbed junor notes, that are classfed among the orgnator s securtes. Ths mples that credt rsk s not transferred out through securtzaton; so, f one would use securtzaton as a credt rsk management tool, he must pay attenton to project a correct trade-off between credt rsk transferrng and credt enhancement provdng 2. 2 Credt enhancement s useful because t enables orgnator to ssue hgher rated bonds and thus to fund at a lower cost (n terms of yeld offered to nvestors). 3

4 2. Credt spread The yeld of a bond s nfluenced by several factors, among whch a very mportant one s credt ratng. Ratng reflects the credt rsk of the bond, so credt spread defned as the dfference between the yeld on a (rsky) bond and the yeld on a rsk-free bond (such as, for example, a T-bond) s n large part determned by credt rsk, but other factors may be ncluded as well. Lterature about credt spread on corporate bonds s plentful; He, Hu and Lang (2000) studed the shape of credt spread curves and fnd that hgh-rated bonds have postve sloped curves, low-rated bonds show negatve sloped curves, whle spread curves for mddle-rated bonds are hump-shaped wth peak ponts. Jakson and Perraudn (1999) found evdence for securtes ssued by U.S. banks to offer hgher spreads compared to bonds ssued by other ndustral and utltes U.S. companes. Elton, Agrawal and Mann (2001) found that credt spread s due to default and taxaton premum and also to a premum that covers a systematc rsk. Ths knd of rsk s due to the fact that spreads on bonds are n some part nfluenced also by the same factors that affect stock prces. Colln-Dufresne, Goldsten and Martn (2001) used a lnear regresson to fnd out whch factors determne changes of credt spread. Ther most nterestng result s that factor thought to be mportant varable to explan credt spread changes have lttle explanatory power, whle there s a common factor whch s able to explan the remanng spread; however they could not determne such factor. Annaert and De Ceuster (1999) analysed credt spread on European corporate bonds and found evdence for the presence of a lqudty premum n the yeld of these securtes. Huang and Huang (2002) estmated how much of credt spread s due to credt rsk and found that lttle part of t s explaned by credt rsk for hgh-qualty bonds, whle credt rsk s more mportant n explanng credt spread on junk bonds. There are two partcularly nterestng artcles about credt spread determnants of bonds ssued by a securtzaton transacton: Mars and Segal (2002) and Rothberg, Nothfat Gabrel (1989). The former used a lnear regresson to fnd the determnants of credt spread on CMBS 3 ; results dentfy several varables that are related to spreads. In partcular nterest rate volatlty and a recesson ndex capture the default probablty on underlyng mortgages and so nfluence spread on CMBSs; tranche sze has a negatve slope, provdng some support for the noton that larger tranches are assocated wth lower lqudty prema; at the opposte, total 3 Commercal Mortgage Backed Securtes. 4

5 deal sze are postve and sgnfcant, ndcatng that larger deals are assocated wth hgher spread. The authors explan the last result wth the fact that a larger transacton requre larger spreads to attract a suffcent number of nvestors to place the ssue. Moreover an mportant factor explanng spread on CMBS s a tme dependent varable whch has a negatve and sgnfcant coeffcent that ndcates that CMBS credt spread decrease from year to year. Ths provdes evdence to support the noton that a learnng process s takng place and nvolves ssuers, ratng agences and nvestors. In fact lack of confdence wth analyss and evaluaton of ths knd of securtes caused nvestors to overtmate the rsk and then explans the hgher yeld spreads of CMBSs n the early 1990s. Rothberg, Nothaft and Gabrel (1989) assert that mortgage pass-through and Treasury securtes may reflect dfferences n taxaton and compensaton for default, call and marketablty rsk on mortgages. Usng a lnear regresson they found that nterest rate volatlty and the term structure of rates have grown n mportance n recent years as exercse of the prepayment opton has ncreased. They also found evdence that lqudty and credt concerns affect the prcng of pass-through securtes. 3. Emprcal Analyss 3.1. Data descrpton Data used n ths work for Asset-Backed securtes are publshed by BNP Parbas, whle data for corporate floatng bonds are provded by Bloomberg. Both types of securtes are ssued n Europe and are ndexed to 3 or 6 months Eurbor and to 3 months Lbor; ratng categores are AA, A, BBB, BB, followng S&P s notaton. Many ABSs are trple A rated, but t s dffcult to fnd European corporate-floatng bonds rated AAA, so n the regresson analyss ABSs rated AAA are excluded. The tme perod covered s for corporate bonds, whle for ABS data prevous to 2001 (second quarter) were not avalable. The full ABS sample ncludes 448 securtes (101 are AAA - rated), whle corporate bonds ncluded n the analyss are For each bond we needed spread, sze of the ssue, maturty, ssue date, ratng, dex-rate, type of ABS. Followng Mars and Segal (2002), data for each securty are ssue date (n partcular 4 In the followng regresson (secton 3.4, 3.5 and 3.6) some bonds are elmnated and some more are ncluded, so t wll be ponted out each tme n the approprate secton the proper sample sze. 5

6 spread s the launch spread). The category ABS ncludes ABS n a strct sense (leases, credt cards, auto loans, etc.) 5, CMBS, RMBS, CDO, CLO and CBO Descrptve analyss The frst am of ths paper s to test whether the theoretcal relaton between spread and ratng holds for our data set. In fact theory predcts that lower rated bonds should offer hgher spreads snce they are rsker than hgher rated ones, whch should, thus, have lower yelds. Evdence supports ths argument n lterature 6 and also n our sample the relaton holds: let us look at fgure 1; the graphs depct ratng on the horzontal axs and spread (n bass ponts) n the vertcal axs. Fgure 1: Relaton between average spreads and credt ratng and comparson between average spreads for corporate bonds and ABSs. COMPARISON BETWEEN AVERAGE SPREADS AVERAGE SPREADS (b.p.) ABS_3ME CORP_3ME AAA AA A BBB BB RATING COMPARISON BETWEEN AVERAGE SPREADS 200 AVERAGE SPREAD (b.p.) ABS_3ML CORP_3ML AAA AA A BBB RATING COMPARISON BETWEEN AVERAGE SPREADS AVERAGE SPREADS (b.p.) ABS_6ME CORP_6ME AAA AA A BBB RATING 5 When a dstncton s needed, ABS n a strct sense are labelled classcal ABS. 6 For example see Sarg and Warga (1989); Mars and Segal (2002). 6

7 The fgure shows that the average spread for each ratng category wthn the same ndex group grows as credt qualty worsens; ths holds for corporate bonds (blue bars) as well as for Asset-Backed Securtes (yellow bars). The same relaton holds also for the aggregated sample as well that s wthout dstngushng between ndex-rate groups. Fgure 1 s useful also to compare spreads on ABSs and on Corporate bonds holdng for the ratng class and ndex-rate. As one can easly notce, ABSs spreads (represented by the yellow bars) are always much hgher than spreads on corporate bonds (depcted as the blue bars), holdng for credt ratng. We can say that there s an excess spread on ABSs compared to corporate bonds, whch must be due to factors other than those that affect corporate bonds yeld. Fgure 2 depcts the excess spread.e. the dfference between the spread on ABSs and the spread on corporate bonds. In the horzontal axs credt ratng s reported, whle the vertcal axs shows credt spread n bass ponts. As we can see, the curve has a postve slope ndcatng that the dfference n spreads grows at a hgher pace when ratng gets worse. So low-rated ABSs has a hgher excess spread than hgher-rated ones. An explanaton may be the followng: snce a goal of securtzaton s to reach a hgher ratng than that of the orgnator (so that the orgnator can fund at a lower cost), senor tranches have the best underlyng assets, whle subordnated tranches are less attractve and so must offer a hgher spread to be subscrbed by nvestors. Fgure 2: ABSs excess spread. EXCESS SPREAD (ABS-CORP) AA A BBB BB We can now try to dscuss some possble explanatons for the excess spread on ABSs. 7

8 3.3. Explanatons for the excess spread Ratng agences evaluate the credt rsk of an ssue and so classfy bonds nto dfferent rsk classes. Assumng that ratng s relable, two dfferent securtes wthn the same ratng category should be exposed to credt rsk n the same way. When one assgns ratng to a corporate bond, he consders the varables that capture the capacty of the ssuer to fulfl hs commtments. However to evaluate an ABS ssue t s necessary to deeply examne the structure of the transacton, because the credt qualty of the bond does not (entrely) depends on the credt qualty of the ssuer but on the qualty of the underlyng assets. In partcular economc and fnancal performances of the assets are analysed (geographcal and ndustry dversfcaton, cash-flows, tranche allocaton, etc.); dfferent stress scenaros are studed n order to calculate default probablty and recovery rates; fnally the legal structure and credt enhancement of the transacton are analysed. Even f we assume that ratng exactly reflects the credt rsk of a bond, to conclude that dfferent types of securtes wth the same ratng have the same prce, we should assert that credt rsk s the unque rsk to whch bonds are exposed. For ths s not true, we must say that dfferent types of securtes wth the same ratng are not necessarly prced the same. So we can say that there s a part of spread that s the same for the two categores of bonds holdng for credt ratng (and ths part of the spread s due to credt rsk), whle the remanng spread the excess spread shown n fgure 2 s not caused by credt rsk and must be explaned ntroducng other factors. These factors can be summarsed by () the dffculty of evaluatng a securtzaton transacton; the reasons for ths dffculty s the complexty of the transacton, () the presence of some nformatonal problems and () the lack of experence of the market. In fact, securtzaton s dffcult to evaluate snce t s a complcated transacton that nvolves many partes and a large number of contracts (and thus several small sub-transactons ). Ths mples that experence and knowledge s needed n order to understand securtzaton and to asses the rght prce to the securtes ssued. Moreover we can say that there s a nformatonal problem: whle nformaton about ssuer of corporate bonds s easly accessble and qute prompt to be nterpreted, nformaton about the credt qualty of all the assets underlyng a securtzaton transacton s not easy to collect and not smple to evaluate. In fact ABSs are collateralsed by several assets (often thousands), so the subscrber should look for nformaton about all these assets and then study how the cash flow are allocated throughout the subordnated tranches; on the opposte a bondholder only 8

9 has to gan nformaton about the ssuer of the securtes, whch s a smpler and less costly process. The dffculty to evaluate ABSs ssues s also due to the relatve lack of experence of the market: t takes tme for experts to get confdent wth ths knd of fnancal product and then to evaluate t n a somewhat automatc way. In fact, although the legal framework provdes good guarantes to nvestors n order to avod the presence of structural rsks, there s not a adequate case hstores to be sure that law provsons wll be fulflled. In other words, f one subscrbes a corporate bond and the frm suffers default, law provsons and the jurdcal practce assures that bondholders are repad before stockholders (and thus there s no uncertanty about what nvestors have to expect f such an event happens). But what does t happen f a large proporton of the borrowers underlyng a securtzaton transacton go bankrupt? Legal and structural guarantes are clear n a theoretcal level but n practce cases lke ths dd not happen (at least not n a suffcent number) and so nobody can say whether the legal provsons wll be enough to assure nvestors from the happenng of such an event. So we can dentfy somethng lke a learnng by dong process, whch needs tme to be completed and whch should make spreads get lower as tme passes. A smlar argument can be found n Mars and Segal (2002) about Amercan CMBS: they argue that nvestors (and ratng agences) were unfamlar wth analysng commercal mortgages and rsks they posed. Lack of famlarty mght have caused nvestors to overestmate the rsks, and could explan why yeld spreads were hgh n the early 1990s.As nvestors ganed confdence wth the product they developed a greater ablty to asses the rsks and so requred lower rsk prema Emprcal analyss of factors that nfluence the excess spread We can now try to determne factors that affect the excess spread usng the data set descrbed n secton 3.1. Securtes employed n the regresson analyss are 444, among whch 347 are ABSs 7 and 97 are corporate bonds. Lnear regresson s estmated wth the Ordnary Least Squares method usng the spread as dependent varable, whle usng a number of j regressors as ndependent varables, as descrbed below. In partcular, followng Mars and Segal (2002), we ntroduce varables that consder the sze of the ssue, the ratng, the ssue date are classcal ABSs; 141 are CDOs; 127 are RMBSs. 9

10 The frst varable s CORP_ABS, whch dscrmnates corporate bonds from ABSs; t s equal to 0 f the securty s ssued by a frm and t has value 1 f the note s ssued by a securtzaton transacton. We expect ths varable to have a postve coeffcent, ndcatng that Asset-Backed Securtes have hgher spreads compared to corporate bonds wthn the same ratng class and holdng for the other factors. T1, T2 and T3 are dummy varables as well and are defned as follows: 1, f the bond s ndexed to 3 months T 1 = 0, otherwse 1, f the bond s ndexed to 3 months T 2 = 0, otherwse 1, f the bond s ndexed to 6 months T 3 = 0, otherwse Eurbor Lbor Eurbor These dummes eventually capture the dfferences n spread due to the dfferent ndex rate of the bond. Ratng s also represented by dummy varables, R1, R2, R3, R4: 1, f the bond s rated AA R 1= 0, otherwse R 2 R 3 R 4 1, f the bond s rated A = 0, otherwse 1, f the bond s rated BBB = 0, otherwse 1, f the bond s rated BB = 0, otherwse In order to avod the sngularty of the estmaton matrx, we must exclude from the regresson one varable of each dummy-group; for the ratng group we chose to elmnate R4. R4 represents the lowest-ratng securtes n the sample ( BB ) and the coeffcents of the remanng dummes must be nterpreted as the average dfference between spread on, respectvely, the bonds rated AA, A and BBB and the spread on the dummy excluded; hence we expect the coeffcents of R1, R2 and R3 to be negatve and decreasng n ther absolute value. We argue that the excess spread of Asset-Backed Securtes depends on the ratng of each bond, so we nclude n the regresson other four varables n order to capture the dfferences n 10

11 excess spread due to the ratng class of the securty: CORPABS_R1, CORPABS_R2, CORPABS_R3, CORPABS_R4. They are constructed, respectvely, as the varable CORP_ABS tmes R1, R2, R3, R4; excludng CORPABS_R4 from the regresson, we expect CORPABS_R1-2-3, to be statstcally sgnfcant and to have negatve coeffcent, decreasng n ther absolute value, as for R1, R2, R3. Another explanatory varable s SIZE,.e. the sze of the ssue expressed as thousands of Euro. The greater the sze, the greater the lqudty n the market, so the lower the spread should be. So we expect the coeffcent of sze to be negatve, as found n Mars and Segal (2002). Another explanatory varable s MATURITY whch s defed as follows: for corporate bonds t represents the fracton of year whch elapses from the ssue date to the maturty date; for ABS t represents the Weghted Average Lfe (WAL). WAL s the average number each dollar of unpad prncpal due on the mortgage remans outstandng; t s computed as the weghted average tme to the recept of all future cash flows weghts the dollar amounts of the prncpal pay-downs. WAL s generally used by market operators whle dealng wth ABSs and other structured-fnance products, hence also n ths work WAL s used. The longer the term to maturty, the greater the uncertanty about the future, so the greater the rskness s. Thus we expect a postve estmated coeffcent for maturty. NWMKT s a group of dummes whch dscrmnate securtes consderng the ssung quarter of the year. NWMKT1 s equal to 1 f the securty was ssued n Aprl, May or June 2001; 0 otherwse. NWMKT2 s equal to 1 f the ssue date of the bond s July, August or September 2001; 0 otherwse. NWMKT3 equals 1 f the bond was ssued n the forth quarter of 2001; 0 otherwse. NWMKT4 and NWMKT5 are equal to 1 f the bond was ssued, respectvely, n the frst or second quarter of 2002; they are zero otherwse. In ths way we can capture also non-lnear relatonshps between tme and spread. The varable s zero for all corporate bonds. NWMKT1 s not ncluded n the regresson n order to avod the sngularty of the estmaton matrx, hence the coeffcents of NWMKT must be nterpreted as average spreads of bonds ssued, respectvely, n the thrd, forth quarter of 2001 and frst or second quarter of 2002 n excess compared to the excluded category s spread. The later a securtes was ssued, the lower the spread on t should be, because nvestors should have acqured a hgher confdence wth ABSs evaluaton and so they should ask for lower spreads. Therefore we expect a negatve coeffcent for Nwmkt1-5 (whch means that, for example, 11

12 a bond ssued n the second quarter of 2002 should have lower spreads compared to securtes ssued on the second quarter of 2001 that s the excluded category). The estmated equaton s: (1) SPREAD = β + β CORP_ ABS + β T1 + β T2 + β R1 + β R2 + β R3 0 + β CORPABS_ R1 + β CORPABS_ R2 7 + β + β SIZE + β NWMKT4 11 SCADENZA + β + β NWMKT5 + ε β CORPABS_ R3 NWMKT2 + β NWMKT3 6 where ndcates the -th securty. T3, R4, CORPABS_R4 and NWMKT1 are excluded from equaton 1 to avod the estmaton matrx to become sngular Results Results are shown n table 1. The frst column reports the explanatory varables. The second column shows the estmated coeffcent for each varable. Standard error s reported n column three and column four exhbts the t-statstcs (calculated as the rato of the estmated coeffcent and the related standard error). The last column dsplays the p-value; the last two rows report R 2 and ts adjusted verson. We can frst note that the regresson explan almost 68% of the excess spread whle remanng 32% s not captured by the varables. From table 1 we can frst notce that R1, R2 and R3 are hghly sgnfcant and ther coeffcents has the expected features: they are negatve and decreasng n absolute value when ratng worsens. So the results ndcate that bonds rated AA have spreads 254 b.p. hgher on average than bonds rated BB ; smlar arguments hold for R2 and R3. The coeffcent of CORPABS_R1 represents the average excess spread between AA-rated Asset-Backed Securtes and AA-rated corporate bonds compared to the category excluded (bonds rated BB ), holdng for all the other condtons; smlarly, the coeffcents of CORPABS_R2 and CORPABS_R3 represent the average spread, respectvely, between A- rated ABSs and A-rated corporate bonds and between BBB-rated Asset-Backed Securtes and BBB-rated corporate bonds, holdng for the other condtons. All these coeffcents are statstcally sgnfcant and have the expected sgns. Ths does not necessarly mply that for all ratng categores ABSs have hgher spreads compared to corporate bonds; n order to 12

13 verfy whether the excess spread holds for all ratng groups, we employed three coeffcent tests. The null hypothess are: TEST 1 H 0 : β 1 + β 7 = 0; TEST 2 H 0 : β 1 + β 8 = 0; TEST 3 H 0 : β 1 + β 9 = 0. If we accept the null hypothess for the frst test, we can conclude that AA-rated ABSs have not hgher average spreads than AA-rated corporate bonds. On the opposte rejectng H 0 for test 1 mples to confrm the excess spread between ABSs and corporate bonds for AA-ratng category. Smlar arguments hold for test 2 and 3, that s for A and BBB groups, respectvely. Therefore f we accept H 0 for all the tests we can conclude that the overall average excess spread s entrely determned by the average excess spread on BB-rated securtes. We reject H 0, at a 5% sgnfcance level, f the p-value s lower than The results of the tests show that p-value s for the frst test; for the second test; for the thrd test. Therefore we can conclude that the excess spread does exsts only for securtes rated BBB (and for BB-rated bonds), but evdence suggest that for the groups AA and A Asset-Backed Securtes have not hgher spreads than corporate bonds. The sze and the maturty of the bonds do not seem to nfluence the spread, snce the coeffcents of the two varable are not sgnfcant. Moreover T1 s not statstcally sgnfcant, whle T2 s weakly sgnfcant; ts coeffcent s negatve, ndcatng that 3-months-Lbor bonds have lower spreads compared to 6-month-Eurbbor (represented by T3,.e. the excluded dummy). The results do not provde evdence for a tme dependence of the spreads: only the coeffcents for NWMKT3 and NWMKT5 are sgnfcant 8 and the value of the coeffcents of entre group does not show a clear postve or negatve trend. Ths result contrast wth the one obtaned by Mars and Segal (2002) wth US data. A potental explanaton s that the European market s new and so only after fve or ten years we could expect to observe ths effect. It mght be nterestng to evdence how mportant each regressor s n determnng the excess spread. To do so we can estmate agan equaton (1), excludng one varable and repeat ths procedure as many tmes as the number of the varables. In such a way we can see how much the R 2 reduces when a varable s not ncluded n the regresson; the lower the R 2, the more 13

14 mportant the excluded varable s. Fgure 3 shows the dfference between R 2 n the total regresson and R 2 n the j-1 regresson. It s easy to notce that ratng, corp_abs and corp_abs*r1-2-3 are the most mportant varables. Table 1: Estmated coeffcents for equaton (1). The regresson has SPREAD (n bass ponts) as dependent varable, whle the followng varables are ntroduced as explanatory varables. CORP_ABS dscrmnates corporate bonds from ABSs; t s equal to 0 f the securty s ssued by a frm and t has value 1 f the note s ssued by a securtzaton transacton. T1 s equal to 1 f the bond s dexed to 3-months Eurbor; 0 otherwse. T2 s equal to 1 f the securty s dexed to 3-months Lbor; 0 otherwse. R1 dentfes AA-rated bonds; R2 represents A-rated securtes and R3 ndecates BBB-rated bonds. CORPABS_R1 s equal to CORP_ABS*R1; CORPABS_R2 s equal to CORP_ABS*R2; CORPABS_R3 s equal to CORP_ABS*R3. NWMKT s a group of dummes whch dscrmnate securtes consderng the ssung quarter of the year. NWMKT2 dentfes bonds ssued n the thrd quarter of 2001; NWMKT3 represents bonds ssued n the forth quarter of 2001; NWMKT4 dentfes securtes ssued n the frst quarter of 2002; NWMKT5 ndcates bonds ssued n the second quarter of SIZE s the sze of the ssue expressed as thousands of Euro. MATURITY s defed as follows: for corporate bonds t represents the fracton of year whch elapses from the ssue date to the maturty date; for ABS t represents the Weghted Average Lfe (WAL). Varable Coeffcent Std. Error Prob. C * CORP_ABS * T T * R * R * R * CORPABS_R * CORPABS_R * CORPABS_R * SIZE -2.03E E SCADENZA NWMKT NWMKT * NWMKT NWMKT * R-squared Adjusted R- squared NWMKT5 s weakly sgnfcant. 14

15 Fgure 3: Margnal contrbute of each explanatory varable. Nwmkt Maturty Sze Corp_abs_ R1-2-3 Ratng Index-rate corp_abs 0 0,02 0,04 0,06 0,08 0, The subsamples: classcal ABSs, CDOs and RMBSs. It mght be worth to analyse what happens f we regress only selected types of Asset-Backed Securtes and corporate bonds n order to understand whether the type of ABSs nfluence credt spread; ths s exactly what we do n ths secton. Before presentng the regresson and the results, t s worth to say that not all the types of Asset-Backed Securtes are charactersed by the same level of newness. In fact RMBS are the frst type of securtes ssued by a securtzaton transacton n the Unted States n the md 1970s, so nvestors should have acqured more experence and confdence wth ths knd of bonds and less wth more recently created products of the structured fnance. A smlar argument holds for classcal ABSs, such as the notes ssued by securtzaton of leasng, auto loans, recevables, etc. So we now try to regress separately classcal ABSs, CDOs and RMBSs wth corporate bonds; fnally we compare classcal ABSs and RMBSs Classcal ABSs The frst sub-sample analysed s classcal ABSs. The observatons nclude 176 bonds, among whch 97 are corporate and the remanng 79 are Asset-Backed Securtes. The estmated equaton s equaton (1), as the regresson mplemented n secton 3.4. Thus consderatons about the explanatory varables are the same as n secton 3.4. Moreover smlar consderaton as n secton 3.5 can be made for almost all the varables. In fact the 15

16 greatest dfferences between table 1 and table 2 deal wth the varables NWMKT : none of these are statstcally sgnfcant. Three tests smlar to the ones exposed n secton 3.5 are mplemented for the sub-sample classcal ABSs. The results are: p-value equal to for test one; p-value equal to for test 2; p-value equal to 0 for the thrd test. Therefore we can reject the null hypothess for two of the ratng categores A and BBB and conclude that evdence provdes support for the exstence of a postve excess spread between ABSs and corporate bonds for these two groups. On the other hand the frst test makes us argue that AA group has not an excess spread between the two types of bonds. Table 2: Estmated coeffcents for equaton (1) for the subsample classcal ABSs. The regresson has SPREAD (n bass ponts) as dependent varable, whle the followng varables are ntroduced as explanatory varables; only classcal ABSs and corporate bonds are ncluded n the analyss. CORP_ABS dscrmnates corporate bonds from ABSs; t s equal to 0 f the securty s ssued by a frm and t has value 1 f the note s ssued by a securtzaton transacton. T1 s equal to 1 f the bond s dexed to 3-months Eurbor; 0 otherwse. T2 s equal to 1 f the securty s dexed to 3-months Lbor; 0 otherwse. R1 dentfes AArated bonds; R2 represents A-rated securtes and R3 ndcates BBB-rated bonds. CORPABS_R1 s equal to CORP_ABS*R1; CORPABS_R2 s equal to CORP_ABS*R2; CORPABS_R3 s equal to CORP_ABS*R3. NWMKT s a group of dummes whch dscrmnate securtes consderng the ssung quarter of the year. NWMKT2 dentfes bonds ssued n the thrd quarter of 2001; NWMKT3 represents bonds ssued n the forth quarter of 2001; NWMKT4 dentfes securtes ssued n the frst quarter of 2002; NWMKT5 ndcates bonds ssued n the second quarter of SIZE s the sze of the ssue expressed as thousands of Euro. MATURITY s defed as follows: for corporate bonds t represents the fracton of year whch elapses from the ssue date to the maturty date; for ABS t represents the Weghted Average Lfe (WAL). Varable Coeffcent Std. Error Prob. C * CORP_ABS * T T * R * R * R * CORPABS_R * CORPABS_R * CORPABS_R * SIZE -2.03E E MATURITY NWMKT NWMKT NWMKT NWMKT R-squared Adjusted R-squared

17 3.6.2 RMBSs In ths secton 127 RMBSs and 97 corporate bonds are analysed; the regresson equaton s the same used n secton 3.4 and The results, reported n table 3, are smlar as well; n partcular none of the varables NWMKT s statstcally sgnfcant. Sze, Maturty, T1 and T2 are not sgnfcant whle CORP_ABS, R1-2-3 and CORPRMBS_R1-2-3 are hghly sgnfcant. Interpretaton of the coeffcents of these varables s smlar to that exposed n secton 3.5. Table 3: Estmated coeffcents for equaton (1) for the subsample RMBSs. The regresson has SPREAD (n bass ponts) as dependent varable, whle the followng varables are ntroduced as explanatory varables; only RMBSs and corporate bonds are ncluded n the analyss. CORP_ABS dscrmnates corporate bonds from ABSs; t s equal to 0 f the securty s ssued by a frm and t has value 1 f the note s ssued by a securtzaton transacton. T1 s equal to 1 f the bond s dexed to 3-months Eurbor; 0 otherwse. T2 s equal to 1 f the securty s dexed to 3-months Lbor; 0 otherwse. R1 dentfes AArated bonds; R2 represents A-rated securtes and R3 ndcates BBB-rated bonds. CORPABS_R1 s equal to CORP_ABS*R1; CORPABS_R2 s equal to CORP_ABS*R2; CORPABS_R3 s equal to CORP_ABS*R3. NWMKT s a group of dummes whch dscrmnate securtes consderng the ssung quarter of the year. NWMKT2 dentfes bonds ssued n the thrd quarter of 2001; NWMKT3 represents bonds ssued n the forth quarter of 2001; NWMKT4 dentfes securtes ssued n the frst quarter of 2002; NWMKT5 ndcates bonds ssued n the second quarter of SIZE s the sze of the ssue expressed as thousands of Euro. MATURITY s defed as follows: for corporate bonds t represents the fracton of year whch elapses from the ssue date to the maturty date; for ABS t represents the Weghted Average Lfe (WAL). Varable Coeffcent Std. Error Prob. C CORP_RMBS T T R R R CORPRMBS_R CORPRMBS_R CORPRMBS_R SIZE 6.22E E SCADENZA NWMKT NWMKT NWMKT NWMKT R-squared Adjusted R-squared

18 The coeffcent tests were carred out n the way already explaned n the prevous sectons. P- values for the frst and second tests are, respectvely, and , whch make us accept the null hypothess. The thrd test s charactersed by a p-value equal to , mplyng that the null hypothess s rejected. So we can agan conclude that the exstence of the excess spread s verfed only for lower-rated bonds CDOs The last sub-sample that has been analysed n ths paper s CDOs. The sample ncludes 141 CDOs and 97 corporate bonds. The estmaton output s summarsed n table 4. As we can easly notce, the results are the same as the prevous analyss except for the regressors NWMKT. In fact three of the four varables n the group are sgnfcant, provdng evdence for a relaton between spread and tme. However, as underlned n secton 3.5, the coeffcents of these varables does not show a clear trend ether postve nor negatve. The coeffcent tests agan support the dea that the dfference n spread between securtes ssued n a securtzaton transacton and corporate bonds does exsts only for low-rated securtes 9. Table 4: Estmated coeffcents for equaton (1) for the subsample CDOs. The regresson has SPREAD (n bass ponts) as dependent varable, whle the followng varables are ntroduced as explanatory varables; only CDOs and corporate bonds are ncluded n the analyss. CORP_ABS dscrmnates corporate bonds from ABSs; t s equal to 0 f the securty s ssued by a frm and t has value 1 f the note s ssued by a securtzaton transacton. T1 s equal to 1 f the bond s dexed to 3-months Eurbor; 0 otherwse. T2 s equal to 1 f the securty s dexed to 3-months Lbor; 0 otherwse. R1 dentfes AA-rated bonds; R2 represents A-rated securtes and R3 ndcates BBB-rated bonds. CORPABS_R1 s equal to CORP_ABS*R1; CORPABS_R2 s equal to CORP_ABS*R2; CORPABS_R3 s equal to CORP_ABS*R3. NWMKT s a group of dummes whch dscrmnate securtes consderng the ssung quarter of the year. NWMKT2 dentfes bonds ssued n the thrd quarter of 2001; NWMKT3 represents bonds ssued n the forth quarter of 2001; NWMKT4 dentfes securtes ssued n the frst quarter of 2002; NWMKT5 ndcates bonds ssued n the second quarter of SIZE s the sze of the ssue expressed as thousands of Euro. MATURITY s defed as follows: for corporate bonds t represents the fracton of year whch elapses from the ssue date to the maturty date; for ABS t represents the Weghted A verage Lfe (WAL). Varable Coeffcent Std. Error Prob. C CORP_CDO T T R In fact the frst and second tests have a p-value equal to , whle the p-value of the thrd one s nearly zero (0.0009). 18

19 R R CORPCDO_R CORPCDO_R CORPCDO_R SIZE -1.66E E SCADENZA NWMKT NWMKT NWMKT NWMKT R-squared Adjusted R-squared RMBS and CABS: a comparson In the prevous sectons corporate bonds were compared to securtes ssued by securtzaton transactons; we would lke now to nvestgate whether and how an excess spread exsts between RMBSs and classcal ABSs and/or between RMBSs and CDOs. In fact, as sad above, RMBSs were the frst type of Asset-Backed Securtes to be ssued, so we mght ague that RMBSs have lower spreads than classcal ABSs and n partcular lower spreads than CDOs ths would confrm that the newness of the securty does matter for the spread requred by the market. Hence, the frst analyss comprses 127 RMBSs and 79 classcal ABSs. The explanatory varables are the same as n equaton (1), however RMBS_ABS s defned n a somewhat dfferent way: t s equal to zero f the bond s a RMBS, whle t s equal to 1 f the securty s a classcal ABS. In such a way the varable dscrmnates RMBSs from classcal ABSs and ts coeffcent gves a measure of the average excess spread f t exsts between the two types of securtes. Estmaton output s reported by table 5. Results suggest that there s an excess spread between RMBSs and classcal ABSs, snce the coeffcent of RMBS_ABS s postve and statstcally sgnfcant. Moreover RMBSABS_R1-2-3 are sgnfcant as well, therefore, as n the prevous sectons, three coeffcent tests are run. The results make us accept the null hypothess n all the case, meanng that for the ratng groups AA, A, and BBB the excess spread s not statstcally sgnfcant. We can, thus, conclude that the excess spread only exsts for lower rated bonds labelled BB whch are the excluded category and are ncorporated n the coeffcent of RMBS_ABS. NWMKT are not statstcally 19

20 sgnfcant, mplyng that spread does not seem to be affected by the ssue date of the bond. Surprsngly SIZE s weakly sgnfcant and has a negatve estmated coeffcent; ths means that the market requres slghtly lower spreads for bonds ssued n a large sze. Table 5: Estmated coeffcents for equaton (1) for RMBSs and classcal ABSs. The regresson has SPREAD (n bass ponts) as dependent varable, whle the followng varables are ntroduced as explanatory varables; only RMBSs and classcal ABSs are ncluded n the analyss. CORP_ABS dscrmnates RMBSs from classcal ABSs; t s equal to 0 f the securty s ssued by a frm and t has value 1 f the note s ssued by a securtzaton transacton. T1 s equal to 1 f the bond s dexed to 3- months Eurbor; 0 otherwse. T2 s equal to 1 f the securty s dexed to 3-months Lbor; 0 otherwse. R1 dentfes AA-rated bonds; R2 represents A-rated securtes and R3 ndcates BBB-rated bonds. CORPABS_R1 s equal to CORP_ABS*R1; CORPABS_R2 s equal to CORP_ABS*R2; CORPABS_R3 s equal to CORP_ABS*R3. NWMKT s a group of dummes whch dscrmnate securtes consderng the ssung quarter of the year. NWMKT2 dentfes bonds ssued n the thrd quarter of 2001; NWMKT3 represents bonds ssued n the forth quarter of 2001; NWMKT4 dentfes securtes ssued n the frst quarter of 2002; NWMKT5 ndcates bonds ssued n the second quarter of SIZE s the sze of the ssue expressed as thousands of Euro. MATURITY s defed as follows: for corporate bonds t represents the fracton of year whch elapses from the ssue date to the maturty date; for ABS t represents the Weghted Average Lfe (WAL). Varable Coeffcent Std. Error Prob. C * RMBS_ABS * T * T R * R * R * RMBSABS_R * RMBSABS_R * RMBSABS_R * SIZE -2.59E -05* 1.07E MATURITY NWMKT NWMKT NWMKT * NWMKT R-squared Adjusted R-squared In the second analyss 127 RMBSs are compared to 141 CDOs. The estmated equaton s the same as n the prevous sectons equaton (1), however the varable RMBS_CDO s defned n a way smlar to RMBS_ABS : t s equal to zero f the bond s a RMBS, whle t s equal to 1 f the securty s a CDO. Table 6 shows that the estmated coeffcent for RMBS_CDO s postve and statstcally sgnfcant, ndcatng that CDOs have hgher 20

21 spreads than RMBSs. Moreover, snce RMBSCDO_R1-2-3 are not sgnfcant, meanng that the dstncton between the ratng class s not mportant. Only NWMKT3 and NWMKT5 are sgnfcant n the group and the coeffcents do not suggest a lnear trend or relaton between tme and spread. Table 6: Estmated coeffcents for equaton (1) for RMBSs and CDOs. The regresson has SPREAD (n bass ponts) as dependent varable, whle the followng varables are ntroduced as explanatory varables; only RMBSs and CDOs are ncluded n the analyss. CORP_ABS dscrmnates RMBSs from classcal ABSs; t s equal to 0 f the securty s ssued by a frm and t has value 1 f the note s ssued by a securtzaton transacton. T1 s equal to 1 f the bond s dexed to 3-months Eurbor; 0 otherwse. T2 s equal to 1 f the securty s dexed to 3-months Lbor; 0 otherwse. R1 dentfes AA-rated bonds; R2 represents A-rated securtes and R3 ndcates BBB-rated bonds. CORPABS_R1 s equal to CORP_ABS*R1; CORPABS_R2 s equal to CORP_ABS*R2; CORPABS_R3 s equal to CORP_ABS*R3. NWMKT s a group of dummes whch dscrmnate securtes consderng the ssung quarter of the year. NWMKT2 dentfes bonds ssued n the thrd quarter of 2001; NWMKT3 represents bonds ssued n the forth quarter of 2001; NWMKT4 dentfes securtes ssued n the frst quarter of 2002; NWMKT5 ndcates bonds ssued n the second quarter of SIZE s the sze of the ssue expressed as thousands of Euro. MATURITY s defed as follows: for corporate bonds t represents the fracton of year whch elapses from the ssue date to the maturty date; for ABS t represents the Weghted Average Lfe (WAL). Varable Coeffcent Std. Error t-statstc Prob. C RMBS_CDO T T R R R RMBSCDO_R RMBSCDO_R RMBSCDO_R SIZE -7.81E E MATURITY NWMKT NWMKT NWMKT NWMKT R-squared Adjusted R-squared Concluson 21

22 In ths paper credt spread on corporate bonds and on Asset-Backed Securtes are compared. A descrptve analyss shows that ABSs have hgher spread than corporate bonds; we called excess spread the dfference between spread on ABSs and spread on corporate bonds. Further analyss was mplemented, usng lnear regresson method. However the presence of excess spread s confrmed only for low rated securtes, whle for the categores AA and A Asset-Backed Securtes do not seem to have statstcally sgnfcant hgher spreads than bonds ssued by frms. Ths means that the market requres the same remuneraton to nvest n a bond of a sngle frm and n a bond on a portfolo wth the same ratng, f ths ratng s hgh. On the contrary, the market requres a dfferent remuneraton f the ratng s low. The reasons for ths are many and n partcular: () the dffculty of evaluatng a securtzaton transacton; () the presence of nformatonal problems and () the lack of experence of the market. In partcular, the asymmetres of nformaton generated by a portfolo are dfferent than the one generated by a sngle frm. In the paper we consdered both () RMBS and () CABS and ths phenomenon s confrmed for the retal as for the commercal portfolos. A potental nterpretaton of ths result s that the market do not consder as dfferent assets Retal MBS or Commercal ABS f the ratng s hgh. However, our analyss shows that the market requres a hgher return on Commercal ABS wth respect to RMBS when the ratng s low. The reason of ths result could be the hgher development of RMBS market and the hgher expertse on rsk measurement of retal portfolos. Moreover we argued that part of the excess spread could be due to the fact that ABSs are a new product n the European market, so nvestors could requre a hgher spread because they are not confdent wth ths knd of product. The requred spread should lower as nvestors gan famlarty wth ABSs; a smlar argument s found by Mars and Segal (2002). However the relaton between spread and tme s not supported by emprcal evdence provded n ths paper. Even f we regress spread separately for each ABSs type, we fnd no evdence of a tme dependence for spread. To be more precse, most of the varables that should capture the tmefactor for the sub-sample CDOs are sgnfcant, but a clear relaton between tme and spread cannot be found. So we must conclude that evdence does not support the dea that requred spread on ABSs gets lower as the learnng process s gong to complete, n other words we are not able to assert that nvestors ask for lower spreads on ABSs when they become more famlar wth ths product. 22

23 References ANNAERT J., DE CEUSTER M. (1999) Modellng European Credt Spreads Unverstet Antwerpen UFSIA BNP PARIBAS (2002) European ABS 2001 Forth Quarter Revew, Report BNP PARIBAS (2002) European ABS 2001 Second Quarter Revew, Report BNP PARIBAS (2002) European ABS 2001 Thrd Quarter Revew, Report BNP PARIBAS (2002) European ABS 2002 Frst Quarter Revew, Report BNP PARIBAS (2002) European ABS 2002 Second Quarter Revew, Report COLLIN-DUFRESNE P., GOLDSTEIN R., MARTIN S. (2001) The Determnants of Credt Spread Changes, Journal of Fnance, 56 ELTON E., GRUBER M., AGRAWAL D., MANN C. (2001) Explanng the Rate Spread on Corporate Bonds, Journal of Fnance, 56 HE J., HU W., LANG L. (2000) Credt Spread Curves and Credt Ratngs Workng Paper, Chnese Unversty of Hong Kong HUANG J., HUANG M. (2002) How much of the Corporate-Treasury Yeld Spread s due to Credt Rsk? A New Calbraton Approach, Workng Paper Pennsylvana State Unversty JACKSON P., PERRAUDIN W. (1999) The Nature of Credt Rsk: the Effect of Maturty, Type of Oblgor, and Country of Domcle Fnancal Stablty Revew (November) MARIS B., SEGAL W. (2002) Analyss of Yeld Spreads on Commercal Mortgage-Backed Securtes Journal Real Estate Research, vol. 23 ROTHBERG J., NOTHAFT F., GABRIEL S. (1989) On the Determnants of Yeld Spreads Between Mortgage Pass-Through and Treasury Securtes Journal of Real Estate Fnance and Economcs SARIG O., WARGA A. (1989) Some emprcal Estmates of the Rsk Structure of Interest Rates Journal of Fnance, 44(5) STANDARD AND POOR S (2002) Italan Banks Embrace Securtzaton downloadable from the nternet ste 23

THE VOLATILITY OF EQUITY MUTUAL FUND RETURNS

THE VOLATILITY OF EQUITY MUTUAL FUND RETURNS North Amercan Journal of Fnance and Bankng Research Vol. 4. No. 4. 010. THE VOLATILITY OF EQUITY MUTUAL FUND RETURNS Central Connectcut State Unversty, USA. E-mal: BelloZ@mal.ccsu.edu ABSTRACT I nvestgated

More information

FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS. Richard M. Levich. New York University Stern School of Business. Revised, February 1999

FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS. Richard M. Levich. New York University Stern School of Business. Revised, February 1999 FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS by Rchard M. Levch New York Unversty Stern School of Busness Revsed, February 1999 1 SETTING UP THE PROBLEM The bond s beng sold to Swss nvestors for a prce

More information

Chapter 5 Bonds, Bond Prices and the Determination of Interest Rates

Chapter 5 Bonds, Bond Prices and the Determination of Interest Rates Chapter 5 Bonds, Bond Prces and the Determnaton of Interest Rates Problems and Solutons 1. Consder a U.S. Treasury Bll wth 270 days to maturty. If the annual yeld s 3.8 percent, what s the prce? $100 P

More information

Money, Banking, and Financial Markets (Econ 353) Midterm Examination I June 27, Name Univ. Id #

Money, Banking, and Financial Markets (Econ 353) Midterm Examination I June 27, Name Univ. Id # Money, Bankng, and Fnancal Markets (Econ 353) Mdterm Examnaton I June 27, 2005 Name Unv. Id # Note: Each multple-choce queston s worth 4 ponts. Problems 20, 21, and 22 carry 10, 8, and 10 ponts, respectvely.

More information

Clearing Notice SIX x-clear Ltd

Clearing Notice SIX x-clear Ltd Clearng Notce SIX x-clear Ltd 1.0 Overvew Changes to margn and default fund model arrangements SIX x-clear ( x-clear ) s closely montorng the CCP envronment n Europe as well as the needs of ts Members.

More information

MgtOp 215 Chapter 13 Dr. Ahn

MgtOp 215 Chapter 13 Dr. Ahn MgtOp 5 Chapter 3 Dr Ahn Consder two random varables X and Y wth,,, In order to study the relatonshp between the two random varables, we need a numercal measure that descrbes the relatonshp The covarance

More information

Risk and Return: The Security Markets Line

Risk and Return: The Security Markets Line FIN 614 Rsk and Return 3: Markets Professor Robert B.H. Hauswald Kogod School of Busness, AU 1/25/2011 Rsk and Return: Markets Robert B.H. Hauswald 1 Rsk and Return: The Securty Markets Lne From securtes

More information

Finance 402: Problem Set 1 Solutions

Finance 402: Problem Set 1 Solutions Fnance 402: Problem Set 1 Solutons Note: Where approprate, the fnal answer for each problem s gven n bold talcs for those not nterested n the dscusson of the soluton. 1. The annual coupon rate s 6%. A

More information

Highlights of the Macroprudential Report for June 2018

Highlights of the Macroprudential Report for June 2018 Hghlghts of the Macroprudental Report for June 2018 October 2018 FINANCIAL STABILITY DEPARTMENT Preface Bank of Jamaca frequently conducts assessments of the reslence and strength of the fnancal system.

More information

Impact of CDO Tranches on Economic Capital of Credit Portfolios

Impact of CDO Tranches on Economic Capital of Credit Portfolios Impact of CDO Tranches on Economc Captal of Credt Portfolos Ym T. Lee Market & Investment Bankng UnCredt Group Moor House, 120 London Wall London, EC2Y 5ET KEYWORDS: Credt rsk, Collateralzaton Debt Oblgaton,

More information

CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS

CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS QUESTIONS 9.1. (a) In a log-log model the dependent and all explanatory varables are n the logarthmc form. (b) In the log-ln model the dependent varable

More information

Domestic Savings and International Capital Flows

Domestic Savings and International Capital Flows Domestc Savngs and Internatonal Captal Flows Martn Feldsten and Charles Horoka The Economc Journal, June 1980 Presented by Mchael Mbate and Chrstoph Schnke Introducton The 2 Vews of Internatonal Captal

More information

Elements of Economic Analysis II Lecture VI: Industry Supply

Elements of Economic Analysis II Lecture VI: Industry Supply Elements of Economc Analyss II Lecture VI: Industry Supply Ka Hao Yang 10/12/2017 In the prevous lecture, we analyzed the frm s supply decson usng a set of smple graphcal analyses. In fact, the dscusson

More information

Monetary Tightening Cycles and the Predictability of Economic Activity. by Tobias Adrian and Arturo Estrella * October 2006.

Monetary Tightening Cycles and the Predictability of Economic Activity. by Tobias Adrian and Arturo Estrella * October 2006. Monetary Tghtenng Cycles and the Predctablty of Economc Actvty by Tobas Adran and Arturo Estrella * October 2006 Abstract Ten out of thrteen monetary tghtenng cycles snce 1955 were followed by ncreases

More information

Evaluating Performance

Evaluating Performance 5 Chapter Evaluatng Performance In Ths Chapter Dollar-Weghted Rate of Return Tme-Weghted Rate of Return Income Rate of Return Prncpal Rate of Return Daly Returns MPT Statstcs 5- Measurng Rates of Return

More information

Final Exam. 7. (10 points) Please state whether each of the following statements is true or false. No explanation needed.

Final Exam. 7. (10 points) Please state whether each of the following statements is true or false. No explanation needed. Fnal Exam Fall 4 Econ 8-67 Closed Book. Formula Sheet Provded. Calculators OK. Tme Allowed: hours Please wrte your answers on the page below each queston. (5 ponts) Assume that the rsk-free nterest rate

More information

Tests for Two Correlations

Tests for Two Correlations PASS Sample Sze Software Chapter 805 Tests for Two Correlatons Introducton The correlaton coeffcent (or correlaton), ρ, s a popular parameter for descrbng the strength of the assocaton between two varables.

More information

Multifactor Term Structure Models

Multifactor Term Structure Models 1 Multfactor Term Structure Models A. Lmtatons of One-Factor Models 1. Returns on bonds of all maturtes are perfectly correlated. 2. Term structure (and prces of every other dervatves) are unquely determned

More information

Chapter 10 Making Choices: The Method, MARR, and Multiple Attributes

Chapter 10 Making Choices: The Method, MARR, and Multiple Attributes Chapter 0 Makng Choces: The Method, MARR, and Multple Attrbutes INEN 303 Sergy Butenko Industral & Systems Engneerng Texas A&M Unversty Comparng Mutually Exclusve Alternatves by Dfferent Evaluaton Methods

More information

Mutual Funds and Management Styles. Active Portfolio Management

Mutual Funds and Management Styles. Active Portfolio Management utual Funds and anagement Styles ctve Portfolo anagement ctve Portfolo anagement What s actve portfolo management? How can we measure the contrbuton of actve portfolo management? We start out wth the CP

More information

Spurious Seasonal Patterns and Excess Smoothness in the BLS Local Area Unemployment Statistics

Spurious Seasonal Patterns and Excess Smoothness in the BLS Local Area Unemployment Statistics Spurous Seasonal Patterns and Excess Smoothness n the BLS Local Area Unemployment Statstcs Keth R. Phllps and Janguo Wang Federal Reserve Bank of Dallas Research Department Workng Paper 1305 September

More information

Notes are not permitted in this examination. Do not turn over until you are told to do so by the Invigilator.

Notes are not permitted in this examination. Do not turn over until you are told to do so by the Invigilator. UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton 2016-17 BANKING ECONOMETRICS ECO-7014A Tme allowed: 2 HOURS Answer ALL FOUR questons. Queston 1 carres a weght of 30%; queston 2 carres

More information

Module Contact: Dr P Moffatt, ECO Copyright of the University of East Anglia Version 2

Module Contact: Dr P Moffatt, ECO Copyright of the University of East Anglia Version 2 UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton 2012-13 FINANCIAL ECONOMETRICS ECO-M017 Tme allowed: 2 hours Answer ALL FOUR questons. Queston 1 carres a weght of 25%; Queston 2 carres

More information

Risk, return and stock performance measures

Risk, return and stock performance measures Rsk, return and stock performance measures MIRELA MOMCILOVIC Hgher School of Professonal Busness Studes Vladmra Perca-Valtera 4, Nov Sad bznscentar@gmal.com http://www.vps.ns.ac.rs/sr/nastavnk.1.30.html?sn=237

More information

Principles of Finance

Principles of Finance Prncples of Fnance Grzegorz Trojanowsk Lecture 6: Captal Asset Prcng Model Prncples of Fnance - Lecture 6 1 Lecture 6 materal Requred readng: Elton et al., Chapters 13, 14, and 15 Supplementary readng:

More information

Asset Management. Country Allocation and Mutual Fund Returns

Asset Management. Country Allocation and Mutual Fund Returns Country Allocaton and Mutual Fund Returns By Dr. Lela Heckman, Senor Managng Drector and Dr. John Mulln, Managng Drector Bear Stearns Asset Management Bear Stearns Actve Country Equty Executve Summary

More information

ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE)

ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE) ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE) May 17, 2016 15:30 Frst famly name: Name: DNI/ID: Moble: Second famly Name: GECO/GADE: Instructor: E-mal: Queston 1 A B C Blank Queston 2 A B C Blank Queston

More information

Lecture Note 2 Time Value of Money

Lecture Note 2 Time Value of Money Seg250 Management Prncples for Engneerng Managers Lecture ote 2 Tme Value of Money Department of Systems Engneerng and Engneerng Management The Chnese Unversty of Hong Kong Interest: The Cost of Money

More information

FM303. CHAPTERS COVERED : CHAPTERS 5, 8 and 9. LEARNER GUIDE : UNITS 1, 2 and 3.1 to 3.3. DUE DATE : 3:00 p.m. 19 MARCH 2013

FM303. CHAPTERS COVERED : CHAPTERS 5, 8 and 9. LEARNER GUIDE : UNITS 1, 2 and 3.1 to 3.3. DUE DATE : 3:00 p.m. 19 MARCH 2013 Page 1 of 11 ASSIGNMENT 1 ST SEMESTER : FINANCIAL MANAGEMENT 3 () CHAPTERS COVERED : CHAPTERS 5, 8 and 9 LEARNER GUIDE : UNITS 1, 2 and 3.1 to 3.3 DUE DATE : 3:00 p.m. 19 MARCH 2013 TOTAL MARKS : 100 INSTRUCTIONS

More information

Which of the following provides the most reasonable approximation to the least squares regression line? (a) y=50+10x (b) Y=50+x (d) Y=1+50x

Which of the following provides the most reasonable approximation to the least squares regression line? (a) y=50+10x (b) Y=50+x (d) Y=1+50x Whch of the followng provdes the most reasonable approxmaton to the least squares regresson lne? (a) y=50+10x (b) Y=50+x (c) Y=10+50x (d) Y=1+50x (e) Y=10+x In smple lnear regresson the model that s begn

More information

Elton, Gruber, Brown and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 4

Elton, Gruber, Brown and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 4 Elton, Gruber, Brown and Goetzmann Modern ortfolo Theory and Investment Analyss, 7th Edton Solutons to Text roblems: Chapter 4 Chapter 4: roblem 1 A. Expected return s the sum of each outcome tmes ts assocated

More information

Maturity Effect on Risk Measure in a Ratings-Based Default-Mode Model

Maturity Effect on Risk Measure in a Ratings-Based Default-Mode Model TU Braunschweg - Insttut für Wrtschaftswssenschaften Lehrstuhl Fnanzwrtschaft Maturty Effect on Rsk Measure n a Ratngs-Based Default-Mode Model Marc Gürtler and Drk Hethecker Fnancal Modellng Workshop

More information

Consumption Based Asset Pricing

Consumption Based Asset Pricing Consumpton Based Asset Prcng Mchael Bar Aprl 25, 208 Contents Introducton 2 Model 2. Prcng rsk-free asset............................... 3 2.2 Prcng rsky assets................................ 4 2.3 Bubbles......................................

More information

Examining the Validity of Credit Ratings Assigned to Credit Derivatives

Examining the Validity of Credit Ratings Assigned to Credit Derivatives Examnng the Valdty of redt atngs Assgned to redt Dervatves hh-we Lee Department of Fnance, Natonal Tape ollege of Busness No. 321, Sec. 1, h-nan d., Tape 100, Tawan heng-kun Kuo Department of Internatonal

More information

Teaching Note on Factor Model with a View --- A tutorial. This version: May 15, Prepared by Zhi Da *

Teaching Note on Factor Model with a View --- A tutorial. This version: May 15, Prepared by Zhi Da * Copyrght by Zh Da and Rav Jagannathan Teachng Note on For Model th a Ve --- A tutoral Ths verson: May 5, 2005 Prepared by Zh Da * Ths tutoral demonstrates ho to ncorporate economc ves n optmal asset allocaton

More information

Price and Quantity Competition Revisited. Abstract

Price and Quantity Competition Revisited. Abstract rce and uantty Competton Revsted X. Henry Wang Unversty of Mssour - Columba Abstract By enlargng the parameter space orgnally consdered by Sngh and Vves (984 to allow for a wder range of cost asymmetry,

More information

How diversifiable is firm-specific risk? James Bennett. and. Richard W. Sias * October 20, 2006

How diversifiable is firm-specific risk? James Bennett. and. Richard W. Sias * October 20, 2006 How dversfable s frm-specfc rsk? James Bennett and Rchard W. Sas * October 0, 006 JEL: G0, G, G, G4 Keywords: dversfcaton, dosyncratc rsk * Bennett s from the Department of Accountng and Fnance, Unversty

More information

Data Mining Linear and Logistic Regression

Data Mining Linear and Logistic Regression 07/02/207 Data Mnng Lnear and Logstc Regresson Mchael L of 26 Regresson In statstcal modellng, regresson analyss s a statstcal process for estmatng the relatonshps among varables. Regresson models are

More information

4. Greek Letters, Value-at-Risk

4. Greek Letters, Value-at-Risk 4 Greek Letters, Value-at-Rsk 4 Value-at-Rsk (Hull s, Chapter 8) Math443 W08, HM Zhu Outlne (Hull, Chap 8) What s Value at Rsk (VaR)? Hstorcal smulatons Monte Carlo smulatons Model based approach Varance-covarance

More information

MULTIPLE CURVE CONSTRUCTION

MULTIPLE CURVE CONSTRUCTION MULTIPLE CURVE CONSTRUCTION RICHARD WHITE 1. Introducton In the post-credt-crunch world, swaps are generally collateralzed under a ISDA Master Agreement Andersen and Pterbarg p266, wth collateral rates

More information

Quiz on Deterministic part of course October 22, 2002

Quiz on Deterministic part of course October 22, 2002 Engneerng ystems Analyss for Desgn Quz on Determnstc part of course October 22, 2002 Ths s a closed book exercse. You may use calculators Grade Tables There are 90 ponts possble for the regular test, or

More information

Analysis of Variance and Design of Experiments-II

Analysis of Variance and Design of Experiments-II Analyss of Varance and Desgn of Experments-II MODULE VI LECTURE - 4 SPLIT-PLOT AND STRIP-PLOT DESIGNS Dr. Shalabh Department of Mathematcs & Statstcs Indan Insttute of Technology Kanpur An example to motvate

More information

Survey of Math: Chapter 22: Consumer Finance Borrowing Page 1

Survey of Math: Chapter 22: Consumer Finance Borrowing Page 1 Survey of Math: Chapter 22: Consumer Fnance Borrowng Page 1 APR and EAR Borrowng s savng looked at from a dfferent perspectve. The dea of smple nterest and compound nterest stll apply. A new term s the

More information

IND E 250 Final Exam Solutions June 8, Section A. Multiple choice and simple computation. [5 points each] (Version A)

IND E 250 Final Exam Solutions June 8, Section A. Multiple choice and simple computation. [5 points each] (Version A) IND E 20 Fnal Exam Solutons June 8, 2006 Secton A. Multple choce and smple computaton. [ ponts each] (Verson A) (-) Four ndependent projects, each wth rsk free cash flows, have the followng B/C ratos:

More information

Problem Set 6 Finance 1,

Problem Set 6 Finance 1, Carnege Mellon Unversty Graduate School of Industral Admnstraton Chrs Telmer Wnter 2006 Problem Set 6 Fnance, 47-720. (representatve agent constructon) Consder the followng two-perod, two-agent economy.

More information

A copy can be downloaded for personal non-commercial research or study, without prior permission or charge

A copy can be downloaded for personal non-commercial research or study, without prior permission or charge Sganos, A. (2013) Google attenton and target prce run ups. Internatonal Revew of Fnancal Analyss. ISSN 1057-5219 Copyrght 2012 Elsever A copy can be downloaded for personal non-commercal research or study,

More information

Risk Reduction and Real Estate Portfolio Size

Risk Reduction and Real Estate Portfolio Size Rsk Reducton and Real Estate Portfolo Sze Stephen L. Lee and Peter J. Byrne Department of Land Management and Development, The Unversty of Readng, Whteknghts, Readng, RG6 6AW, UK. A Paper Presented at

More information

Chapter 3 Descriptive Statistics: Numerical Measures Part B

Chapter 3 Descriptive Statistics: Numerical Measures Part B Sldes Prepared by JOHN S. LOUCKS St. Edward s Unversty Slde 1 Chapter 3 Descrptve Statstcs: Numercal Measures Part B Measures of Dstrbuton Shape, Relatve Locaton, and Detectng Outlers Eploratory Data Analyss

More information

THE RELATIONSHIP BETWEEN AVERAGE ASSET CORRELATION AND DEFAULT PROBABILITY

THE RELATIONSHIP BETWEEN AVERAGE ASSET CORRELATION AND DEFAULT PROBABILITY JULY 22, 2009 THE RELATIONSHIP BETWEEN AVERAGE ASSET CORRELATION AND DEFAULT PROBABILITY AUTHORS Joseph Lee Joy Wang Jng Zhang ABSTRACT Asset correlaton and default probablty are crtcal drvers n modelng

More information

Creating a zero coupon curve by bootstrapping with cubic splines.

Creating a zero coupon curve by bootstrapping with cubic splines. MMA 708 Analytcal Fnance II Creatng a zero coupon curve by bootstrappng wth cubc splnes. erg Gryshkevych Professor: Jan R. M. Röman 0.2.200 Dvson of Appled Mathematcs chool of Educaton, Culture and Communcaton

More information

Incorrect Beliefs. Overconfidence. Types of Overconfidence. Outline. Overprecision 4/15/2017. Behavioral Economics Mark Dean Spring 2017

Incorrect Beliefs. Overconfidence. Types of Overconfidence. Outline. Overprecision 4/15/2017. Behavioral Economics Mark Dean Spring 2017 Incorrect Belefs Overconfdence Behavoral Economcs Mark Dean Sprng 2017 In objectve EU we assumed that everyone agreed on what the probabltes of dfferent events were In subjectve expected utlty theory we

More information

Labor Market Transitions in Peru

Labor Market Transitions in Peru Labor Market Transtons n Peru Javer Herrera* Davd Rosas Shady** *IRD and INEI, E-mal: jherrera@ne.gob.pe ** IADB, E-mal: davdro@adb.org The Issue U s one of the major ssues n Peru However: - The U rate

More information

Welfare Aspects in the Realignment of Commercial Framework. between Japan and China

Welfare Aspects in the Realignment of Commercial Framework. between Japan and China Prepared for the 13 th INFORUM World Conference n Huangshan, Chna, July 3 9, 2005 Welfare Aspects n the Realgnment of Commercal Framework between Japan and Chna Toshak Hasegawa Chuo Unversty, Japan Introducton

More information

Hybrid Tail Risk and Expected Stock Returns: When Does the Tail Wag the Dog?

Hybrid Tail Risk and Expected Stock Returns: When Does the Tail Wag the Dog? Hybrd Tal Rsk and Expected Stock Returns: When Does the Tal Wag the Dog? Turan G. Bal, a Nusret Cakc, b and Robert F. Whtelaw c* ABSTRACT Ths paper ntroduces a new, hybrd measure of covarance rsk n the

More information

Tests for Two Ordered Categorical Variables

Tests for Two Ordered Categorical Variables Chapter 253 Tests for Two Ordered Categorcal Varables Introducton Ths module computes power and sample sze for tests of ordered categorcal data such as Lkert scale data. Assumng proportonal odds, such

More information

An Empirical Study on Stock Price Responses to the Release of the Environmental Management Ranking in Japan. Abstract

An Empirical Study on Stock Price Responses to the Release of the Environmental Management Ranking in Japan. Abstract An Emprcal Study on Stock Prce esponses to the elease of the Envronmental Management ankng n Japan Fumko Takeda Unversy of Tokyo Takanor Tomozawa Unversy of Tokyo Abstract Ths paper nvestgates how stock

More information

REFINITIV INDICES PRIVATE EQUITY BUYOUT INDEX METHODOLOGY

REFINITIV INDICES PRIVATE EQUITY BUYOUT INDEX METHODOLOGY REFINITIV INDICES PRIVATE EQUITY BUYOUT INDEX METHODOLOGY 1 Table of Contents INTRODUCTION 3 TR Prvate Equty Buyout Index 3 INDEX COMPOSITION 3 Sector Portfolos 4 Sector Weghtng 5 Index Rebalance 5 Index

More information

Advisory. Category: Capital

Advisory. Category: Capital Advsory Category: Captal NOTICE* Subject: Alternatve Method for Insurance Companes that Determne the Segregated Fund Guarantee Captal Requrement Usng Prescrbed Factors Date: Ths Advsory descrbes an alternatve

More information

An Application of Alternative Weighting Matrix Collapsing Approaches for Improving Sample Estimates

An Application of Alternative Weighting Matrix Collapsing Approaches for Improving Sample Estimates Secton on Survey Research Methods An Applcaton of Alternatve Weghtng Matrx Collapsng Approaches for Improvng Sample Estmates Lnda Tompkns 1, Jay J. Km 2 1 Centers for Dsease Control and Preventon, atonal

More information

iii) pay F P 0,T = S 0 e δt when stock has dividend yield δ.

iii) pay F P 0,T = S 0 e δt when stock has dividend yield δ. Fnal s Wed May 7, 12:50-2:50 You are allowed 15 sheets of notes and a calculator The fnal s cumulatve, so you should know everythng on the frst 4 revews Ths materal not on those revews 184) Suppose S t

More information

Spatial Variations in Covariates on Marriage and Marital Fertility: Geographically Weighted Regression Analyses in Japan

Spatial Variations in Covariates on Marriage and Marital Fertility: Geographically Weighted Regression Analyses in Japan Spatal Varatons n Covarates on Marrage and Martal Fertlty: Geographcally Weghted Regresson Analyses n Japan Kenj Kamata (Natonal Insttute of Populaton and Socal Securty Research) Abstract (134) To understand

More information

NYSE Specialists Participation in the Posted Quotes

NYSE Specialists Participation in the Posted Quotes European Journal of Economc and Poltcal Studes NYSE Specalsts Partcpaton n the Posted Quotes Bülent Köksal 1 Abstract: Usng 2001 NYSE system order data n the decmal prcng envronment, we analyze how the

More information

2) In the medium-run/long-run, a decrease in the budget deficit will produce:

2) In the medium-run/long-run, a decrease in the budget deficit will produce: 4.02 Quz 2 Solutons Fall 2004 Multple-Choce Questons ) Consder the wage-settng and prce-settng equatons we studed n class. Suppose the markup, µ, equals 0.25, and F(u,z) = -u. What s the natural rate of

More information

The Effects of Industrial Structure Change on Economic Growth in China Based on LMDI Decomposition Approach

The Effects of Industrial Structure Change on Economic Growth in China Based on LMDI Decomposition Approach 216 Internatonal Conference on Mathematcal, Computatonal and Statstcal Scences and Engneerng (MCSSE 216) ISBN: 978-1-6595-96- he Effects of Industral Structure Change on Economc Growth n Chna Based on

More information

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9 Elton, Gruber, Brown, and Goetzmann Modern Portfolo Theory and Investment Analyss, 7th Edton Solutons to Text Problems: Chapter 9 Chapter 9: Problem In the table below, gven that the rskless rate equals

More information

WHAT ARE REGISTERED SHARES?

WHAT ARE REGISTERED SHARES? Regstered Shares Secton BECOME A REGISTERED SHAREHOLDER AND RECEIVE A LOYALTY BONUS +10% WHAT ARE? Holdng regstered shares means that your shares are regstered n your name, makng t easer for you to receve

More information

A Comparison of Statistical Methods in Interrupted Time Series Analysis to Estimate an Intervention Effect

A Comparison of Statistical Methods in Interrupted Time Series Analysis to Estimate an Intervention Effect Transport and Road Safety (TARS) Research Joanna Wang A Comparson of Statstcal Methods n Interrupted Tme Seres Analyss to Estmate an Interventon Effect Research Fellow at Transport & Road Safety (TARS)

More information

SYSTEMATIC LIQUIDITY, CHARACTERISTIC LIQUIDITY AND ASSET PRICING. Duong Nguyen* Tribhuvan N. Puri*

SYSTEMATIC LIQUIDITY, CHARACTERISTIC LIQUIDITY AND ASSET PRICING. Duong Nguyen* Tribhuvan N. Puri* SYSTEMATIC LIQUIDITY, CHARACTERISTIC LIQUIDITY AND ASSET PRICING Duong Nguyen* Trbhuvan N. Pur* Address for correspondence: Trbhuvan N. Pur, Professor of Fnance Char, Department of Accountng and Fnance

More information

Equilibrium in Prediction Markets with Buyers and Sellers

Equilibrium in Prediction Markets with Buyers and Sellers Equlbrum n Predcton Markets wth Buyers and Sellers Shpra Agrawal Nmrod Megddo Benamn Armbruster Abstract Predcton markets wth buyers and sellers of contracts on multple outcomes are shown to have unque

More information

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers II. Random Varables Random varables operate n much the same way as the outcomes or events n some arbtrary sample space the dstncton s that random varables are smply outcomes that are represented numercally.

More information

3/3/2014. CDS M Phil Econometrics. Vijayamohanan Pillai N. Truncated standard normal distribution for a = 0.5, 0, and 0.5. CDS Mphil Econometrics

3/3/2014. CDS M Phil Econometrics. Vijayamohanan Pillai N. Truncated standard normal distribution for a = 0.5, 0, and 0.5. CDS Mphil Econometrics Lmted Dependent Varable Models: Tobt an Plla N 1 CDS Mphl Econometrcs Introducton Lmted Dependent Varable Models: Truncaton and Censorng Maddala, G. 1983. Lmted Dependent and Qualtatve Varables n Econometrcs.

More information

Problems to be discussed at the 5 th seminar Suggested solutions

Problems to be discussed at the 5 th seminar Suggested solutions ECON4260 Behavoral Economcs Problems to be dscussed at the 5 th semnar Suggested solutons Problem 1 a) Consder an ultmatum game n whch the proposer gets, ntally, 100 NOK. Assume that both the proposer

More information

ASSET LIQUIDITY, STOCK LIQUIDITY, AND OWNERSHIP CONCENTRATION: EVIDENCE FROM THE ASE

ASSET LIQUIDITY, STOCK LIQUIDITY, AND OWNERSHIP CONCENTRATION: EVIDENCE FROM THE ASE ASSET LIQUIDITY, STOCK LIQUIDITY, AND OWNERSHIP CONCENTRATION: EVIDENCE FROM THE ASE Ghada Tayem*, Mohammad Tayeh**, Adel Bno** * Correspondng author: Department of Fnance, School of Busness, The Unversty

More information

On the Style Switching Behavior of Mutual Fund Managers

On the Style Switching Behavior of Mutual Fund Managers On the Style Swtchng Behavor of Mutual Fund Managers Bart Frjns Auckland Unversty of Technology, Auckland, New Zealand Auckland Centre for Fnancal Research Aaron Glbert Auckland Unversty of Technology,

More information

Morningstar After-Tax Return Methodology

Morningstar After-Tax Return Methodology Mornngstar After-Tax Return Methodology Mornngstar Research Report 24 October 2003 2003 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property of Mornngstar, Inc. Reproducton

More information

TRADING RULES IN HOUSING MARKETS WHAT CAN WE LEARN? GREG COSTELLO Curtin University of Technology

TRADING RULES IN HOUSING MARKETS WHAT CAN WE LEARN? GREG COSTELLO Curtin University of Technology ABSTRACT TRADING RULES IN HOUSING MARKETS WHAT CAN WE LEARN? GREG COSTELLO Curtn Unversty of Technology Ths paper examnes the applcaton of tradng rules n testng nformatonal effcency n housng markets. The

More information

Analysis of Moody s Bottom Rung Firms

Analysis of Moody s Bottom Rung Firms Analyss of Moody s Bottom Rung Frms Stoyu I. Ivanov * San Jose State Unversty Howard Turetsky San Jose State Unversty Abstract: Moody s publshed for the frst tme on March 10, 2009 a lst of Bottom Rung

More information

Measures of Spread IQR and Deviation. For exam X, calculate the mean, median and mode. For exam Y, calculate the mean, median and mode.

Measures of Spread IQR and Deviation. For exam X, calculate the mean, median and mode. For exam Y, calculate the mean, median and mode. Part 4 Measures of Spread IQR and Devaton In Part we learned how the three measures of center offer dfferent ways of provdng us wth a sngle representatve value for a data set. However, consder the followng

More information

Speed and consequences of venture capitalist post-ipo exit

Speed and consequences of venture capitalist post-ipo exit Speed and consequences of venture captalst post-ipo ext Imants Paegls * and Paranen Veeren ** Ths verson: January, 2010 * John Molson School of Busness, Concorda Unversty, 1450 Guy St. Montreal, QC, H1H

More information

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019 5-45/65: Desgn & Analyss of Algorthms January, 09 Lecture #3: Amortzed Analyss last changed: January 8, 09 Introducton In ths lecture we dscuss a useful form of analyss, called amortzed analyss, for problems

More information

Financial Crisis and Foreign Exchange Exposure of Korean Exporting Firms

Financial Crisis and Foreign Exchange Exposure of Korean Exporting Firms Fnancal Crss and Foregn Exchange Exposure of Korean Exportng Frms Jae-Young Cho a, Ronald A. Ratt b*, Sung-Wook Yoon c a Mnstry of Plannng and Budget, 520-3, Banpo-dong, Seocho-gu, Seoul 137-756, Korea

More information

A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME

A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME Vesna Radonć Đogatovć, Valentna Radočć Unversty of Belgrade Faculty of Transport and Traffc Engneerng Belgrade, Serba

More information

DOUBLE IMPACT. Credit Risk Assessment for Secured Loans. Jean-Paul Laurent ISFA Actuarial School University of Lyon & BNP Paribas

DOUBLE IMPACT. Credit Risk Assessment for Secured Loans. Jean-Paul Laurent ISFA Actuarial School University of Lyon & BNP Paribas DOUBLE IMPACT Credt Rsk Assessment for Secured Loans Al Chabaane BNP Parbas Jean-Paul Laurent ISFA Actuaral School Unversty of Lyon & BNP Parbas Julen Salomon BNP Parbas julen.salomon@bnpparbas.com Abstract

More information

>1 indicates country i has a comparative advantage in production of j; the greater the index, the stronger the advantage. RCA 1 ij

>1 indicates country i has a comparative advantage in production of j; the greater the index, the stronger the advantage. RCA 1 ij 69 APPENDIX 1 RCA Indces In the followng we present some maor RCA ndces reported n the lterature. For addtonal varants and other RCA ndces, Memedovc (1994) and Vollrath (1991) provde more thorough revews.

More information

Information Flow and Recovering the. Estimating the Moments of. Normality of Asset Returns

Information Flow and Recovering the. Estimating the Moments of. Normality of Asset Returns Estmatng the Moments of Informaton Flow and Recoverng the Normalty of Asset Returns Ané and Geman (Journal of Fnance, 2000) Revsted Anthony Murphy, Nuffeld College, Oxford Marwan Izzeldn, Unversty of Lecester

More information

Random Variables. b 2.

Random Variables. b 2. Random Varables Generally the object of an nvestgators nterest s not necessarly the acton n the sample space but rather some functon of t. Techncally a real valued functon or mappng whose doman s the sample

More information

Chapter 3 Student Lecture Notes 3-1

Chapter 3 Student Lecture Notes 3-1 Chapter 3 Student Lecture otes 3-1 Busness Statstcs: A Decson-Makng Approach 6 th Edton Chapter 3 Descrbng Data Usng umercal Measures 005 Prentce-Hall, Inc. Chap 3-1 Chapter Goals After completng ths chapter,

More information

Conditional Beta Capital Asset Pricing Model (CAPM) and Duration Dependence Tests

Conditional Beta Capital Asset Pricing Model (CAPM) and Duration Dependence Tests Condtonal Beta Captal Asset Prcng Model (CAPM) and Duraton Dependence Tests By Davd E. Allen 1 and Imbarne Bujang 1 1 School of Accountng, Fnance and Economcs, Edth Cowan Unversty School of Accountng,

More information

Construction Rules for Morningstar Canada Dividend Target 30 Index TM

Construction Rules for Morningstar Canada Dividend Target 30 Index TM Constructon Rules for Mornngstar Canada Dvdend Target 0 Index TM Mornngstar Methodology Paper January 2012 2011 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property of Mornngstar,

More information

Preliminary communication. Received: 20 th November 2013 Accepted: 10 th December 2013 SUMMARY

Preliminary communication. Received: 20 th November 2013 Accepted: 10 th December 2013 SUMMARY Elen Twrdy, Ph. D. Mlan Batsta, Ph. D. Unversty of Ljubljana Faculty of Martme Studes and Transportaton Pot pomorščakov 4 632 Portorož Slovena Prelmnary communcaton Receved: 2 th November 213 Accepted:

More information

The first step in using market prices

The first step in using market prices Strppng Coupons wth Lnear Programmng DAVID E. ALLEN, LYN C. THOMAS, AND HARRY ZHENG DAVID E. ALLEN s professor of fnance at the School of Fnance and Busness Economcs of Edth Cowan Unversty n Western Australa,

More information

UNDERPRICING AND EX ANTE UNCERTAINTY IN IPOS: EVIDENCE FROM THE TUNISIAN STOCK MARKET

UNDERPRICING AND EX ANTE UNCERTAINTY IN IPOS: EVIDENCE FROM THE TUNISIAN STOCK MARKET Busness Excellence and Management Jerb, A. UNDERPRICING AND EX ANTE UNCERTAINTY IN IPOS: EVIDENCE FROM THE TUNISIAN STOCK MARKET Ahmed JERIBI Unversty of Sfax, Sfax, Tunsa ahmedjerb07@yahoo.fr Abstract

More information

Testing Benjamin Graham s Net Current Asset Value Strategy in London

Testing Benjamin Graham s Net Current Asset Value Strategy in London Testng Benjamn Graham s Net Current Asset Value Strategy n London Yng Xao and Glen Arnold Centre for Economcs and Fnance Research Salford Busness School Unversty of Salford Salford Manchester M5 4WT, UK

More information

/ Computational Genomics. Normalization

/ Computational Genomics. Normalization 0-80 /02-70 Computatonal Genomcs Normalzaton Gene Expresson Analyss Model Computatonal nformaton fuson Bologcal regulatory networks Pattern Recognton Data Analyss clusterng, classfcaton normalzaton, mss.

More information

OCR Statistics 1 Working with data. Section 2: Measures of location

OCR Statistics 1 Working with data. Section 2: Measures of location OCR Statstcs 1 Workng wth data Secton 2: Measures of locaton Notes and Examples These notes have sub-sectons on: The medan Estmatng the medan from grouped data The mean Estmatng the mean from grouped data

More information

arxiv: v1 [q-fin.pm] 13 Feb 2018

arxiv: v1 [q-fin.pm] 13 Feb 2018 WHAT IS THE SHARPE RATIO, AND HOW CAN EVERYONE GET IT WRONG? arxv:1802.04413v1 [q-fn.pm] 13 Feb 2018 IGOR RIVIN Abstract. The Sharpe rato s the most wdely used rsk metrc n the quanttatve fnance communty

More information

Value of L = V L = VL = VU =$48,000,000 (ii) Owning 1% of firm U provides a dollar return of.01 [EBIT(1-T C )] =.01 x 6,000,000 = $60,000.

Value of L = V L = VL = VU =$48,000,000 (ii) Owning 1% of firm U provides a dollar return of.01 [EBIT(1-T C )] =.01 x 6,000,000 = $60,000. OLUTION 1. A company wll call a bond when the market prce of the bond s at or above the call prce. For a zero-coupon bond, ths wll never happen because the market prce wll always be below the face value.

More information

Property Type, Size and REIT Value

Property Type, Size and REIT Value THE JOURNAL OF REAL ESTATE RESEARCH Property Type, Sze and REIT Value Denns R. Capozza* Sohan Lee** Abstract. Ths study documents the wde devaton of securtzed real estate assets n equty REITs from the

More information

Risk and Returns of Commercial Real Estate: A Property Level Analysis

Risk and Returns of Commercial Real Estate: A Property Level Analysis Rsk and Returns of Commercal Real Estate: A Property Level Analyss Lang Peng Leeds School of Busness Unversty of Colorado at Boulder 419 UCB, Boulder, CO 80309-0419 Emal: lang.peng@colorado.edu Phone:

More information

Accounting discretion of banks during a financial crisis

Accounting discretion of banks during a financial crisis Accountng dscreton of banks durng a fnancal crss Harry Huznga * (Tlburg Unversty and CEPR) and Luc Laeven (Internatonal Monetary Fund and CEPR) November 6, 2009 Abstract: Ths paper shows that banks use

More information