jei jei A Bootstrap Analysis of the Nikkei 225 Abstract
|
|
- Arron Briggs
- 5 years ago
- Views:
Transcription
1 A Boosrap Analysis of he Nikkei 225 Journal of Economic Inegraion A Boosrap Analysis of he Nikkei 225 James J. Kung Ming Chuan Universiy Andrew P. Carverhill Universiy of Hong Kong Absrac This sudy inends o find ou wheher or no he Nikkei 225 evolves over ime in accordance wih he following four widely used processes for deermining sock prices: random walk wih a drif, AR(1), GARCH(1,1), and GARCH(1,1)-M. Given he fac ha, in acualiy, we have bu one sample of ime series daa, he moivaion of his sudy is o make use of he boosrap echnology o deal wih his one-sample problem. Specifically, we use he boosrap echnique o generae 2,000 arificial Nikkei series from each process and compue he reurn from he rading rule for each of he 2,000 arificial Nikkei series. Then, we consruc a 95% boosrap percenile inerval wih hese 2,000 reurns and deermine if i conains he reurn compued from he acual Nikkei series. If i does, we claim ha reurns from he arificial Nikkei series are in agreemen wih hose from he acual Nikkei series. Our resuls show ha, of he four processes, GARCH(1,1)-M generaes reurns ha are mos agreeable wih hose compued from he acual Nikkei series. An imporan implicaion of his sudy is ha a proper model for pricing Nikkei-relaed derivaives is one ha uses he GARCH(1,1)-M process o depic he dynamics of he Nikkei reurn series. JEL Classificaions: C15, C22, G15 Key Words: Nikkei 225, Boosrap mehod, Simple Moving Average, Reurn-generaing processes, Boosrap percenile inerval * Corresponding Auhor: James J. Kung; School of Managemen, Ming Chuan Universiy, 250 Chung Shan N. Road, Secion 5, Taipei 111, Taiwan, Tel: , Fax: , fnjames@mail.mcu.edu.w; Co-Auhor: Andrew P. Carverhill; School of Business, Universiy of Hong Kong, Pokfulam road, Hong Kong, Tel: (852) , Fax: (852) , carverhill@business.hku.hk. Acknowledgemens: We are graeful o Alan Wong of he Hong Kong Bapis Universiy and Kalok Chan, Susheng Wang, and John Wei of he Hong Kong Universiy of Science & Technology for useful commens and suggesions. c 2012-Cener for Economic Inegraion, Sejong Insiuion, Sejong Universiy, All Righs Reserved. 487
2 James J. Kung, Andrew P. Carverhill I. Inroducion Over he years, he Japanese sock marke has been consisenly classified as a developed marke by FTSE Group s lis, MSCI lis, Dow Jones lis, Russell Global lis, he Inernaional Finance Corporaion, and he Global Sock Markes Facbook. As a maer of fac, he Japanese sock marke has undergone a series of reforms since he 1990s. In paricular, in November 1996, Prime Miniser Ryuaro Hashimoo iniiaed a comprehensive reform package, ofen referred o as he Japanese Big Bang 1, wih he objecive of creaing a free, fair, and global financial marke. According o he prime miniser, free means creaing a marke in which marke principles prevail; fair means enhancing he fairness and ransparency of he marke hrough clearly defined accouning and supervisory rules; and global means reforming he marke in line wih inernaional sandards. Thanks o he reforms implemened during he 1990s, many sudies (e.g., Cajueiro and Tabak, 2004; Worhingon and Higgs, 2006; Lim, 2007; Chong and Chan, 2008) have provided empirical evidence ha he Japanese sock marke has become more efficien. The above said, i is imporan o sudy how he Japanese sock marke one of he larges markes in he world in erms of rading volume and marke capializaion - evolved before and afer hose financial reforms were implemened in he 1990s. Specifically, his sudy aims o find ou wheher or no he Nikkei 225 (henceforh referred o as he Nikkei), he benchmark indicaor of he Japanese sock marke, evolves over ime in accordance wih some widely used random processes for sock prices. Accordingly, by means of a simple moving average rading rule, we employ he boosrap mehod of Efron (1979, 1982) o invesigae how closely reurns from he Nikkei are in agreemen wih reurns generaed from he following four widely used random processes for sock prices: a random walk wih a drif RW(α )), an auoregressive process of order one (AR(1)), and wo generalized auoregressive condiional heeroscedasic models - (GARCH(1,1) and GARCH(1,1)-M). Given he imporance of he Nikkei, he implicaion of his sudy is ha, for paricipans (e.g., invesors, speculaors, and hedgers) in he Japanese sock marke, a beer knowledge of he dynamics of he index is crucial for appropriaely managing sock marke risk and correcly pricing Nikkei-relaed derivaives 2. However, for ime series daa, we are consrained by he fac ha we have only one hisorical sample. Hence, i is hardly surprising ha previous empirical findings 3 based on differen 1 One major focus of he Japanese Big Bang reform was on insiuional changes. These changes included he following wo broad areas: (1) Improving he efficiency and compeiiveness of domesic financial insiuions (e.g., foreign financial companies were allowed o operae more freely in Japan); and (2) Abolishing monopoly powers previously enjoyed by domesic banks, securiies companies, and insurance firms (e.g., domesic and foreign financial insiuions would compee on an equal fooing for Japan s muli-rillion pension fund business). For deails, see Craig (1998) and Hall (1998). 2 Some Nikkei-relaed derivaives include he Nikkei 225 index opions raded on he Osaka Securiies Exchange and he Singapore Exchange, and he Nikkei 225 warrans raded on he American and Torono Sock Exchanges. 3 For example, Alexander (1961, 1964), Jensen and Beningon (1970), Bollerslev (1986), Engle, e al. (1987), French, e al. (1987), Chou (1988), Conrad and Kaul (1988, 1989), Bollerslev, e al. (1992), and Tsay (2005). 488
3 A Boosrap Analysis of he Nikkei 225 ime series daa are, o a cerain exen, divided on asse price dynamics. Accordingly, he moivaion of his sudy is o make use of boosrap echnology o deal wih his one sample problem. Simply pu, in he conex of his sudy, he boosrap enables us o generae a random a large number of samples (2,000 samples in his sudy) hrough replacemen wih each of he above four random processes such ha each of hese so-called boosrap samples (i.e., arificial Nikkei series) will possess he same saisical properies as he acual Nikkei series. Using hese 2,000 arificial Nikkei series for consrucing a 95% boosrap percenile inerval, we can invesigae how closely reurns from he acual Nikkei series mach reurns generaed from each of he four processes. The logic of our boosrap implemenaion can beer be undersood from anoher perspecive. Suppose he Nikkei lierally evolves hrough ime according o, say, a random walk wih a drif (i.e., RW(α )). Then, he acual Nikkei series is simply a sample drawn from his RW(α ) process. Hence, i is highly likely ha he reurn compued from his acual Nikkei series will fall wihin he 95% boosrap percenile inerval consruced using he 2,000 reurns generaed from his RW(α ) process. The res of he paper proceeds as follows: Secion II describes he Nikkei price series and gives a descripion of he simple moving average rading rule. Secion III describes our boosrap mehod, presens reasons for using he four reurn-generaing processes, and uses an illusraion o show how o implemen our boosrap by which arificial Nikkei price series are generaed for each of he four processes. In Secion IV, we presen and discuss our empirical resuls. In Secion V, we conclude his sudy and ouline he implicaions of his research. II. Daa and Simple Moving Average Trading Rule A. The Nikkei Daa Series The daa used are daily closing prices of he Nikkei (see Figure 1) from January 1, 1971, o December 31, 2010 a oal of 10,436 observaions. They were rerieved from he DaaSream daabase. Since no major financial reform was implemened in Japan before he 1990s, we pariion he enire sample period ino wo equal sub-periods 4 : and We compue he daily reurn as he naural log difference of he Nikkei prices. Tha is, R (1) = log( P ) log( P 1 ) 4 The sub-period is he ime when he Japanese sock marke moved forward a full speed; he sub-period is he ime which is ofen referred o as he wo los decades of Japan. 489
4 James J. Kung, Andrew P. Carverhill where P and 1 for he ime from day -1 o day. P are he closing prices of he Nikkei on day -1 and day, and R is he reurn In an efficien marke, securiy reurns are independen of one anoher over ime because new informaion comes o he marke in a random and unpredicable manner, and securiy prices respond insanly and accuraely o his new informaion. Hence, he magniude of he auocorrelaion in securiy reurns can offer some clues as o he efficiency of he marke. Simply pu, auocorrelaion should be insignifican if he marke is efficien. From Table 1, we noe ha he daily auocorrelaion for is saisically significan a he 1% level a lags 1, 2, 3, and 5, whereas ha for is saisically significan a he 1% level only a lag 1. In oher words, for he earlier sub-period, he reurn on day is likely o depend on reurns on days -1, -2, -3, and -5; whereas for he laer sub-period, he reurn on day is likely o depend only on he reurn on day -1. Hence, he auocorrelaions provide a rough indicaion ha he Japanese sock marke displayed relaively greaer efficiency for he sub-period. B. The Trading Rule The rading rule used in his sudy is he Simple Moving Average 5 (SMA). The n-day Moving Average (MA) on day is 1 1 M +, n = Pk = n+ 1 n n k = n+ 1 n [ P + P + + P P ] where P k is he closing price of he Nikkei on day k. According o SMA rules, a buy signal is generaed when he closing price rises above he n- day MA and a sell signal is generaed when he closing price falls below he n-day MA. Tha is, an invesor would ake a long posiion in he Nikkei when a buy signal is generaed and, conversely, a shor posiion in he Nikkei when a sell signal is generaed. When a signal is generaed, SMA rules require ha he posiion be mainained unil he closing price peneraes he n- day MA again. A commonly used SMA rule is 1-100, where he MA is 100 days. In his sudy, we use he following SMA rules: (1, 20), (1, 50), (1, 100), and (1, 200). Each rule is evaluaed wih bands of 0% and 1%, making a oal of eigh SMA rules. A band is used o reduce he number of imes an invesor would have o move ino and ou of he marke. For example, Brock e al. (1992), Bessembinder and Chan (1998), and Siegel (2002) all use a 1% band for heir SMA rules. (2) 5 See Edwards, e al. (2007) for deails on he simple moving average and oher rading rules. 490
5 A Boosrap Analysis of he Nikkei 225 III. Boosrap Implemenaion In his secion, we firs give a simple descripion of he boosrap mehod 6 cusomized for his sudy; hen, we poin ou why i is jusifiable o use he four random processes for generaing arificial Nikkei series; and finally, we make use of an illusraion o show how o implemen our boosrap by which arificial Nikkei price series are generaed for each of he four processes. A. The Boosrap Mehod For our boosrap implemenaion, we apply each of he four random processes o he acual Nikkei reurn series o obain heir respecive esimaed parameers (e.g., α in RW(α )) and residuals. The residuals are hen redrawn wih replacemen o form a scrambled residual series which is hen used wih he esimaed parameers o generae arificial Nikkei reurn series such ha each of hese so-called boosrap samples (i.e., arificial Nikkei series) will possess he same saisical properies as he acual Nikkei series. In his sudy, he relevan saisic is he reurn from he 2000SMA rading rule compued from he Nikkei seriesseries. Specifically, we use he boosrap o generae 2,000 arificial Nikkei series 7 from each of he four processes and compue he reurn from he SMA rading rule for each of hese 2,000 arificial Nikkei series. Then, following on from Efron and Tibshirani (1993), we consruc a 95% boosrap percenile inerval wih hese 2,000 reurns. If he reurn from he SMA rule compued from he acual Nikkei series falls wihin his 95% percenile inerval, hen we claim ha his reurn agrees wih hose generaed from he arificial Nikkei series and, furhermore, infer ha he acual Nikkei series is in agreemen wih hose arificial Nikkei series generaed for a given random process. In oher words, he acual Nikkei series is like a sample drawn from his process. B. The Four Reurn-Generaing Processes The four random processes 8 for generaing arificial Nikkei reurn series are as follows: RW(α ) AR(1) GARCH(1,1) R = α + ε (3) R = α + βr + ε (4) 1 R α + ε = (5) u 6 See Efron and Tibshirani (1993) for deails. 7 According o Efron and Tibshirani (1993), 2,000 arificial Nikkei series are more han enough for esimaion accuracy purposes. 8 Alernaively, we can wrie he four processes in a compac form as R = u + ε, where u = α in RW(α ) and GARCH(1,1); α + βr in AR(1); and u = α + βσ 2 in GARCH(1,1)-M. = 1 491
6 James J. Kung, Andrew P. Carverhill GARCH(1,1)-M 9 R = α + βσ + ε 2 (6) For equaions (3) and (4), ε is independenly and idenically disribued. For equaions (5) and (6), σ = + + a bε 1 cσ 1, ε = σ z, and z ~ N( 0,1). Tha is, R is he reurn on day, ε is normally disribued and serially uncorrelaed, z is normally 10 disribued wih zero mean and 2 uni variance, and σ is a linear funcion of he square of he las period s error (i.e., ε 1) and 2 of he las period s condiional variance (i.e., σ 1). Noe ha if β in equaion (6) is posiive and saisically significan, hen increased risk (as measured by an increase in he condiional variance σ ) resuls in an increase in reurn 2 R. Hence, β can be regarded as he amoun of risk. We choose he four random processes because hey have been found by numerous sudies o bes characerize he dynamics of asse/sock reurns. These four encompass a wide range of random processes commonly used for asse/sock reurns. A his juncure, some relaed references are in order. For he RW(α ) process, Fama (1995) claims ha he empirical evidence o dae provides srong suppor for he random walk model. For he AR(1) process 11, Conrad and Kaul (1989) show ha a firs-order auocorrelaion of 0.20 is found for a value-weighed porfolio of he larges companies over he period, and ha higher order auocorrelaion beyond a lag of one day is basically zero. For he GARCH (1,1) process, Brooks (2008) saes ha in general a GARCH(1,1) model will be sufficien o capure he volailiy clusering in he daa, and rarely is any higher order model esimaed or even enerained in he academic finance lieraure. For he GARCH(1,1)-M process, Chou (1988) fis such a process o he weekly reurns of he NYSE value-weighed index over he period and finds he exisence of changing equiy premiums. For deails, see Alexander (1961, 1964), Fama (1965, 1970, 1995), and Jensen and Beningon (1970) for random walks; Conrad and Kaul (1988, 1989) and Tsay (2005) for he AR(1) process; and Bollerslev (1986), Engle e al. (1987), French e al. (1987), Chou (1988), Bollerslev e al. (1992), and Brook (2008) for he wo GARCH processes. C. An Illusraion As an illusraion, we use an AR(1) process o demonsrae how o implemen our boosrap by aking he following seps. (i) Based on he acual Nikkei reurn series, we esimae he wo parameers in equaion (4) using he ordinary leas squares mehod and obain he 9 The GARCH(1,1)-M process can also be expressed in such a form ha he condiional mean is linear in he condiional sandard 2 deviaion σ raher han in he condiional variance σ. 10 An alernaive o a sandard normal disribuion is o assume ha z follows a sandard Suden s disribuion, in which case he densiy has more probabiliy mass in he ails. 11 In his sudy, a major reason for using an AR(1) process is ha he auocorrelaions for daily reurns from he Nikkei over he wo sub-periods are boh saisically significan a he 1% level a lag 1. See Table
7 A Boosrap Analysis of he Nikkei 225 wo esimaes α and β. (ii) We compue he residual as e = R R, where = 1, 2,, N and R = α + β R 1. Hence, we obain a series of residuals; ha is, { e 1, e 2,, e N }. (iii) For each j, we randomly draw a residual wih replacemen from he residual series and form R j = α + β R j 1 + e j(where j = 1, 2,, N) o generae an arificial AR(1) Nikkei reurn series. (iv) We conver each arificial AR(1) Nikkei reurn series ino an arificial AR(1) Nikkei price series using equaion (1). (v) Similar o compuing he daily reurn from he SMA rule using acual Nikkei price series, we compue he daily reurn from he SMA rule for buy and for sell using arificial AR(1) Nikkei price series. (vi) Repeaing seps (i) (v), we obain 2,000 daily b b b s s s reurns (denoed by R1, R2,..., R2000) for buy and 2,000 daily reurns (denoed by R1, R2,..., R2000) for sell compued respecively from 2,000 arificial AR(1) Nikkei price series. Following Efron and Tibshirani (1993), we consruc a 95% boosrap percenile inerval such ha he 2.5h percenile and 97.5h percenile of he 2,000 daily reurns (for buy and for sell) compued from arificial Nikkei price series are, respecively, he lower and upper limis for he inerval. Specifically, arranging he 2,000 daily reurns in ascending order such ha b b b b s s s s R ( 1) R(2)... R(1999) R(2000) for buy and R ( 1) R(2)... R(1999) R(2000) for sell, we find ha he 95% boosrap inerval 12 is [R b b (51), R (1950) ] for buy and [R s s (51), R (1950) ] for sell. Tha said, we deermine if he daily reurn (for buy and for sell) from each SMA rule compued from he acual Nikkei price series lies wihin his 95% boosrap inerval. IV. Empirical Resuls We use he ordinary leas squares mehod o esimae he parameers of he RW(α) and AR(1) processes, and he maximum likelihood mehod o esimae he parameers of he GARCH(1,1) and GARCH(1,1)-M processes. Table 2 presens he parameer esimaes for he four processes over he wo sub-periods. The parameers are esimaed using he RATS economeric package. Table 3 shows he daily reurns from he eigh SMA rules based on acual Nikkei price series. For , each of he eigh rules resuls in a posiive daily reurn for buy and a negaive daily reurn for sell, suggesing ha he marke ended o move upward over his sub-period. For , each of he eigh rules resuls in a daily reurn for buy smaller han ha for sell, implying ha he marke ended o move downward over his sub-period. In he following secions, we will deermine if he daily reurns from he eigh SMA rules in Table 3 compued from he acual Nikkei price series lie wihin heir respecive 95% boosrap percenile inervals under each of he four processes. In Tables 4-7, for each of he eigh SMA rules, Mean is he average value of he 2, b b Tha is, R ( 51) and R are he 2.5h and 97.5h perceniles of he 2,000 daily reurns for buy; ( 1950) R s s (51) and R are he 2.5h and ( 1950) 97.5h perceniles of he 2,000 daily reurns for sell. 493
8 James J. Kung, Andrew P. Carverhill daily reurns compued from arificial Nikkei price series, R (51) ( and R denoe he 2.5h (1950) and 97.5h perceniles of he 2,000 daily reurns for buy and for sell. For example, considering he (1, 20, 0%) rule for buy in he firs hree rows of Table 4, is he average value of he 2,000 daily reurns, and and are he 2.5h and 97.5h perceniles of he 2,000 daily reurns. Tha is, [ , ] is a 95% boosrap confidence inerval. For visual clariy, hose 95% inervals are shaded if he daily reurns in Table 3 compued from he acual Nikkei price series lie wihin heir respecive inervals. A. Resuls based on Arificial RW(α ) Nikkei Series Table 4 shows he daily reurns from he eigh SMA rules based on he arificial Nikkei price series generaed from he random walk process for he wo sub-periods. For , none of he eigh SMA rules for buy resuls in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals; bu hree of he eigh SMA rules for sell resul in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals. Tha is, he (1, 20, 0%), (1, 50, 0%), and (1, 100, 0%) rules for sell resul in he daily reurns of , , and (see Table 3) from he acual Nikkei series lying wihin [ , ], [ , ], and [ , ], respecively. For , four SMA rules for buy and five SMA rules for sell resul in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals İn comparison, daily reurns compued from he acual Nikkei series appear more likely o have been generaed from he RW(α ) process for he sub-period han for he sub-period. Given he fac ha he Japanese sock marke has become more efficien (see Secion I) afer a series of financial reforms were implemened during he 1990s, i is no surprise ha he Nikkei evolved over he second sub-period as if i were a sample likely drawn from he RW(α ) process. B. Resuls based on Arificial AR(1) Nikkei Series The AR(1) process is used o deec wheher he resuls from he SMA rules are caused by daily auocorrelaions in he series. If he reurns are posiively auo- correlaed, a higher (lower) reurn oday will end o be followed by a higher (lower) reurn on he following day; if he reurns are negaively auocorrelaed, a higher (lower) reurn oday will end o be followed by a lower (higher) reurn on he following day. The parameer esimaes for he AR(1) process in Table 2 indicae some degree of posiive auocorrelaion for (where β = ) and some degree of negaive auocorrelaion for (where β = ). 494
9 A Boosrap Analysis of he Nikkei 225 Table 5 shows he daily reurns from he eigh SMA rules based on he arificial Nikkei price series generaed from he AR(1) process for he wo sub-periods. For , four SMA rules for buy and seven SMA rules for sell resul in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals. For , none of he eigh SMA rules for boh buy and sell resuls in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals. In comparison, daily reurns compued from he acual Nikkei series appear more likely o have been generaed from he AR(1) process for he sub-period han for he sub-period. Given he fac ha he sock marke has become more efficien as a resul of he reforms implemened since he 1990s, i is no surprising ha he dynamics of he Nikkei exhibied no obvious sign of auocorrelaion over he second sub-period. C. Resuls based on Arificial GARCH(1,1) Nikkei Series The GARCH(1,1) process allows he condiional variance o be dependen on one previous variance and one lagged squared error. Table 6 shows he daily reurns from he eigh SMA rules based on he arificial Nikkei price series generaed from he GARCH(1,1) process. For , wo SMA rules for buy and six SMA rules for sell resul in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals. For , only one SMA rule for buy bu hree SMA rules for sell resul in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals. In comparison, daily reurns compued from he acual Nikkei series appear more likely o have been generaed from he GARCH(1,1) process for he firs sub-period han for he second sub-period. D. Resuls based on Arificial GARCH(1,1)-M Nikkei Series Finance heory claims ha invesors should be rewarded a higher reurn for bearing addiional risk. The GARCH(1,1)-M process is designed o model such a phenomenon, where he condiional variance of asse reurns is included in he reurn equaion (see equaion (6)). Table 7 shows he daily reurns from he eigh SMA rules based on he arificial Nikkei price series generaed from he GARCH(1,1)-M process. For , five SMA rules for buy and all eigh SMA rules for sell resul in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals. For , four SMA rules for buy and six SMA rules for sell resul in he daily reurns compued from he acual Nikkei series lying wihin heir respecive 95% inervals. Of he four random processes, he GARCH(1,1)-M process appears o have generaed daily reurns ha are mos likely in agreemen wih hose from he acual Nikkei series. 495
10 James J. Kung, Andrew P. Carverhill V. Conclusion and Implicaion This sudy aims o find ou wheher or no reurns from he Nikkei are agreeable wih hose generaed from four widely used random processes for sock prices. Given he fac ha we have only one sample of any ime series daa, he moivaion of his sudy is o use he boosrap o deal wih his one-sample problem To proceed, we use he boosrap o generae 2,000 arificial Nikkei series for each process and compue he reurn from he SMA rading rule for each of hese 2,000 arificial Nikkei series. Then, we se up a 95% boosrap percenile inerval wih hese 2,000 reurns and deermine if he inerval conains he reurn compued from he acual Nikkei series. If i does, we claim ha his reurn agrees wih hose generaed from he arificial Nikkei series and, moreover, we infer ha he acual Nikkei series is in agreemen wih hose arificial Nikkei series generaed for a given process. Our empirical resuls indicae ha, of he four random processes, GARCH(1,1)-M generaes reurns ha are mos agreeable wih hose compued from he acual Nikkei series. Given he imporance of he Japanese sock marke in he world, a beer grasp of he dynamics of he Nikkei is indispensable for appropriaely handling Japanese sock marke risk and correcly pricing Nikkei-relaed derivaives. A relevan case in poin is he Nikkei 225 index opions, which are acively raded on he Osaka Securiies Exchange and he Singapore Exchange. Given our resuls ha boh he random walk wih a drif 13 and he GARCH(1,1) process 14 are inadequae for depicing he Nikkei reurn series, an imporan implicaion of his sudy is ha a more appropriae model for pricing Nikkei 225 index opions is one ha uses he GARCH(1,1)-M process o characerize he dynamics of he Nikkei reurn series. Received 06 April 2011, Revised 28 April 2012, Acceped 30 May 2012 References Alexander, S.S. (1961), Price Movemens in Speculaive Markes: Trends or Random Walks, Indusrial Managemen Review, Vol. 2, pp Alexander, S.S. (1964), Price Movemens in Speculaive Markes: Trends or Random Walks, Number 2, Indusrial Managemen Review, Vol. 5, pp Bessembinder, H., Chan, K. (1998), Marke Efficiency and he Reurns o Technical Analysis, Financial Managemen, Vol. 27, No. 2, pp The coninuous-ime analog of he random walk wih drif in equaion (3) is he arihmeic Brownian moion, which means ha he Nikkei price process in equaion (1) is a geomeric Brownian moion (GBM). Our resuls imply ha he well-known Black-Scholes opion pricing model (1973), which assumes a GBM for he price process, is no appropriae for pricing Nikkei 225 opions. 14 Our resuls also imply ha he GARCH opion pricing model of Duan (1995) is no appropriae for pricing Nikkei 225 opions. 496
11 A Boosrap Analysis of he Nikkei 225 Black, F., Scholes, M. (1973), The Pricing of Opions and Corporae Liabiliies, Journal of Poliical Economy, Vol. 81, pp Bollerslev, T. (1986), Generalized Auoregressive Condiional Heeroskedasiciy, Journal of Economerics, Vol. 31, pp Bollerslev, T., Chou, R.Y., Kroner, K.F. (1992), ARCH Modeling in Finance: A Review of he Theory and Empirical Evidence, Journal of Economerics, Vol. 52, pp Brock, W., Lakonishok, J., LeBaron, B. (1992), Simple Technical Trading Rules and he Sochasic Properies of Sock Reurns, Journal of Finance, Vol. 47, No. 5, pp Brooks, C. (2008), Inroducory Economerics for Finance, 2nd ediion, Cambridge Universiy Press, Cambridge, UK. Cajueiro, D.O., Tabak, B.M. (2004), Ranking Efficiency for Emerging Markes, Chaos, Solions and Fracals, Vol. 22, pp Chong, T. T., Chan, S. T. (2008), Srucural Change in he Efficiency of he Japanese Sock Marke afer he Millennium, Economics Bullein, Vol. 7, No. 7, pp Chou, R.Y. (1988), Volailiy Persisence and Sock Valuaions: Some Empirical Evidence using GARCH, Journal of Applied Economerics, Vol. 3, pp Conrad, J., Kaul, G. (1988), Time-Variaion in Expeced Reurns, Journal of Business, Vol. 61, No. 4, pp Conrad, J., Kaul, G. (1989), Mean Reversion in Shor-Horizon Expeced Reurns, Review of Financial Sudies, Vol. 2, No. 2, pp Craig, V.V. (1998), Financial Deregulaion in Japan, FDIC Banking Review, Vol. 11, No. 3, pp Duan, J.C. (1995), The GARCH Opion Pricing Model, Mahemaical Finance, Vol. 5, No. 1, pp Edwards, R.D., Magee, J., Bassei, W.H.C. (2007), Technical Analysis of Sock Trends, 9h ediion, AMA- COM, New York. Efron, B. (1979), Boosrap Mehods: Anoher Look a he Jackknife, Annals of Saisics, Vol. 7, No. 1, pp Efron, B. (1982), The Jackknife, he Boosrap, and Oher Resampling Plans, Sociey for Indusrial and Applied Mahemaics, Philadelphia. Efron, B., Tibshirani, R. (1993), An Inroducion o he Boosrap, Chapman and Hall, New York. Engle, R.F., Lilien, D.M., Robins, R.P. (1987), Esimaing Time-Varying Risk Premia in he Term Srucure: The ARCH-M Model, Economerica, Vol. 55, No. 2, pp Fama, E.F. (1965), The Behavior of Sock Marke Prices, Journal of Business, Vol. 38, pp Fama, E.F. (1970), Efficien Capial Markes: A Review of Theory and Empirical Work, Journal of Finance, Vol. 25, pp Fama, E.F. (1995), Random Walks in Sock Marke Prices, Financial Analyss Journal, January-February, pp French, K.R., Schwer, G.W., Sambaugh, R.F. (1987), Expeced Sock Reurns and Volailiy, Journal of Financial Economics, Vol. 19, No. 1, pp
12 James J. Kung, Andrew P. Carverhill Hall, M.J.B. (1998), Financial Reform in Japan: Causes and Consequences, Edward Elgar, Norhampon. Jensen, M., Beningon, G. (1970), Random Walks and Technical Theories: Some Addiional Evidence, Journal of Finance, Vol. 25, pp LeRoy, S.F. (1982), Expecaions Models of Asse Prices: A Survey of Theory, Journal of Finance, Vol. 37, pp Lim, K.P. (2007), Ranking Marke Efficiency for Sock Markes: A Nonlinear Perspecive, Physica A, Vol. 376, pp Siegel, J.J. (2002), Socks for he Long Run, 3rd ediion, McGraw-Hill, New York. Tsay, R.S. (2005), Analysis of Financial Time Series, 2nd ediion, John Wiley & Sons, Hoboken, New Jersey. Worhingon, A.C., Higgs, H. (2006), Weak-Form Marke Efficiency in Asian Emerging and Developed Equiy Markes: Comparaive Tess of Random Walk Behavior, working paper, Universiy of Wollongong, Ausralia. Figure 1. The Nikkei 225 (1971~2010) Table 1. Summary Saisics for Daily Reurns on Acual Nikkei Series 1971~ ~2010 Number of Observaions Average Daily Reurn Daily Sandard Deviaion Esimaed auocorrelaions: Lag ** ** Lag ** * Lag ** * Lag * * Lag ** Noe: Numbers wih * (**) are significan a 5% (1%) level for a wo-ailed es. 498
13 A Boosrap Analysis of he Nikkei 225 Table 2. Parameer Esimaes for he Four Reurn-Generaing Processes Process Parameer 1971~ ~2010 RW(α ) α ( ) ( ) AR(1) α ( ) ( ) β ( ) ( ) GARCH(1,1) α ( ) ( ) α ( ) ( ) b ( ) ( ) c ( ) ( ) GARCH(1,1)-M α ( ) ( ) β ( ) ( ) α ( ) ( ) b ( ) ( ) c ( ) ( ) Noe: Parameers are esimaed using RATS. Numbers in parenheses are sandard -raios. 499
14 James J. Kung, Andrew P. Carverhill Table 3. Daily Reurns from Simple Moving Average Rules based on Acual Nikkei Series 1971~ ~2010 Rule Buy Sell Buy Sell (1, 20, 0%) (1, 50, 0%) (1,100,0%) (1,200,0%) (1, 20, 1%) (1, 50, 1%) (1,100,1%) (1,200,1%) Average Noe: Simple Moving Average rules are idenified as (shor, long, band), where shor and long are he lenghs of shor and long moving averages respecively, and band is he percenage difference required o generae a buy or sell signal. 500
15 A Boosrap Analysis of he Nikkei 225 Table 4. Daily Reurns from Simple Moving Average Rules based on Arificial RW(α) Nikkei Series 1971~ ~2010 Rule Buy Sell Buy Sell (1, 20, 0%) Mean R (51) R (1950) (1, 50, 0%) Mean R (51) R (1950) (1,100,0%) Mean R (51) R (1950) (1,200,0%) Mean R (51) R (1950) (1, 20, 1%) Mean R (51) R (1950) (1, 50, 1%) Mean R (51) R (1950) (1,100,1%) Mean R (51) R (1950) (1,200,1%) Mean R (51) R (1950) Average Noe: Simple Moving Average rules are idenified as (shor, long, band), where shor and long are he lenghs of shor and long moving averages respecively, and band is he percenage difference required o generae a buy or sell signal. Mean is he average value of he 2,000 daily reurns. R (51) and R (1950) are he 2.5h and 97.5h perceniles of he 2,000 reurns for buy and for sell. Shaded R (51) and R (1950) are he 95% boosrap inervals ha conain he daily reurn from he acual Nikkei series. 501
16 James J. Kung, Andrew P. Carverhill 502 Table 5. Daily Reurns from Simple Moving Average Rules based on Arificial AR(1) Nikkei Series 1971~ ~2010 Rule Buy Sell Buy Sell (1, 20, 0%) Mean R (51) R (1950) (1, 50, 0%) Mean R (51) R (1950) (1,100,0%) Mean R (51) R (1950) (1,200,0%) Mean R (51) R (1950) (1, 20, 1%) Mean R (51) R (1950) (1, 50, 1%) Mean R (51) R (1950) (1,100,1%) Mean R (51) R (1950) (1,200,1%) Mean R (51) R (1950) Average Noe: Simple Moving Average rules are idenified as (shor, long, band), where shor and long are he lenghs of shor and long moving averages respecively, and band is he percenage difference required o generae a buy or sell signal. Mean is he average value of he 2,000 daily reurns. R (51) and R (1950) are he 2.5h and 97.5h perceniles of he 2,000 reurns for buy and for sell. Shaded R (51) and R (1950) are he 95% boosrap inervals ha conain he daily reurn from he acual Nikkei series.
17 A Boosrap Analysis of he Nikkei 225 Table 6. Daily Reurns from Simple Moving Average Rules based on Arificial GARCH(1,1) Nikkei Series 1971~ ~2010 Rule Buy Sell Buy Sell (1, 20, 0%) Mean R (51) R (1950) (1, 50, 0%) Mean R (51) R (1950) (1,100,0%) Mean R (51) R (1950) (1,200,0%) Mean R (51) R (1950) (1, 20, 1%) Mean R (51) R (1950) (1, 50, 1%) Mean R (51) R (1950) (1,100,1%) Mean R (51) R (1950) (1,200,1%) Mean R (51) R (1950) Average Noe: Simple Moving Average rules are idenified as (shor, long, band), where shor and long are he lenghs of shor and long moving averages respecively, and band is he percenage difference required o generae a buy or sell signal. Mean is he average value of he 2,000 daily reurns. R (51) and R (1950) are he 2.5h and 97.5h perceniles of he 2,000 reurns for buy and for sell. Shaded R (51) and R (1950) are he 95% boosrap inervals ha conain he daily reurn from he acual Nikkei series. 503
18 James J. Kung, Andrew P. Carverhill 504 Table 7. Daily Reurns from Simple Moving Average Rules based on Arificial GARCH(1,1)-M Nikkei Series 1971~ ~2010 Rule Buy Sell Buy Sell (1, 20, 0%) Mean R (51) R (1950) (1, 50, 0%) Mean R (51) R (1950) (1,100,0%) Mean R (51) R (1950) (1,200,0%) Mean R (51) R (1950) (1, 20, 1%) Mean R (51) R (1950) (1, 50, 1%) Mean R (51) R (1950) (1,100,1%) Mean R (51) R (1950) (1,200,1%) Mean R (51) R (1950) Average Noe: Simple Moving Average rules are idenified as (shor, long, band), where shor and long are he lenghs of shor and long moving averages respecively, and band is he percenage difference required o generae a buy or sell signal. Mean is he average value of he 2,000 daily reurns. R (51) and R (1950) are he 2.5h and 97.5h perceniles of he 2,000 reurns for buy and for sell. Shaded R (51) and R (1950) are he 95% boosrap inervals ha conain he daily reurn from he acual Nikkei series.
19 A Boosrap Analysis of he Nikkei
On the Impact of Inflation and Exchange Rate on Conditional Stock Market Volatility: A Re-Assessment
MPRA Munich Personal RePEc Archive On he Impac of Inflaion and Exchange Rae on Condiional Sock Marke Volailiy: A Re-Assessmen OlaOluwa S Yaya and Olanrewaju I Shiu Deparmen of Saisics, Universiy of Ibadan,
More informationComparison of back-testing results for various VaR estimation methods. Aleš Kresta, ICSP 2013, Bergamo 8 th July, 2013
Comparison of back-esing resuls for various VaR esimaion mehods, ICSP 3, Bergamo 8 h July, 3 THE MOTIVATION AND GOAL In order o esimae he risk of financial invesmens, i is crucial for all he models o esimae
More informationA Note on Missing Data Effects on the Hausman (1978) Simultaneity Test:
A Noe on Missing Daa Effecs on he Hausman (978) Simulaneiy Tes: Some Mone Carlo Resuls. Dikaios Tserkezos and Konsaninos P. Tsagarakis Deparmen of Economics, Universiy of Cree, Universiy Campus, 7400,
More informationIJRSS Volume 2, Issue 2 ISSN:
A LOGITIC BROWNIAN MOTION WITH A PRICE OF DIVIDEND YIELDING AET D. B. ODUOR ilas N. Onyango _ Absrac: In his paper, we have used he idea of Onyango (2003) he used o develop a logisic equaion used in naural
More informationVOLATILITY CLUSTERING, NEW HEAVY-TAILED DISTRIBUTION AND THE STOCK MARKET RETURNS IN SOUTH KOREA
64 VOLATILITY CLUSTERING, NEW HEAVY-TAILED DISTRIBUTION AND THE STOCK MARKET RETURNS IN SOUTH KOREA Yoon Hong, PhD, Research Fellow Deparmen of Economics Hanyang Universiy, Souh Korea Ji-chul Lee, PhD,
More informationProceedings of the 48th European Study Group Mathematics with Industry 1
Proceedings of he 48h European Sudy Group Mahemaics wih Indusry 1 ADR Opion Trading Jasper Anderluh and Hans van der Weide TU Delf, EWI (DIAM), Mekelweg 4, 2628 CD Delf jhmanderluh@ewiudelfnl, JAMvanderWeide@ewiudelfnl
More informationAvailable online at ScienceDirect
Available online a www.sciencedirec.com ScienceDirec Procedia Economics and Finance 8 ( 04 658 663 s Inernaional Conference 'Economic Scienific Research - Theoreical, Empirical and Pracical Approaches',
More informationFinancial Econometrics Jeffrey R. Russell Midterm Winter 2011
Name Financial Economerics Jeffrey R. Russell Miderm Winer 2011 You have 2 hours o complee he exam. Use can use a calculaor. Try o fi all your work in he space provided. If you find you need more space
More informationFinancial Markets And Empirical Regularities An Introduction to Financial Econometrics
Financial Markes And Empirical Regulariies An Inroducion o Financial Economerics SAMSI Workshop 11/18/05 Mike Aguilar UNC a Chapel Hill www.unc.edu/~maguilar 1 Ouline I. Hisorical Perspecive on Asse Prices
More informationFORECASTING WITH A LINEX LOSS: A MONTE CARLO STUDY
Proceedings of he 9h WSEAS Inernaional Conference on Applied Mahemaics, Isanbul, Turkey, May 7-9, 006 (pp63-67) FORECASTING WITH A LINEX LOSS: A MONTE CARLO STUDY Yasemin Ulu Deparmen of Economics American
More informationEstimating Earnings Trend Using Unobserved Components Framework
Esimaing Earnings Trend Using Unobserved Componens Framework Arabinda Basisha and Alexander Kurov College of Business and Economics, Wes Virginia Universiy December 008 Absrac Regressions using valuaion
More informationPricing formula for power quanto options with each type of payoffs at maturity
Global Journal of Pure and Applied Mahemaics. ISSN 0973-1768 Volume 13, Number 9 (017, pp. 6695 670 Research India Publicaions hp://www.ripublicaion.com/gjpam.hm Pricing formula for power uano opions wih
More information1 Purpose of the paper
Moneary Economics 2 F.C. Bagliano - Sepember 2017 Noes on: F.X. Diebold and C. Li, Forecasing he erm srucure of governmen bond yields, Journal of Economerics, 2006 1 Purpose of he paper The paper presens
More informationThe Mathematics Of Stock Option Valuation - Part Four Deriving The Black-Scholes Model Via Partial Differential Equations
The Mahemaics Of Sock Opion Valuaion - Par Four Deriving The Black-Scholes Model Via Parial Differenial Equaions Gary Schurman, MBE, CFA Ocober 1 In Par One we explained why valuing a call opion as a sand-alone
More informationR e. Y R, X R, u e, and. Use the attached excel spreadsheets to
HW # Saisical Financial Modeling ( P Theodossiou) 1 The following are annual reurns for US finance socks (F) and he S&P500 socks index (M) Year Reurn Finance Socks Reurn S&P500 Year Reurn Finance Socks
More informationFinal Exam Answers Exchange Rate Economics
Kiel Insiu für Welwirhschaf Advanced Sudies in Inernaional Economic Policy Research Spring 2005 Menzie D. Chinn Final Exam Answers Exchange Rae Economics This exam is 1 ½ hours long. Answer all quesions.
More informationDYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus University Toruń Krzysztof Jajuga Wrocław University of Economics
DYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus Universiy Toruń 2006 Krzyszof Jajuga Wrocław Universiy of Economics Ineres Rae Modeling and Tools of Financial Economerics 1. Financial Economerics
More informationThe Relationship between Money Demand and Interest Rates: An Empirical Investigation in Sri Lanka
The Relaionship beween Money Demand and Ineres Raes: An Empirical Invesigaion in Sri Lanka R. C. P. Padmasiri 1 and O. G. Dayarana Banda 2 1 Economic Research Uni, Deparmen of Expor Agriculure 2 Deparmen
More informationThe Impact of Interest Rate Liberalization Announcement in China on the Market Value of Hong Kong Listed Chinese Commercial Banks
Journal of Finance and Invesmen Analysis, vol. 2, no.3, 203, 35-39 ISSN: 224-0998 (prin version), 224-0996(online) Scienpress Ld, 203 The Impac of Ineres Rae Liberalizaion Announcemen in China on he Marke
More informationFINAL EXAM EC26102: MONEY, BANKING AND FINANCIAL MARKETS MAY 11, 2004
FINAL EXAM EC26102: MONEY, BANKING AND FINANCIAL MARKETS MAY 11, 2004 This exam has 50 quesions on 14 pages. Before you begin, please check o make sure ha your copy has all 50 quesions and all 14 pages.
More informationVaR and Low Interest Rates
VaR and Low Ineres Raes Presened a he Sevenh Monreal Indusrial Problem Solving Workshop By Louis Doray (U de M) Frédéric Edoukou (U de M) Rim Labdi (HEC Monréal) Zichun Ye (UBC) 20 May 2016 P r e s e n
More informationModels of Default Risk
Models of Defaul Risk Models of Defaul Risk 1/29 Inroducion We consider wo general approaches o modelling defaul risk, a risk characerizing almos all xed-income securiies. The srucural approach was developed
More informationA Study of Process Capability Analysis on Second-order Autoregressive Processes
A Sudy of Process apabiliy Analysis on Second-order Auoregressive Processes Dja Shin Wang, Business Adminisraion, TransWorld Universiy, Taiwan. E-mail: shin@wu.edu.w Szu hi Ho, Indusrial Engineering and
More informationModelling Volatility Using High, Low, Open and Closing Prices: Evidence from Four S&P Indices
Inernaional Research Journal of Finance and Economics ISSN 1450-2887 Issue 28 (2009) EuroJournals Publishing, Inc. 2009 hp://www.eurojournals.com/finance.hm Modelling Volailiy Using High, Low, Open and
More informationEquivalent Martingale Measure in Asian Geometric Average Option Pricing
Journal of Mahemaical Finance, 4, 4, 34-38 ublished Online Augus 4 in SciRes hp://wwwscirporg/journal/jmf hp://dxdoiorg/436/jmf4447 Equivalen Maringale Measure in Asian Geomeric Average Opion ricing Yonggang
More informationAsymmetry and Leverage in Stochastic Volatility Models: An Exposition
Asymmery and Leverage in Sochasic Volailiy Models: An xposiion Asai, M. a and M. McAleer b a Faculy of conomics, Soka Universiy, Japan b School of conomics and Commerce, Universiy of Wesern Ausralia Keywords:
More informationIntroduction to Black-Scholes Model
4 azuhisa Masuda All righs reserved. Inroducion o Black-choles Model Absrac azuhisa Masuda Deparmen of Economics he Graduae Cener, he Ciy Universiy of New York, 365 Fifh Avenue, New York, NY 6-439 Email:
More informationStock Market Behaviour Around Profit Warning Announcements
Sock Marke Behaviour Around Profi Warning Announcemens Henryk Gurgul Conen 1. Moivaion 2. Review of exising evidence 3. Main conjecures 4. Daa and preliminary resuls 5. GARCH relaed mehodology 6. Empirical
More informationCapital Strength and Bank Profitability
Capial Srengh and Bank Profiabiliy Seok Weon Lee 1 Asian Social Science; Vol. 11, No. 10; 2015 ISSN 1911-2017 E-ISSN 1911-2025 Published by Canadian Cener of Science and Educaion 1 Division of Inernaional
More informationOn the Relationship between Time-Varying Price dynamics of the Underlying. Stocks: Deregulation Effect on the Issuance of Third-Party Put Warrant
On he Relaionship beween Time-Varying Price dynamics of he Underlying Socks: Deregulaion Effec on he Issuance of Third-Pary Pu Warran Yi-Chen Wang * Deparmen of Financial Operaions, Naional Kaohsiung Firs
More informationStock Index Volatility: the case of IPSA
MPRA Munich Personal RePEc Archive Sock Index Volailiy: he case of IPSA Rodrigo Alfaro and Carmen Gloria Silva 31. March 010 Online a hps://mpra.ub.uni-muenchen.de/5906/ MPRA Paper No. 5906, posed 18.
More informationINSTITUTE OF ACTUARIES OF INDIA
INSTITUTE OF ACTUARIES OF INDIA EXAMINATIONS 05 h November 007 Subjec CT8 Financial Economics Time allowed: Three Hours (14.30 17.30 Hrs) Toal Marks: 100 INSTRUCTIONS TO THE CANDIDATES 1) Do no wrie your
More informationDocumentation: Philadelphia Fed's Real-Time Data Set for Macroeconomists First-, Second-, and Third-Release Values
Documenaion: Philadelphia Fed's Real-Time Daa Se for Macroeconomiss Firs-, Second-, and Third-Release Values Las Updaed: December 16, 2013 1. Inroducion We documen our compuaional mehods for consrucing
More informationINSTITUTE OF ACTUARIES OF INDIA
INSIUE OF ACUARIES OF INDIA EAMINAIONS 23 rd May 2011 Subjec S6 Finance and Invesmen B ime allowed: hree hours (9.45* 13.00 Hrs) oal Marks: 100 INSRUCIONS O HE CANDIDAES 1. Please read he insrucions on
More informationUCLA Department of Economics Fall PhD. Qualifying Exam in Macroeconomic Theory
UCLA Deparmen of Economics Fall 2016 PhD. Qualifying Exam in Macroeconomic Theory Insrucions: This exam consiss of hree pars, and you are o complee each par. Answer each par in a separae bluebook. All
More informationA NOTE ON BUSINESS CYCLE NON-LINEARITY IN U.S. CONSUMPTION 247
Journal of Applied Economics, Vol. VI, No. 2 (Nov 2003), 247-253 A NOTE ON BUSINESS CYCLE NON-LINEARITY IN U.S. CONSUMPTION 247 A NOTE ON BUSINESS CYCLE NON-LINEARITY IN U.S. CONSUMPTION STEVEN COOK *
More informationPARAMETER ESTIMATION IN A BLACK SCHOLES
PARAMETER ESTIMATIO I A BLACK SCHOLES Musafa BAYRAM *, Gulsen ORUCOVA BUYUKOZ, Tugcem PARTAL * Gelisim Universiy Deparmen of Compuer Engineering, 3435 Isanbul, Turkey Yildiz Technical Universiy Deparmen
More informationPortfolio Risk of Chinese Stock Market Measured by VaR Method
Vol.53 (ICM 014), pp.6166 hp://dx.doi.org/10.1457/asl.014.53.54 Porfolio Risk of Chinese Sock Marke Measured by VaR Mehod Wu Yudong School of Basic Science,Harbin Universiy of Commerce,Harbin Email:wuyudong@aliyun.com
More informationMODELLING THE US SWAP SPREAD
MODEING THE US SWAP SPREAD Hon-un Chung, School of Accouning and Finance, The Hong Kong Polyechnic Universiy, Email: afalan@ine.polyu.edu.hk Wai-Sum Chan, Deparmen of Finance, The Chinese Universiy of
More informationDOES EVA REALLY HELP LONG TERM STOCK PERFORMANCE?
DOES EVA REALLY HELP LONG TERM STOCK PERFORMANCE? Wesley M. Jones, Jr. The Ciadel wes.jones@ciadel.edu George Lowry, Randolph Macon College glowry@rmc.edu ABSTRACT Economic Value Added (EVA) as a philosophy
More informationThis specification describes the models that are used to forecast
PCE and CPI Inflaion Differenials: Convering Inflaion Forecass Model Specificaion By Craig S. Hakkio This specificaion describes he models ha are used o forecas he inflaion differenial. The 14 forecass
More information(1 + Nominal Yield) = (1 + Real Yield) (1 + Expected Inflation Rate) (1 + Inflation Risk Premium)
5. Inflaion-linked bonds Inflaion is an economic erm ha describes he general rise in prices of goods and services. As prices rise, a uni of money can buy less goods and services. Hence, inflaion is an
More informationExtreme Risk Value and Dependence Structure of the China Securities Index 300
MPRA Munich Personal RePEc Archive Exreme Risk Value and Dependence Srucure of he China Securiies Index 300 Terence Tai Leung Chong and Yue Ding and Tianxiao Pang The Chinese Universiy of Hong Kong, The
More informationEVA NOPAT Capital charges ( = WACC * Invested Capital) = EVA [1 P] each
VBM Soluion skech SS 2012: Noe: This is a soluion skech, no a complee soluion. Disribuion of poins is no binding for he correcor. 1 EVA, free cash flow, and financial raios (45) 1.1 EVA wihou adjusmens
More informationSubdivided Research on the Inflation-hedging Ability of Residential Property: A Case of Hong Kong
Subdivided Research on he -hedging Abiliy of Residenial Propery: A Case of Hong Kong Guohua Huang 1, Haili Tu 2, Boyu Liu 3,* 1 Economics and Managemen School of Wuhan Universiy,Economics and Managemen
More informationWatch out for the impact of Scottish independence opinion polls on UK s borrowing costs
Wach ou for he impac of Scoish independence opinion polls on UK s borrowing coss Cosas Milas (Universiy of Liverpool; email: cosas.milas@liverpool.ac.uk) and Tim Worrall (Universiy of Edinburgh; email:
More informationPrinciples of Finance CONTENTS
Principles of Finance CONENS Value of Bonds and Equiy... 3 Feaures of bonds... 3 Characerisics... 3 Socks and he sock marke... 4 Definiions:... 4 Valuing equiies... 4 Ne reurn... 4 idend discoun model...
More informationSystemic Risk Illustrated
Sysemic Risk Illusraed Jean-Pierre Fouque Li-Hsien Sun March 2, 22 Absrac We sudy he behavior of diffusions coupled hrough heir drifs in a way ha each componen mean-revers o he mean of he ensemble. In
More informationReturn-Volume Dynamics of Individual Stocks: Evidence from an Emerging Market
Reurn-Volume Dynamics of Individual Socks: Evidence from an Emerging Marke Cein Ciner College of Business Adminisraion Norheasern Universiy 413 Hayden Hall Boson, MA 02214 Tel: 617-373 4775 E-mail: c.ciner@neu.edu
More information4452 Mathematical Modeling Lecture 17: Modeling of Data: Linear Regression
Mah Modeling Lecure 17: Modeling of Daa: Linear Regression Page 1 5 Mahemaical Modeling Lecure 17: Modeling of Daa: Linear Regression Inroducion In modeling of daa, we are given a se of daa poins, and
More informationCh. 10 Measuring FX Exposure. Is Exchange Rate Risk Relevant? MNCs Take on FX Risk
Ch. 10 Measuring FX Exposure Topics Exchange Rae Risk: Relevan? Types of Exposure Transacion Exposure Economic Exposure Translaion Exposure Is Exchange Rae Risk Relevan?? Purchasing Power Pariy: Exchange
More informationThe Empirical Study about Introduction of Stock Index Futures on the Volatility of Spot Market
ibusiness, 013, 5, 113-117 hp://dx.doi.org/10.436/ib.013.53b04 Published Online Sepember 013 (hp://www.scirp.org/journal/ib) 113 The Empirical Sudy abou Inroducion of Sock Index Fuures on he Volailiy of
More informationNON-LINEAR MODELING OF DAILY EXCHANGE RATE RETURNS, VOLATILITY, AND NEWS IN A SMALL DEVELOPING ECONOMY. José R. Sánchez-Fung Kingston University
NON-LINEAR MODELING OF DAILY EXCHANGE RATE RETURNS, VOLATILITY, AND NEWS IN A SMALL DEVELOPING ECONOMY José R. Sánchez-Fung Kingson Universiy Absrac This paper models daily reurns, volailiy, and news in
More informationLinkages and Performance Comparison among Eastern Europe Stock Markets
Easern Europe Sock Marke hp://dx.doi.org/10.14195/2183-203x_39_4 Linkages and Performance Comparison among Easern Europe Sock Markes Faculdade de Economia da Universidade de Coimbra and GEMF absrac This
More informationOption Valuation of Oil & Gas E&P Projects by Futures Term Structure Approach. Hidetaka (Hugh) Nakaoka
Opion Valuaion of Oil & Gas E&P Projecs by Fuures Term Srucure Approach March 9, 2007 Hideaka (Hugh) Nakaoka Former CIO & CCO of Iochu Oil Exploraion Co., Ld. Universiy of Tsukuba 1 Overview 1. Inroducion
More informationUNIVERSITY OF MORATUWA
MA5100 UNIVERSITY OF MORATUWA MSC/POSTGRADUATE DIPLOMA IN FINANCIAL MATHEMATICS 009 MA 5100 INTRODUCTION TO STATISTICS THREE HOURS November 009 Answer FIVE quesions and NO MORE. Quesion 1 (a) A supplier
More informationAn Alternative Test of Purchasing Power Parity
An Alernaive Tes of Purchasing Power Pariy Frederic H. Wallace* Deparmen of Managemen and Mareing Prairie View A&M Universiy Prairie View, Texas 77446 and Gary L. Shelley Deparmen of Economics, Finance,
More informationThe role of the SGT Density with Conditional Volatility, Skewness and Kurtosis in the Estimation of VaR: A Case of the Stock Exchange of Thailand
Available online a www.sciencedirec.com Procedia - Social and Behavioral Sciences 4 ( ) 736 74 The Inernaional (Spring) Conference on Asia Pacific Business Innovaion and Technology Managemen, Paaya, Thailand
More informationPricing FX Target Redemption Forward under. Regime Switching Model
In. J. Conemp. Mah. Sciences, Vol. 8, 2013, no. 20, 987-991 HIKARI Ld, www.m-hikari.com hp://dx.doi.org/10.12988/ijcms.2013.311123 Pricing FX Targe Redempion Forward under Regime Swiching Model Ho-Seok
More informationCENTRO DE ESTUDIOS MONETARIOS Y FINANCIEROS T. J. KEHOE MACROECONOMICS I WINTER 2011 PROBLEM SET #6
CENTRO DE ESTUDIOS MONETARIOS Y FINANCIEROS T J KEHOE MACROECONOMICS I WINTER PROBLEM SET #6 This quesion requires you o apply he Hodrick-Presco filer o he ime series for macroeconomic variables for he
More informationSTOCK MARKET EFFICIENCY IN NEPAL
40 Vol. Issue 5, May 0, ISSN 3 5780 ABSTRACT STOCK MARKET EFFICIENCY IN NEPAL JEETENDRA DANGOL* *Lecurer, Public Youh Campus, Tribhuvan Universiy, Nepal. The paper examines random-walk behaviour and weak-form
More informationThe Effect of Open Market Repurchase on Company s Value
The Effec of Open Marke Repurchase on Company s Value Xu Fengju Wang Feng School of Managemen, Wuhan Universiy of Technology, Wuhan, P.R.China, 437 (E-mail:xfju@63.com, wangf9@63.com) Absrac This paper
More informationGUIDELINE Solactive Gold Front Month MD Rolling Futures Index ER. Version 1.1 dated April 13 th, 2017
GUIDELINE Solacive Gold Fron Monh MD Rolling Fuures Index ER Version 1.1 daed April 13 h, 2017 Conens Inroducion 1 Index specificaions 1.1 Shor name and ISIN 1.2 Iniial value 1.3 Disribuion 1.4 Prices
More informationMarket risk VaR historical simulation model with autocorrelation effect: A note
Inernaional Journal of Banking and Finance Volume 6 Issue 2 Aricle 9 3--29 Marke risk VaR hisorical simulaion model wih auocorrelaion effec: A noe Wananee Surapaioolkorn SASIN Chulalunkorn Universiy Follow
More informationt=1 C t e δt, and the tc t v t i t=1 C t (1 + i) t = n tc t (1 + i) t C t (1 + i) t = C t vi
Exam 4 is Th. April 24. You are allowed 13 shees of noes and a calculaor. ch. 7: 137) Unless old oherwise, duraion refers o Macaulay duraion. The duraion of a single cashflow is he ime remaining unil mauriy,
More informationDescription of the CBOE Russell 2000 BuyWrite Index (BXR SM )
Descripion of he CBOE Russell 2000 BuyWrie Index (BXR SM ) Inroducion. The CBOE Russell 2000 BuyWrie Index (BXR SM ) is a benchmark index designed o rack he performance of a hypoheical a-he-money buy-wrie
More informationNon-Stationary Processes: Part IV. ARCH(m) (Autoregressive Conditional Heteroskedasticity) Models
Alber-Ludwigs Universiy Freiburg Deparmen of Economics Time Series Analysis, Summer 29 Dr. Sevap Kesel Non-Saionary Processes: Par IV ARCH(m) (Auoregressive Condiional Heeroskedasiciy) Models Saionary
More informationModeling Volatility of Exchange Rate of Chinese Yuan against US Dollar Based on GARCH Models
013 Sixh Inernaional Conference on Business Inelligence and Financial Engineering Modeling Volailiy of Exchange Rae of Chinese Yuan agains US Dollar Based on GARCH Models Marggie Ma DBA Program Ciy Universiy
More informationDescription of the CBOE S&P 500 2% OTM BuyWrite Index (BXY SM )
Descripion of he CBOE S&P 500 2% OTM BuyWrie Index (BXY SM ) Inroducion. The CBOE S&P 500 2% OTM BuyWrie Index (BXY SM ) is a benchmark index designed o rack he performance of a hypoheical 2% ou-of-he-money
More informationMarket and Information Economics
Marke and Informaion Economics Preliminary Examinaion Deparmen of Agriculural Economics Texas A&M Universiy May 2015 Insrucions: This examinaion consiss of six quesions. You mus answer he firs quesion
More informationCHAPTER CHAPTER18. Openness in Goods. and Financial Markets. Openness in Goods, and Financial Markets. Openness in Goods,
Openness in Goods and Financial Markes CHAPTER CHAPTER18 Openness in Goods, and Openness has hree disinc dimensions: 1. Openness in goods markes. Free rade resricions include ariffs and quoas. 2. Openness
More informationVolatility and Hedging Errors
Volailiy and Hedging Errors Jim Gaheral Sepember, 5 1999 Background Derivaive porfolio bookrunners ofen complain ha hedging a marke-implied volailiies is sub-opimal relaive o hedging a heir bes guess of
More informationIMPACTS OF FINANCIAL DERIVATIVES MARKET ON OIL PRICE VOLATILITY. Istemi Berk Department of Economics Izmir University of Economics
IMPACTS OF FINANCIAL DERIVATIVES MARKET ON OIL PRICE VOLATILITY Isemi Berk Deparmen of Economics Izmir Universiy of Economics OUTLINE MOTIVATION CRUDE OIL MARKET FUNDAMENTALS LITERATURE & CONTRIBUTION
More informationPortfolio investments accounted for the largest outflow of SEK 77.5 billion in the financial account, which gave a net outflow of SEK billion.
BALANCE OF PAYMENTS DATE: 27-11-27 PUBLISHER: Saisics Sweden Balance of Paymens and Financial Markes (BFM) Maria Falk +46 8 6 94 72, maria.falk@scb.se Camilla Bergeling +46 8 6 942 6, camilla.bergeling@scb.se
More informationOnline Appendix to: Implementing Supply Routing Optimization in a Make-To-Order Manufacturing Network
Online Appendix o: Implemening Supply Rouing Opimizaion in a Make-To-Order Manufacuring Nework A.1. Forecas Accuracy Sudy. July 29, 2008 Assuming a single locaion and par for now, his sudy can be described
More informationMeasuring and Forecasting the Daily Variance Based on High-Frequency Intraday and Electronic Data
Measuring and Forecasing he Daily Variance Based on High-Frequency Inraday and Elecronic Daa Faemeh Behzadnejad Supervisor: Benoi Perron Absrac For he 4-hr foreign exchange marke, Andersen and Bollerslev
More informationMultivariate Volatility and Spillover Effects in Financial Markets
Mulivariae Volailiy and Spillover Effecs in Financial Markes Bernardo Veiga and Michael McAleer School of Economics and Commerce, Universiy of Wesern Ausralia (Bernardo@suden.ecel.uwa.edu.au, Michael.McAleer@uwa.edu.au)
More informationOptimal Early Exercise of Vulnerable American Options
Opimal Early Exercise of Vulnerable American Opions March 15, 2008 This paper is preliminary and incomplee. Opimal Early Exercise of Vulnerable American Opions Absrac We analyze he effec of credi risk
More informationMay 2007 Exam MFE Solutions 1. Answer = (B)
May 007 Exam MFE Soluions. Answer = (B) Le D = he quarerly dividend. Using formula (9.), pu-call pariy adjused for deerminisic dividends, we have 0.0 0.05 0.03 4.50 =.45 + 5.00 D e D e 50 e = 54.45 D (
More informationGuglielmo Maria Caporale Brunel; University. Abstract
Herding behaviour in exreme marke condiions: he case of he Ahens Sock Exchange Guglielmo Maria Caporale Brunel; Universiy Foini Economou Universiy of Piraeus Nikolaos Philippas Universiy of Piraeus Absrac
More informationAn Analysis of Trend and Sources of Deficit Financing in Nepal
Economic Lieraure, Vol. XII (8-16), December 014 An Analysis of Trend and Sources of Defici Financing in Nepal Deo Narayan Suihar ABSTRACT Defici financing has emerged as an imporan ool of financing governmen
More informationThe Expiration-Day Effect of Derivatives Trading: Evidence from the Taiwanese Stock Market
Journal of Applied Finance & Banking, vol. 5, no. 4, 2015, 53-60 ISSN: 1792-6580 (prin version), 1792-6599 (online) Scienpress Ld, 2015 The Expiraion-Day Effec of Derivaives Trading: Evidence from he Taiwanese
More informationBlack-Scholes Model and Risk Neutral Pricing
Inroducion echniques Exercises in Financial Mahemaics Lis 3 UiO-SK45 Soluions Hins Auumn 5 eacher: S Oriz-Laorre Black-Scholes Model Risk Neural Pricing See Benh s book: Exercise 44, page 37 See Benh s
More informationThe probability of informed trading based on VAR model
Universiy of Wollongong Research Online Faculy of Commerce - Papers (Archive) Faculy of Business 29 The probabiliy of informed rading based on VAR model Min Xu Beihang Universiy, xumin_828@sina.com Shancun
More informationIntroduction. Enterprises and background. chapter
NACE: High-Growh Inroducion Enerprises and background 18 chaper High-Growh Enerprises 8 8.1 Definiion A variey of approaches can be considered as providing he basis for defining high-growh enerprises.
More informationHave bull and bear markets changed over time? Empirical evidence from the US-stock market
Journal of Finance and Invesmen Analysis, vol.1, no.1, 2012, 151-171 ISSN: 2241-0988 (prin version), 2241-0996 (online) Inernaional Scienific Press, 2012 Have bull and bear markes changed over ime? Empirical
More informationSpeculator identification: A microstructure approach
Speculaor idenificaion: A microsrucure approach Ben Z. Schreiber* Augus 2011 Absrac This paper suggess a mehodology for idenifying speculaors in FX markes by examining boh he speculaive characerisics of
More informationThe day of the week effect patterns on stock market return and volatility: Evidence for the Athens Stock Exchange
Neapolis Universiy HEPHAESTUS Reposiory School of Economic Sciences and Business hp://hephaesus.nup.ac.cy Conference papers 005 The day of he week effec paerns on sock marke reurn and volailiy: Evidence
More informationA Screen for Fraudulent Return Smoothing in the Hedge Fund Industry
A Screen for Fraudulen Reurn Smoohing in he Hedge Fund Indusry Nicolas P.B. Bollen Vanderbil Universiy Veronika Krepely Universiy of Indiana May 16 h, 2006 Hisorical performance Cum. Mean Sd Dev CSFB Tremon
More informationDecision Science Letters
Decision Science Leers (3) 9 4 Conens liss available a GrowingScience Decision Science Leers homepage: www.growingscience.com/dsl Esimaing he risk-reurn radeoff in MENA Sock Markes Salim Lahmiri * ESCA
More informationMAFS Quantitative Modeling of Derivative Securities
MAFS 5030 - Quaniaive Modeling of Derivaive Securiies Soluion o Homework Three 1 a For > s, consider E[W W s F s = E [ W W s + W s W W s Fs We hen have = E [ W W s F s + Ws E [W W s F s = s, E[W F s =
More informationAdvanced Forecasting Techniques and Models: Time-Series Forecasts
Advanced Forecasing Techniques and Models: Time-Series Forecass Shor Examples Series using Risk Simulaor For more informaion please visi: www.realopionsvaluaion.com or conac us a: admin@realopionsvaluaion.com
More informationHedging Performance of Indonesia Exchange Rate
Hedging Performance of Indonesia Exchange Rae By: Eneng Nur Hasanah Fakulas Ekonomi dan Bisnis-Manajemen, Universias Islam Bandung (Unisba) E-mail: enengnurhasanah@gmail.com ABSTRACT The flucuaion of exchange
More informationUzawa(1961) s Steady-State Theorem in Malthusian Model
MPRA Munich Personal RePEc Archive Uzawa(1961) s Seady-Sae Theorem in Malhusian Model Defu Li and Jiuli Huang April 214 Online a hp://mpra.ub.uni-muenchen.de/55329/ MPRA Paper No. 55329, posed 16. April
More informationVolatility Spillovers between Stock Market Returns and Exchange Rate Changes: the New Zealand Case
Volailiy Spillovers beween Sock Marke eurns and Exchange ae Changes: he New Zealand Case Choi, D.F.S., V. Fang and T.Y. Fu Deparmen of Finance, Waikao Managemen School, Universiy of Waikao, Hamilon, New
More informationOn the Intraday Relation between the VIX and its Futures
On he Inraday Relaion beween he VIX and is Fuures Bar Frijns a, *, Alireza Tourani-Rad a and Rober I. Webb b a Deparmen of Finance, Auckland Universiy of Technology, Auckland, New Zealand b Universiy of
More informationAn Analytical Implementation of the Hull and White Model
Dwigh Gran * and Gauam Vora ** Revised: February 8, & November, Do no quoe. Commens welcome. * Douglas M. Brown Professor of Finance, Anderson School of Managemen, Universiy of New Mexico, Albuquerque,
More informationErratic Price, Smooth Dividend. Variance Bounds. Present Value. Ex Post Rational Price. Standard and Poor s Composite Stock-Price Index
Erraic Price, Smooh Dividend Shiller [1] argues ha he sock marke is inefficien: sock prices flucuae oo much. According o economic heory, he sock price should equal he presen value of expeced dividends.
More informationMonetary policy and multiple equilibria in a cash-in-advance economy
Economics Leers 74 (2002) 65 70 www.elsevier.com/ locae/ econbase Moneary policy and muliple equilibria in a cash-in-advance economy Qinglai Meng* The Chinese Universiy of Hong Kong, Deparmen of Economics,
More informationAsymmetric Stochastic Volatility in Nordic Stock Markets
EconWorld017@Rome Proceedings 5-7 January, 017; Rome, Ialy Asymmeric Sochasic Volailiy in Nordic Sock Markes Aycan Hepsağ 1 Absrac The goal of his paper is o invesigae he asymmeric impac of innovaions
More information