Taxes can have a considerable impact on portfolio

Size: px
Start display at page:

Download "Taxes can have a considerable impact on portfolio"

Transcription

1 Diversificatio i the Presece of Taxes There are substatial risks icurred with cocetrated holdigs. David M. Stei, drew F. Siegel, Premkumar Narasimha, ad Charles E. ppeadu DVID M. STEIN is the chief ivestmet officer ad maagig director at Parametric Portfolio ssociates i Seattle (W 98104). NDREW F. SIEGEL is the Grat I. utterbaugh professor of fiace ad maagemet sciece at the Uiversity of Washigto i Seattle (W 9811). PREMKUMR NRSIMHN is the director of research ad advaced techology at Parametric Portfolio ssociates i Seattle (W 98104). CHRLES E. PPEDU is a assistat professor of fiace at Georgia State Uiversity i tlata (G 30303). IT IS ILLEGL TO REPRODUCE THIS RTICLE IN NY FORMT Taxes ca have a cosiderable impact o portfolio value, ad ivestmet decisios should be made with a clear uderstadig of the tax-adjusted performace of the alteratives uder cosideratio. The impact of taxes has received icreased attetio i recet years with regard to portfolio performace (Stei [1998]); asset allocatio (Jacob [1995]); maager selectio (Jeffrey ad rott [1993]); ad tax efficiecy (Dickso ad Shove [1993]). Oe commo ad key problem that taxable ivestors face is diversifyig a low cost basis sigle-asset or cocetrated portfolio. I the tax-exempt case, moder portfolio theory is very clear o the beefits of diversificatio, ad there have evolved useful idustry stadard methods for addressig this, such as the meavariace approach to optimal portfolio diversificatio (Markowitz [1987]). I the presece of taxes, however, there are o stadard approaches for arrivig at a cosidered choice. Taxes complicate the aalysis because capital gais taxes are icurred at the time of diversificatio. The tax resultig from the sale of a portio of the iitial asset reduces the possibility of future returs, ad may or may ot outweigh ay ucertai future beefit from diversificatio. I proposig a approach to solvig the taxable ivestor s diversificatio dilemma, we cosider a very much simplified problem i which there are just two possible assets: the iitial holdigs ad a diversified bechmark portfolio. Our framework cosiders the ivestmet FLL 000 THE JOURNL OF PORTFOLIO MNGEMENT 61 Istitutioal Ivestor, Ic. ll rights reserved.

2 EXHIIT 1 FTER-TX HORIZON LIQUIDTION VLUE INITIL $1 CONCENTRTED SECURITY WITH VOLTILITY 4 Frequecy decisio with iitial taxes, ad shows how it ca be viewed as a equivalet but much simpler ivestmet decisio without iitial taxes. We do this by creatig a tax-deferred ivestor (with differet ivestmet opportuities) who does ot pay iitial capital gais taxes, but whose fial ivestmet performace is idetical to that of the actual ivestor. 1 Idetifyig the best diversificatio decisio available to the tax-deferred ivestor leads to a decisio for the actual ivestor. The tax-deferred ivestor is i a sese a tax-deferred equivalet of the actual ivestor, facig a equivalet but simpler problem. We preset results to the problem uder specific umerical assumptios, ad ivestigate the sesitivity of the results to the key parameters, amely, the risk of the iitial holdig, the ivestmet horizo, the cost basis, excess expected retur, ad riskless rate. I a example, we cosider diversifyig a portfolio so as to reduce trackig error risk usig a mea-variace optimizer. appedix provides techical details. RISKS ND ENEFITS OF DIVERSIFICTION: N EXMPLE We assume iitially a ivestor with a iitial high-risk holdig who ows $1 millio cocetrated i a risky stock with a zero cost basis. We compare the distributios of ucertai ed-of-horizo future values without ad with diversificatio, ad cotrast these with the much simpler compariso (of yearly expected retur ad risk) that may be used to decide the level of diversificatio for a tax-deferred ivestor % 15% 1 5% Most Likely Value $0.5 (Mode) Expected Fial Value $ Liquidatio Value ($) Expected Fial Value $4.5 Stadard Deviatio $13.0 Max. Likelihood $0.5 Probability of $1 or less 43% Probability of $ or less 6 First cosider the iitial udiversified holdig. Let us assume that the risk is 4, measurig risk as the aual stadard deviatio (volatility) of rate of retur, ad that the ivestmet horizo is 0 years. Let us further assume that the stock returs a expected 1 per year, of which 7% is price appreciatio ad 3% divided yield. Each year, divideds are taxed at 39.6%, ad the after-tax divided proceeds are reivested i the portfolio. We also assume that the ivestor liquidates the holdigs ad icurs capital gais taxes of at the horizo. The ivestor s fial wealth is ucertai, ad Exhibit 1 shows its distributio obtaied from a Mote Carlo simulatio i which the security prices follow a logormal process. While the ivestor ca expect $1 millio to grow o average to $4.5 millio after taxes, the distributio of fial wealth is uattractively broad. The mode (most likely value) of the distributio is oly $0.5 millio; the probability of edig up with less tha the iitial $1 millio is 43%; ad the probability of ot keepig up with iflatio is 6. Next, cosider the cosequeces of a decisio to diversify. Suppose that the ivestor liquidates the risky holdig, payig taxes at the rate, ad ivests the remaiig $800,000 i a diversified portfolio with a aual stadard deviatio of 15%, but with the same expected price ad divided returs as the risky stock. fter 0 years, tax is paid at the rate (reduced by a cost basis of $800,000). Exhibit shows the fial wealth distributio. The ivestor ca expect to have oly $3.8 millio, o average, after 0 years. While this expectatio is lower tha the value i Exhibit 1 because oly 8 of the iitial DIVERSIFICTION IN THE PRESENCE OF TXES FLL 000 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

3 EXHIIT HORIZON FTER-TX LIQUIDTION VLUE INITIL $0.80 DIVERSIFIED PORTFOLIO WITH VOLTILITY 15% Frequecy 3 5% 15% 1 5% EXHIIT 4 RISK OF SELECTED INVESTMENTS S&P % Exxo 16% GE 1% IM 3 Microsoft 35% Micro Techologies 7% OL 84% mazo.com 14% Risk is measured by aualized stadard deviatio. ased o Most Likely Value $.5 Expected Fial Value $3.8 Stadard Deviatio $.4 Expected Fial Value $3.8 Max. Likelihood $.5 Probability of $1 or less % Probability of $ or less 1% Liquidatio Value ($) EXHIIT 3 TRDE-OFF ETWEEN EXPECTED RETURN ND RISK IN SENCE OF TXES Expected Retur r f Maximum Sharpe ratio sset 1 Total Risk (std. dev.) sset value is available for compoudig, the probability distributio is oetheless more attractive. The mode icreases from $0.5 millio to $.5 millio; the chace of edig up with less tha the iitial $1 millio drops from 43% to just %; ad the chace of ot keepig up with iflatio falls from 6 to 1%. The graphs show that diversificatio boosts performace whe taxes are icurred eve though the asset expected returs are idetical. How ca this be? Diversificatio makes the expected retur more readily achievable. I Exhibit 1, with a high volatility, the expected retur is i the tail of the distributio ad is ot likely to be achieved; i Exhibit, with a lower volatility, the expected retur is more likely to be achieved. 3 Comparig Exhibits 1 ad, we ask whether the iitial tax is justified. How should the ivestor trade off risk (lesseed by diversificatio) agaist aticipated future wealth (also lesseed by iitial taxes)? I particular, how much of the iitial holdig should be diversified, ad by what priciple ca this be justified? Our approach provides a solutio that is both aalytical ad ituitive. The approach is based o the fact that the taxexempt diversificatio problem is simpler tha that faced by a ivestor who must pay iitial taxes i order to diversify. Exhibit 3 shows the well-kow tax-exempt tradeoff betwee risk ad retur. 4 The Sharpe ratio criterio is a commo oe for selectig a diversificatio level; the ivestor chooses the fractio of iitial asset (sset ) to sell (purchasig shares of sset 1) so that the resultig portfolio has the highest ratio of excess retur (above the risk-free rate r f ) to stadard deviatio of excess retur. FLL 000 THE JOURNL OF PORTFOLIO MNGEMENT 63 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

4 I order to place the 4 stadard deviatio example of Exhibit 1 i cotext, Exhibit 4 shows recet stadard deviatio risks of some well-kow stocks. The 4 volatility of Exhibit 1, while higher tha that of may large-cap securities, is ot particularly high for a techology or small compay. Our base bechmark volatility of 15% is similar to the volatility of the S&P 500 idex over a recet five-year period. N PPROCH TO THE PROLEM 64 We ow specify a aalytical framework for the opportuities ad choices of the actual ivestor ad the tax-deferred ivestor, ad idicate how to match them so as to have very early the same future cash flows. This matchig allows us to view the diversificatio decisio i the presece of taxes (faced by the actual ivestor) as a covetioal risk-retur trade-off without iitial tax complicatios (faced by the tax-deferred ivestor). We use the maximum Sharpe ratio as the decisio criterio for makig the optimal diversificatio choice of the taxdeferred ivestor. If this is the best choice for the taxdeferred ivestor, ad the actual ivestor s future cash flows closely match, it follows that we have also idetified a optimal choice for the actual ivestor. I the simplified problem, the ivestor holds a iitial portfolio,, with market value W 0, which may be sold i whole or i part to purchase shares i a fully diversified bechmark portfolio,. The ivestor s goal is to select the best fractio x (betwee 0 ad 1) of the iitial portfolio to be sold, ad ivest the after-tax proceeds i. The resultig positio is the held for the preset ivestmet horizo of years, durig which time ucertai rates of retur for the iitial asset ad the bechmark are observed ad compouded. t the ed of the ivestmet horizo, the positio is liquidated ad taxes are paid. This defies a probability distributio of fial after-tax values for the actual ivestor to which we will match a taxdeferred ivestor. 5 Imagie, ow, a tax-deferred ivestor who ca diversify without iitially payig capital gais taxes, but who pays taxes o liquidatio ad whose after-tax horizo ivestmet performace matches that of the actual ivestor very closely. The tax-deferred ivestor must face differet ivestmet opportuities; i particular, the taxdeferred assets must pay a lower expected rate of retur to compesate for taxes paid by the actual ivestor. The tax-deferred ivestor holds a iitial portfolio (with market value W 0 ), sells a fractio x, ad (without payig iitial taxes or chagig the cost basis) uses the proceeds to purchase shares of the bechmark,. The resultig portfolio is held for years, ad the positio is the liquidated ad taxes are paid. This defies a probability distributio of fial after-tax values for the tax-deferred ivestor. 6 Iputs to the model are as follows. The expected rates of retur for ad are µ ad µ. Their aual stadard deviatios of retur are σ ad σ, ad the beta of with respect to the bechmark is β. The horizo is fixed at years, after which the ivestmet positio is liquidated. The tax rate o log-term capital gais is τ ; there are o divideds; ad the risk-free rate is r f.. The mathematical formulatio ad its solutio are i the appedix, where we derive the tax-deferred ivestor s rate of retur µ x ad stadard deviatio σ x aalytically i terms of our iputs for each value of the diversificatio fractio x uder the assumptio of joit logormal asset returs. To match the fial cash flow distributios of the actual ad tax-deferred ivestors, we choose x to match diversificatio exposure, ad set the joit performace of ad to compesate for taxes paid by the actual ivestor. THE DIVERSIFICTION SOLUTION: EXMPLE We provide a example to study the actual ivestor s diversificatio decisio, as chose by applyig the maximum Sharpe [1964] ratio criterio to the taxdeferred ivestor. We the use sesitivity aalysis to show that greater diversificatio is associated with: greater iitial asset volatility, loger ivestmet horizo, higher cost basis, lower expected retur of the iitial asset, ad a lower risk-free rate. Less diversificatio is eeded whe the ivestor receives a step-up i basis at the horizo. s our base case, we set umerical values for the iitial asset ad the bechmark as follows: Expected returs: µ µ 1 Volatilities: σ 5%, µ 15% Horizo: 0 years Tax rate: τ o capital gais Risk-free rate: r f 6% Iitial cost basis C 0 0 Exhibit 5 shows the after-tax aual expected retur ad risk trade-off faced by the tax-deferred ivestor i a represetatio aalogous to Exhibit 3. I this case, DIVERSIFICTION IN THE PRESENCE OF TXES FLL 000 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

5 EXHIIT 5 CTUL INVESTOR S DIVERSIFICTION DECISION Expected Total Retur (tax-adjusted) 9% 8% 7% 6% Maximum Sharpe ratio: sell 86% of iitial portfolio Udiversified iitial portfolio Fully diversified : sell 10 of iitial portfolio 5% 1 15% 5% Total Risk (tax -adjusted) EXHIIT 6 SENSITIVITY TO INITIL STOCK VOLTILITY RISK-RETURN CURVES T DIFFERENT LEVELS OF VOLTILITY Mea Retur % 9. X 9% X 96% X 86% X 65% 8.5% 1 15% Total Risk Iitial volatility Iitial volatility 5% Iitial volatility 3 Iitial volatility 4 EXHIIT 6 SENSITIVITY TO INITIL STOCK VOLTILITY DIVERSIFICTION S FUNCTION OF VOLTILITY 10 Diversificatio Iitial Trackig Error the maximum Sharpe ratio criterio recommeds that 86% of the iitial holdig be sold. Greater diversificatio correspods to a lower expected rate of retur for the tax-deferred ivestor because it requires that higher taxes be paid iitially. The taxdeferred ivestor must ear a lower aual rate of retur i order to experiece the same ed-of-period performace as the actual ivestor. What ow is the sesitivity of this solutio to chages i the base umerical parameters? Of key importace is the risk of the iitial holdig, σ. Exhibit 6 shows the riskretur trade-off ad optimal diversificatio x for a rage of risk levels. The more risky the stock, the more it should be diversified. May securities, such as those with volatility of more tha 3, should be almost completely diversified. There is less eed to diversify low-volatility iitial assets. The horizo is also importat to the diversificatio decisio, as show i Exhibit 7. The loger the horizo, the more importat risk becomes, ad the more it should be diversified. For high-volatility iitial holdigs, the decisio is ot very sesitive to the horizo, ad we recommed diversifyig most of the asset. For low-volatility iitial holdigs, the horizo is more importat. While the cost basis C 0 is importat, too, its effect is straightforward. If the iitial asset has a cost basis higher tha zero, the diversificatio is cheaper. Thus, the diversificatio x icreases with the cost basis, as show i Exhibit 8. The relatioship betwee cost basis ad degree of diversificatio is close to liear. Whe the iitial asset has a higher expected retur, i.e., µ µ + α with α > 0, we would expect that the recommeded diversificatio x decrease with the excess retur α because a higher expected retur makes the iitial asset more valuable as compared to FLL 000 THE JOURNL OF PORTFOLIO MNGEMENT 65 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

6 EXHIIT 7 SENSITIVITY TO HORIZON DIVERSIFICTION S FUNCTION OF HORIZON 66 Diversificatio Iitial volatility 5% Iitial volatility 3 Iitial volatility Horizo (years) EXHIIT 8 SENSITIVITY TO COST SIS DIVERSIFICTION S FUNCTION OF COST SIS Diversificatio 10 95% 9 85% 8 75% 7 65% Iitial volatility 3 Iitial volatility Iitial volatility 5% Cost asis (as a percetage of iitial value, C 0 ) EXHIIT 9 SENSITIVITY TO EXCESS RETURN DIVERSIFICTION S FUNCTION OF EXCESS RETURN Diversificatio Iitial volatility Iitial volatility 4 Iitial volatility 3 Iitial volatility 5% -6% -4% -% % 4% 6% ual Excess Retur, α, (% per year) the bechmark. Exhibit 9 shows this. For example, if σ 5% ad α > 3.5% per year for 0 years, o diversificatio would be recommeded. If σ 3 ad α % per year for 0 years, the model would recommed diversifyig about 7 of the holdig. The effect of a lower risk-free rate r f is to icrease the level of diversificatio, as is clear from the covexity of the curve i Exhibit 5. We have assumed liquidatio ad the paymet of capital gais taxes at the horizo. If the ivestor receives a stepup i basis at this time, diversificatio is more costly; the ivestor ca avoid payig capital gais taxes by retaiig the sigle stock, but is taxed for diversifyig. It is iterestig to compare the model s recommeded level of diversificatio i the step-up case with the liquidatio case. s expected, for ay give set of parameters our model always suggests less diversificatio i the step-up case. Differeces are most proouced for short horizos, as show i Exhibit 10. The ituitio is simple; if the horizo is short, the risk i holdig the stock is relatively low, while the tax impact of sellig it is relatively high. We fid that for horizos loger tha 0 years, differeces are ot very great, ad the risk of holdig the sigle stock quickly overwhelms the tax beefit of retetio. Thus, over log ivestmet horizos, it is particularly uwise to icur the risk of cocetratio, eve i the step-up case. RELTED PROLEM: REDUCING TRCKING ERROR related problem cocers the holdig of a low cost basis iitial portfolio that is oly partially diversified, ad how it tracks a specified bechmark. The suitable measure of risk i this case is trackig error, rather tha total stadard deviatio. 7 DIVERSIFICTION IN THE PRESENCE OF TXES FLL 000 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

7 EXHIIT 10 SENSITIVITY TO HORIZON WITH ND WITHOUT COST SIS STEP-UP DIVERSIFICTION S FUNCTION OF HORIZON INITIL VOLTILITY σ 5% Diversificatio Without basis step-up With basis step-up Horizo (years) EXHIIT 11 EMPIRICL TRCKING ERROR VERSUS TX COST Tax Cost 8% 6% 4% % 1% % 3% 4% 5% 6% 7% 8% -% Trackig Error The ivestor reduces trackig error by sellig first the tax lots that realize few taxes but that also provide the best opportuity for diversificatio. I geeral, the tax cost icreases as the portfolio is squeezed dow to track the bechmark. Mea-variace optimizatio ca be used to miimize the tax cost ad to provide a tradeoff betwee tax cost ad trackig error. Exhibit 11 shows a empirical plot of the tax cost versus trackig error for a portfolio with a iitial trackig error of 6.8%. few iitial tax lots have urealized losses, ad these allow the trackig error of the portfolio to be reduced to 4.% before ay et taxes are realized. Thereafter, the tax cost icreases as trackig error decreases. I this example, which poit o the curve is best? Oce agai, by defiig a tax-deferred equivalet ivestor, we ca develop a solutio. I this case, the similar taxdeferred problem is that faced by a ivestor who is seekig a active portfolio maager, tradig off trackig error σ for excess retur α. Such a ivestor typically seeks either a iformatio ratio that is high eough (where the iformatio ratio is measured by the slope α/σ) or maximizatio of utility α λσ for some give λ, the ivestor s risk preferece. Our aalogous approach works for the tax maagemet decisio described here. The choice of λ is somewhat differet from that described, for example, by Griold ad Kah [1995] because whe we compare α values at differet diversificatio levels, the differeces are ot due to ucertai estimated portfolio performace, but istead come from kow iitial taxes paid. y reviewig the choices made by large umbers of ivestors, it would be possible to idetify values of λ that are implicitly revealed by their prefereces. SUMMRY ND CONCLUSIONS We have itroduced a framework for tradig off risk ad retur whe diversifyig low-basis taxable holdigs. I the case of a risky sigle asset, we aim to reduce the total risk (stadard deviatio) of the asset. I the case of a iitial portfolio that seeks to track a specified bechmark, we aim to reduce trackig error to a optimal level. I each case, we weigh the risk improvemet agaist its tax cost. Whe the iitial asset has substatially more risk tha the bechmark, our results recommed ear-complete diversificatio, despite a high iitial tax cost. If the iitial asset s total risk is ot much higher tha that of the bechmark, the approach recommeds less diversificatio, because the beefits do ot cover the margial tax cost. Sesitivity aalysis reveals that greater diversificatio is eeded: with greater iitial asset volatility, with loger ivestmet horizo, with a lower expected retur of the iitial asset, with a higher cost basis, ad with a lower risk-free rate. Less diversificatio is eeded whe the ivestor receives a step-up i basis at the horizo. Our approach has bee to formulate a particularly simple decisio problem. We have cosidered a sigle fixed-horizo ivestmet, with oly two possible extreme choices for portfolio formatio. The formula- FLL 000 THE JOURNL OF PORTFOLIO MNGEMENT 67 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

8 tio ca be geeralized i may pragmatically useful directios. Oe could: Iclude divided yields, which affects the aalysis because of the high rate of divided taxatio. Cosider how a ucertai horizo affects decisiomakig. Ivestors with large low-basis cocetrated holdigs are ofte reluctat to embrace our model s high diversificatio recommedatios. For such ivestors, other pragmatic extesios are iterestig. They might: Seek to compromise by stagig the diversificatio over time; exploit tax-maaged methods as i Stei ad Narasimha [1999] i maagig the diversified slice to reduce the tax burde. Istead of ivestig i a diversified bechmark idex, ivest the liquidated asset i a portfolio that will complete the remaiig udiversified holdigs. That is, seek a portfolio that will have low (or ideally egative) correlatio with the iitial holdigs. I practice, ivestors may also be able to obtai additioal flexibility with derivative securities, exchage fuds, or other ivestmet vehicles. While we have focused o a particular ad simplified aalytical problem, our solutio method ca be quite geerally applied to other portfolio decisios i the presece of taxes. I essece, our method traslates a taxable problem ito a tax-deferred equivalet problem based o aual mea ad stadard deviatio. Istead of maximizig the Sharpe ratio or trackig error utility, oe could choose the portfolio with maximum tax-deferred growth rate (please see edote 3 for a discussio o growth rate), or use ay other criterio for portfolio choice based o yearly mea ad stadard deviatio. Oe ca exted the cocept of a matched tax-deferred ivestor to provide aalytic ituitive solutios to a wide rage of more complex situatios. 68 PPENDIX TECHNICL DETILS We outlie here the assumptios we use i defiig the taxdeferred ivestor, ad we the idetify the future cash flows for both the taxable ad the tax-deferred ivestor. Usig these, we derive closed-form expressios for the tax-adjusted yearly expected rate of retur ad stadard deviatio of retur. Fially, we show how these expressios must be modified for the case i which the ivestor receives a step-up i basis at maturity. We assume that the actual ivestor iitially holds a portfolio with iitial market value W 0 ad iitial cost basis C 0 W 0 so that C 0 represets the iitial cost basis as a fractio betwee 0 ad 1. This portfolio is assumed to grow at a radom realized rate of retur i 1 i year i, so that the total horizo before-tax rate of retur over years is Π i1 i 1. The ivestor is cosiderig sellig a fractio x (betwee 0 ad 1) of the iitial portfolio ad payig taxes of τxw 0 (1 C x ) at rate τ o the proceeds xw 0 less the cost basis xc x W 0 o shares sold. (Note that this formulatio allows high cost basis shares to be chose for sale.) The after-tax proceeds xw 0 [1 τ(1 C x )] are used to purchase shares of a bechmark portfolio with radom realized rate of retur i 1 i year i. The resultig partially diversified portfolio ow has cost basis (C 0 xc x )W 0 i the iitial assets ad full cost basis xw 0 [1 τ(1 C x )] i the ewly purchased bechmark - portfolio. Whe the portfolio is liquidated after years, the ivestor receives the compouded amout (1 x)w0 i 1i + xw0[ 1 τ( 1 Cx) ] i 1i ad pays tax of [ ] + ( ) (-1) τw 0 (1 x) i 1i C0 xcx τxw0 1 τ 1 Cx i 1i 1 (-) resultig i a total after-tax compouded horizo rate of retur equal to r x (1 t)(1 x) i 1i + x(1 t) [ 1 t( 1 Cx )] i 1 + tc + xt(1 t)(1 C ) 1 i 0 x [ ]( ) (-3) I order to replicate the ivestmet performace of this actual ivestor, we seek to costruct a tax-deferred ivestor (who does ot pay tax iitially, but whose after-tax ed-of-horizo ivestmet performace is idetical to that of the actual ivestor) for each choice of x. Such a tax-deferred ivestor iitially holds a portfolio with the same iitial market value W 0 ad iitial cost basis C 0 W 0 as the actual ivestor, but that returs i 1 i year i. DIVERSIFICTION IN THE PRESENCE OF TXES FLL 000 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

9 The tax-deferred ivestor will sell a fractio x of the iitial portfolio give by (-4) where x is chose so that the risk-retur exposures of the actual ad tax-deferred ivestors are equal (that is, x is set equal to the ratio, for the actual ivestor, of the after-tax dollar amout i divided by total postdiversificatio portfolio value). The choice of x adjusts for the fact that the tax-deferred ivestor ca ivest the etire proceeds, x W 0, i the tax-deferred bechmark, which returs i 1 i year i. Note that the probability distributio of (, ) may deped o x. The tax-deferred ivestor retais the iitial cost basis of C 0 W 0. Whe the tax-deferred portfolio is liquidated after years, the tax-deferred ivestor receives the compouded amout ad pays tax at the ed of the time horizo i the amout of (-5) (-6) resultig i a total after-tax compouded horizo rate of retur equal to x i 1 i i 1 i r (1 τ)(1 x ) + (1 τ)x + τc 1 (-7) I the fully diversified case (x x 1), we have total rates of retur r ad r for the actual ad tax-deferred ivestors: r (1 τ) [ 1 τ( 1 C0) ] i 1 i + τ(1 τ) + τ C 1 0 [ ( x) ] ( x) x x1 τ 1 C / 1 τx1 C [ ] 0 i 1 i i 1 i W (1 x ) + x 0 [ i 1 i i 1 i 0 ] τ W (1 x ) Α + x Β C i 1 i r (1 τ) + τc 1 0 [ ] (-8) Ucertaity is specified as follows. The distributio of ( i, i ) is joit logormal, idepedet for differet years, with meas (µ, µ ), stadard deviatios (σ, σ ), ad istataeous beta β. Similarly, the distributio of ( i, i ) is joit logormal, idepedet for differet years, with meas (µ, µ ), stadard deviatios (σ, σ ), ad istataeous beta β (which may differ from β due to iitial taxes). To fid the joit distributio (specified by µ, µ, σ, σ, ad β ) for the tax-deferred ivestor, momet coditios are imposed i order to make the joit distributio of after-tax compouded horizo rates of retur (of the partially diversified portfolio ad the bechmark) early idetical for the actual ad the 0 tax-deferred ivestors at this particular value for x. That is, the joit probability distributio of (r x, r ) is closely matched to that of (r x, r ) usig the five momet coditios: E(r x ) E(r x ) E(r ) E(r ) σ rx σ r x σ r σ r Cov(r x, r ) Cov(r x, r ) (-9) (-10) (-11) The yearly expected rates of retur for the tax-deferred ivestor ca the be show to be give by µ (-1) (-13) Defie ν 1 + µ, θ σ + ν, ad similarly for,, ad to reduce the complexity of the equatios to follow, ad also defie δ ν ν b (θ /ν )β ad similarly δ ν ν b (θ /ν )β. The yearly stadard deviatios ad systematic risk β for the taxdeferred ivestor are give by θ σ θ 1 + µ δ {[ ] + } ( 0) + ( 0 ) µ 1 τ 1 C 1 µ τ 1 C 1 τ θ ττ ν τ [ 1 ( 1 C0) ] τ τc0 τ ν τc0 1 1 τ { ( + ( ) ( + ) + ( x) τ τ τx τ τ x x τ + ( ) 1 C 0 C 1 C + 0 τ C τ C + τ + 0 x ν x τ x ν τ { ( 1 ) + [ 1 ( 1 C )] } 1 + x 1 τ 1 C 1 τ 1 C θ ( 1 x) 1 τ 1 C δ [ ] [ ] + ( 1 x) 1+ µ x 1 τ 1 Cx 1 µ x 1 µ τx 1 C 1 x τc0 τc 1 x ν 1 x ν x θ 1 τ 1 τ [ ] x [ ( 0) ] + ( 0) [ + ( + ) ] [ ] 0 FLL 000 THE JOURNL OF PORTFOLIO MNGEMENT 69 )} ν 1 (-14) (-15) (-16) It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

10 β θ δ l ( 1+ µ ) 1+ µ σ µ l 1 + µ { ( 1 τc0 τx 1 C x 1 τ ( 1 x ) + ( ) τc0 τx 1 Cx 1 x ν x 1 τ 1 Cx ν 1 τ + ( ) + + ( ) + ( 1 x) θ + x 1 τ 1 C θ + x( 1 x) 1 τ 1 C δ σ θ 1 + µ (-17) (-18) (-19) The tax-adjusted yearly expected rate of retur ad stadard deviatio may ow be computed usig the values derived above: [ 1 + 1] µ + x µ µ x E 1 x x 1 x ( ) [ ] x + [ 1 + 1] + + ( ) ( ) + σx Var 1 x x 1 x θ x θ x 1 x δ 1 x ν x ν (-0) (-1) If the ivestor receives a step-up i basis at maturity, the the partially diversified ivestor keeps the compouded amout from Equatio (-1) without payig the tax of Equatio (-), resultig i a total after-tax compouded horizo rate of retur equal to rx ( 1 x) i 1i + x[ 1 τ( 1 Cx) ] i 1i 1 (-) i place of Equatio (-3). We keep the defiitio of x from Equatio (-4) uchaged. For the stepped-up tax-deferred ivestor, i place of Equatio (-7) we fid a total after-tax compouded horizo rate of retur equal to rx ( 1 x ) i 1 i + x i 1 i 1 70 [ ] [ ( x) ] 1 τc0 τc0 1 ν ν θ 1 δ 1 τ 1 τ + x x ( x ) x x [ ] )}/ (-3) I the fully diversified case (x x 1), we have total rates of retur r ad r for the stepped-up actual ad tax-deferred ivestors [i place of Equatio (-8)]: (-4) We use the same forms for the joit distributios of ( i, i ) ad ( i, i ) as before, ad use the same five momet coditios [Equatios (-9) through (-11)]. The yearly expected rates of retur for the stepped-up tax-deferred ivestor ca the be show to be give [i place of Equatios (-1) ad (-13)] by µ 1+ µ 1 τ 1 C 1 µ (-5) (-6) Usig defiitios of ν, θ, ad δ as before, the yearly stadard deviatios for the stepped-up tax-deferred ivestor are the give [i place of Equatios (-14) through (-19)] by θ θ 1 τ 1 C0 σ θ 1 + µ β r [ τ( C0) ] i 1 i r i 1 i 1 ( ) + ( ) + ( 1 x) 1+ µ x 1 τ 1 Cx 1 µ x 1 µ 1 x [ ] [ ] ( ) δ l ( 1+ µ ) 1+ µ σ µ l 1 + µ [ ( 0) ] [ ] + [ ( )] ( ) [ ] + ( ) (-7) (-8) x 1 τ 1 C τ θ θ τ δ x 1 1 C0 x ( 1 x) 1 1 C 0 δ 1 x (-9) 1 (-30) DIVERSIFICTION IN THE PRESENCE OF TXES FLL 000 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

11 θ θ τ θ C θ τ C δ δ ( 1 x) + x 1 ( 1 ) x x x( x) x x x (-31) 1 x σ θ 1 + µ (-3) With these modificatios, the tax-adjusted yearly expected rate of retur ad stadard deviatio i the case of stepped-up basis may ow be computed as before, usig Equatios (-0) ad (-1). ENDNOTES [ ] + t the time of this writig, Charles ppeadu was a portfolio maager at Parametric Portfolio ssociates. 1 We use the term ivestmet performace to refer to the aftertax ed-of-horizo cash flow probability distributio. t a iflatio rate of about 3.5% for 0 years, the iitial $1 millio value doubles to $ millio i 0 years. 3 This pheomeo is due to the fact that the log-term growth rate (see, e.g., Ferholz ad Shay [198]) is less tha the yearly expected rate of retur due to a risk pealty (equal to half the variace i the case of a logormal distributio). Some ituitio ito this paradox is provided by the simple example of gaiig or losig with probability oe-i-two. The expected rate of retur is zero, eve over the log ru. You would have to be very lucky ot to lose moey over the log ru, however, because the particular sequece gai, the lose reduces wealth by 4% [computed as (1 + 0.)(1 0.) 1] or about % each time the example is played (whe there are exactly equal umbers of ups ad dows). This % reductio is ideed half the variace sice 0. / %. Curiously, while the compouded expected rate of retur is equal to the expected compouded rate of retur, over the log ru this rate becomes early impossible to attai due to the risk pealty. 4 ruel [1998] emphasizes that taxable ivestors should use cautio whe usig traditioal efficiet frotier tools directly. 5 For simplicity, we assume o trasactio costs. This assumptio is reasoable whe trasactio costs are small compared to tax costs. 6 The aalysis may also be geeralized to the case of a ivestor who does ot liquidate, but who receives a step-up i cost basis at death. 7 Trackig error is the stadard deviatio of the aual differece betwee the retur of the portfolio ad that of the bechmark. [ ] REFERENCES ruel, J.L.P. Why Taxable Ivestors Should e Cautious Whe Usig Traditioal Frotier Tools. The Joural of Private Portfolio Maagemet, Witer 1998, pp Dickso, J.M., ad J.. Shove. Rakig Mutual Fuds o fter-tax asis. Ceter for Ecoomic Policy Research, Publicatio No. 344, Staford Uiversity, Ferholz, R., ad. Shay. Stochastic Portfolio Theory ad Stock Market Equilibrium. The Joural of Fiace, May 198, pp Griold, R.C., ad R.N. Kah. ctive Portfolio Maagemet Quatitative Theory ad pplicatios. Homewood, IL: Irwi, Jacob, N.L. Taxes, Ivestmet Strategy, ad Diversifyig Low asis Stock. Trusts ad Estates, May Jeffrey, R.H., ad R. rott. Is Your lpha ig Eough to Cover its Taxes? The Joural of Portfolio Maagemet, Sprig Markowitz, H.M. Mea Variace alysis i Portfolio Choice ad Capital Markets. Oxford: asil lackwell, Sharpe, W.F. Capital sset Prices: Theory of Market Equilibrium uder Coditios of Risk. The Joural of Fiace, September 1964, pp Stei, D.M. Measurig ad Evaluatig Portfolio Performace fter Taxes. The Joural of Portfolio Maagemet, Witer Stei, D.M., ad P. Narasimha. Of Passive ad ctive Equity Portfolios i the Presece of Taxes. The Joural of Private Portfolio Maagemet, Fall 1999, pp FLL 000 THE JOURNL OF PORTFOLIO MNGEMENT 71 It Is Illegal To Reproduce This rticle I y Format. ---> reprits@iijourals.com for reprits or permissio. Istitutioal Ivestor, Ic. ll rights reserved.

Models of Asset Pricing

Models of Asset Pricing APPENDIX 1 TO CHAPTER4 Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see

More information

Models of Asset Pricing

Models of Asset Pricing APPENDIX 1 TO CHAPTER 4 Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see

More information

Models of Asset Pricing

Models of Asset Pricing 4 Appedix 1 to Chapter Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see

More information

Subject CT1 Financial Mathematics Core Technical Syllabus

Subject CT1 Financial Mathematics Core Technical Syllabus Subject CT1 Fiacial Mathematics Core Techical Syllabus for the 2018 exams 1 Jue 2017 Subject CT1 Fiacial Mathematics Core Techical Aim The aim of the Fiacial Mathematics subject is to provide a groudig

More information

Appendix 1 to Chapter 5

Appendix 1 to Chapter 5 Appedix 1 to Chapter 5 Models of Asset Pricig I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy a asset, we are

More information

of Asset Pricing R e = expected return

of Asset Pricing R e = expected return Appedix 1 to Chapter 5 Models of Asset Pricig EXPECTED RETURN I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy

More information

of Asset Pricing APPENDIX 1 TO CHAPTER EXPECTED RETURN APPLICATION Expected Return

of Asset Pricing APPENDIX 1 TO CHAPTER EXPECTED RETURN APPLICATION Expected Return APPENDIX 1 TO CHAPTER 5 Models of Asset Pricig I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy a asset, we are

More information

Dr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory

Dr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory Dr Maddah ENMG 64 Fiacial Eg g I 03//06 Chapter 6 Mea-Variace Portfolio Theory Sigle Period Ivestmets Typically, i a ivestmet the iitial outlay of capital is kow but the retur is ucertai A sigle-period

More information

Chapter Four 1/15/2018. Learning Objectives. The Meaning of Interest Rates Future Value, Present Value, and Interest Rates Chapter 4, Part 1.

Chapter Four 1/15/2018. Learning Objectives. The Meaning of Interest Rates Future Value, Present Value, and Interest Rates Chapter 4, Part 1. Chapter Four The Meaig of Iterest Rates Future Value, Preset Value, ad Iterest Rates Chapter 4, Part 1 Preview Develop uderstadig of exactly what the phrase iterest rates meas. I this chapter, we see that

More information

CAPITAL ASSET PRICING MODEL

CAPITAL ASSET PRICING MODEL CAPITAL ASSET PRICING MODEL RETURN. Retur i respect of a observatio is give by the followig formula R = (P P 0 ) + D P 0 Where R = Retur from the ivestmet durig this period P 0 = Curret market price P

More information

CAPITAL PROJECT SCREENING AND SELECTION

CAPITAL PROJECT SCREENING AND SELECTION CAPITAL PROJECT SCREEIG AD SELECTIO Before studyig the three measures of ivestmet attractiveess, we will review a simple method that is commoly used to scree capital ivestmets. Oe of the primary cocers

More information

CHAPTER 2 PRICING OF BONDS

CHAPTER 2 PRICING OF BONDS CHAPTER 2 PRICING OF BONDS CHAPTER SUARY This chapter will focus o the time value of moey ad how to calculate the price of a bod. Whe pricig a bod it is ecessary to estimate the expected cash flows ad

More information

1 Random Variables and Key Statistics

1 Random Variables and Key Statistics Review of Statistics 1 Radom Variables ad Key Statistics Radom Variable: A radom variable is a variable that takes o differet umerical values from a sample space determied by chace (probability distributio,

More information

Binomial Model. Stock Price Dynamics. The Key Idea Riskless Hedge

Binomial Model. Stock Price Dynamics. The Key Idea Riskless Hedge Biomial Model Stock Price Dyamics The value of a optio at maturity depeds o the price of the uderlyig stock at maturity. The value of the optio today depeds o the expected value of the optio at maturity

More information

Chapter Four Learning Objectives Valuing Monetary Payments Now and in the Future

Chapter Four Learning Objectives Valuing Monetary Payments Now and in the Future Chapter Four Future Value, Preset Value, ad Iterest Rates Chapter 4 Learig Objectives Develop a uderstadig of 1. Time ad the value of paymets 2. Preset value versus future value 3. Nomial versus real iterest

More information

APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES

APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES Example: Brado s Problem Brado, who is ow sixtee, would like to be a poker champio some day. At the age of twety-oe, he would

More information

Statistics for Economics & Business

Statistics for Economics & Business Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie

More information

Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1

Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1 Chapter 8 Cofidece Iterval Estimatio Copyright 2015, 2012, 2009 Pearso Educatio, Ic. Chapter 8, Slide 1 Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for

More information

Chapter Six. Bond Prices 1/15/2018. Chapter 4, Part 2 Bonds, Bond Prices, Interest Rates and Holding Period Return.

Chapter Six. Bond Prices 1/15/2018. Chapter 4, Part 2 Bonds, Bond Prices, Interest Rates and Holding Period Return. Chapter Six Chapter 4, Part Bods, Bod Prices, Iterest Rates ad Holdig Period Retur Bod Prices 1. Zero-coupo or discout bod Promise a sigle paymet o a future date Example: Treasury bill. Coupo bod periodic

More information

We learned: $100 cash today is preferred over $100 a year from now

We learned: $100 cash today is preferred over $100 a year from now Recap from Last Week Time Value of Moey We leared: $ cash today is preferred over $ a year from ow there is time value of moey i the form of willigess of baks, busiesses, ad people to pay iterest for its

More information

r i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i

r i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i The iformatio required by the mea-variace approach is substatial whe the umber of assets is large; there are mea values, variaces, ad )/2 covariaces - a total of 2 + )/2 parameters. Sigle-factor model:

More information

Calculation of the Annual Equivalent Rate (AER)

Calculation of the Annual Equivalent Rate (AER) Appedix to Code of Coduct for the Advertisig of Iterest Bearig Accouts. (31/1/0) Calculatio of the Aual Equivalet Rate (AER) a) The most geeral case of the calculatio is the rate of iterest which, if applied

More information

The Time Value of Money in Financial Management

The Time Value of Money in Financial Management The Time Value of Moey i Fiacial Maagemet Muteau Irea Ovidius Uiversity of Costata irea.muteau@yahoo.com Bacula Mariaa Traia Theoretical High School, Costata baculamariaa@yahoo.com Abstract The Time Value

More information

1 Savings Plans and Investments

1 Savings Plans and Investments 4C Lesso Usig ad Uderstadig Mathematics 6 1 Savigs las ad Ivestmets 1.1 The Savigs la Formula Lets put a $100 ito a accout at the ed of the moth. At the ed of the moth for 5 more moths, you deposit $100

More information

Lecture 16 Investment, Time, and Risk (Basic issues in Finance)

Lecture 16 Investment, Time, and Risk (Basic issues in Finance) Lecture 16 Ivestmet, Time, ad Risk (Basic issues i Fiace) 1. Itertemporal Ivestmet Decisios: The Importace o Time ad Discoutig 1) Time as oe o the most importat actors aectig irm s ivestmet decisios: A

More information

KEY INFORMATION DOCUMENT CFD s Generic

KEY INFORMATION DOCUMENT CFD s Generic KEY INFORMATION DOCUMENT CFD s Geeric KEY INFORMATION DOCUMENT - CFDs Geeric Purpose This documet provides you with key iformatio about this ivestmet product. It is ot marketig material ad it does ot costitute

More information

Correlation possibly the most important and least understood topic in finance

Correlation possibly the most important and least understood topic in finance Correlatio...... possibly the most importat ad least uderstood topic i fiace 2014 Gary R. Evas. May be used oly for o-profit educatioal purposes oly without permissio of the author. The first exam... Eco

More information

Quarterly Update First Quarter 2018

Quarterly Update First Quarter 2018 EDWARD JONES ADVISORY SOLUTIONS Quarterly Update First Quarter 2018 www.edwardjoes.com Member SIPC Key Steps to Fiacial Success We Use a Established Process 5 HOW CAN I STAY ON TRACK? 4 HOW DO I GET THERE?

More information

1 The Power of Compounding

1 The Power of Compounding 1 The Power of Compoudig 1.1 Simple vs Compoud Iterest You deposit $1,000 i a bak that pays 5% iterest each year. At the ed of the year you will have eared $50. The bak seds you a check for $50 dollars.

More information

Linear Programming for Portfolio Selection Based on Fuzzy Decision-Making Theory

Linear Programming for Portfolio Selection Based on Fuzzy Decision-Making Theory The Teth Iteratioal Symposium o Operatios Research ad Its Applicatios (ISORA 2011 Duhuag, Chia, August 28 31, 2011 Copyright 2011 ORSC & APORC, pp. 195 202 Liear Programmig for Portfolio Selectio Based

More information

Prospect theory and fat tails

Prospect theory and fat tails Risk ad Decisio Aalysis 1 (2009) 187 195 187 DOI 10.3233/RDA-2009-0016 IOS Press Prospect theory ad fat tails Philip Maymi Polytechic Istitute of New York Uiversity, New York, NY, USA E-mail: phil@maymi.com

More information

Institute of Actuaries of India Subject CT5 General Insurance, Life and Health Contingencies

Institute of Actuaries of India Subject CT5 General Insurance, Life and Health Contingencies Istitute of Actuaries of Idia Subject CT5 Geeral Isurace, Life ad Health Cotigecies For 2017 Examiatios Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical techiques which

More information

REITInsight. In this month s REIT Insight:

REITInsight. In this month s REIT Insight: REITIsight Newsletter February 2014 REIT Isight is a mothly market commetary by Resource Real Estate's Global Portfolio Maager, Scott Crowe. It discusses our perspectives o major evets ad treds i real

More information

Proceedings of the 5th WSEAS Int. Conf. on SIMULATION, MODELING AND OPTIMIZATION, Corfu, Greece, August 17-19, 2005 (pp )

Proceedings of the 5th WSEAS Int. Conf. on SIMULATION, MODELING AND OPTIMIZATION, Corfu, Greece, August 17-19, 2005 (pp ) Proceedigs of the 5th WSEAS It. Cof. o SIMULATION, MODELING AND OPTIMIZATION, Corfu, Greece, August 7-9, 005 (pp488-49 Realized volatility estimatio: ew simulatio approach ad empirical study results JULIA

More information

Overlapping Generations

Overlapping Generations Eco. 53a all 996 C. Sims. troductio Overlappig Geeratios We wat to study how asset markets allow idividuals, motivated by the eed to provide icome for their retiremet years, to fiace capital accumulatio

More information

ENGINEERING ECONOMICS

ENGINEERING ECONOMICS ENGINEERING ECONOMICS Ref. Grat, Ireso & Leaveworth, "Priciples of Egieerig Ecoomy'','- Roald Press, 6th ed., New York, 1976. INTRODUCTION Choice Amogst Alteratives 1) Why do it at all? 2) Why do it ow?

More information

Monetary Economics: Problem Set #5 Solutions

Monetary Economics: Problem Set #5 Solutions Moetary Ecoomics oblem Set #5 Moetary Ecoomics: oblem Set #5 Solutios This problem set is marked out of 1 poits. The weight give to each part is idicated below. Please cotact me asap if you have ay questios.

More information

Anomaly Correction by Optimal Trading Frequency

Anomaly Correction by Optimal Trading Frequency Aomaly Correctio by Optimal Tradig Frequecy Yiqiao Yi Columbia Uiversity September 9, 206 Abstract Uder the assumptio that security prices follow radom walk, we look at price versus differet movig averages.

More information

Chapter 4: Time Value of Money

Chapter 4: Time Value of Money FIN 301 Class Notes Chapter 4: Time Value of Moey The cocept of Time Value of Moey: A amout of moey received today is worth more tha the same dollar value received a year from ow. Why? Do you prefer a

More information

Estimating Proportions with Confidence

Estimating Proportions with Confidence Aoucemets: Discussio today is review for midterm, o credit. You may atted more tha oe discussio sectio. Brig sheets of otes ad calculator to midterm. We will provide Scatro form. Homework: (Due Wed Chapter

More information

First determine the payments under the payment system

First determine the payments under the payment system Corporate Fiace February 5, 2008 Problem Set # -- ANSWERS Klick. You wi a judgmet agaist a defedat worth $20,000,000. Uder state law, the defedat has the right to pay such a judgmet out over a 20 year

More information

Subject CT5 Contingencies Core Technical. Syllabus. for the 2011 Examinations. The Faculty of Actuaries and Institute of Actuaries.

Subject CT5 Contingencies Core Technical. Syllabus. for the 2011 Examinations. The Faculty of Actuaries and Institute of Actuaries. Subject CT5 Cotigecies Core Techical Syllabus for the 2011 Examiatios 1 Jue 2010 The Faculty of Actuaries ad Istitute of Actuaries Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical

More information

Introduction to Financial Derivatives

Introduction to Financial Derivatives 550.444 Itroductio to Fiacial Derivatives Determiig Prices for Forwards ad Futures Week of October 1, 01 Where we are Last week: Itroductio to Iterest Rates, Future Value, Preset Value ad FRAs (Chapter

More information

43. A 000 par value 5-year bod with 8.0% semiaual coupos was bought to yield 7.5% covertible semiaually. Determie the amout of premium amortized i the 6 th coupo paymet. (A).00 (B).08 (C).5 (D).5 (E).34

More information

Chapter 11 Appendices: Review of Topics from Foundations in Finance and Tables

Chapter 11 Appendices: Review of Topics from Foundations in Finance and Tables Chapter 11 Appedices: Review of Topics from Foudatios i Fiace ad Tables A: INTRODUCTION The expressio Time is moey certaily applies i fiace. People ad istitutios are impatiet; they wat moey ow ad are geerally

More information

living well in retirement Adjusting Your Annuity Income Your Payment Flexibilities

living well in retirement Adjusting Your Annuity Income Your Payment Flexibilities livig well i retiremet Adjustig Your Auity Icome Your Paymet Flexibilities what s iside 2 TIAA Traditioal auity Icome 4 TIAA ad CREF Variable Auity Icome 7 Choices for Adjustig Your Auity Icome 7 Auity

More information

This paper provides a new portfolio selection rule. The objective is to minimize the

This paper provides a new portfolio selection rule. The objective is to minimize the Portfolio Optimizatio Uder a Miimax Rule Xiaoiag Cai Kok-Lay Teo Xiaoi Yag Xu Yu Zhou Departmet of Systems Egieerig ad Egieerig Maagemet, The Chiese Uiversity of Hog Kog, Shati, NT, Hog Kog Departmet of

More information

The Valuation of the Catastrophe Equity Puts with Jump Risks

The Valuation of the Catastrophe Equity Puts with Jump Risks The Valuatio of the Catastrophe Equity Puts with Jump Risks Shih-Kuei Li Natioal Uiversity of Kaohsiug Joit work with Chia-Chie Chag Outlie Catastrophe Isurace Products Literatures ad Motivatios Jump Risk

More information

ad covexity Defie Macaulay duratio D Mod = r 1 = ( CF i i k (1 + r k) i ) (1.) (1 + r k) C = ( r ) = 1 ( CF i i(i + 1) (1 + r k) i+ k ) ( ( i k ) CF i

ad covexity Defie Macaulay duratio D Mod = r 1 = ( CF i i k (1 + r k) i ) (1.) (1 + r k) C = ( r ) = 1 ( CF i i(i + 1) (1 + r k) i+ k ) ( ( i k ) CF i Fixed Icome Basics Cotets Duratio ad Covexity Bod Duratios ar Rate, Spot Rate, ad Forward Rate Flat Forward Iterpolatio Forward rice/yield, Carry, Roll-Dow Example Duratio ad Covexity For a series of cash

More information

5. Best Unbiased Estimators

5. Best Unbiased Estimators Best Ubiased Estimators http://www.math.uah.edu/stat/poit/ubiased.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 5. Best Ubiased Estimators Basic Theory Cosider agai

More information

Using Math to Understand Our World Project 5 Building Up Savings And Debt

Using Math to Understand Our World Project 5 Building Up Savings And Debt Usig Math to Uderstad Our World Project 5 Buildig Up Savigs Ad Debt Note: You will have to had i aswers to all umbered questios i the Project Descriptio See the What to Had I sheet for additioal materials

More information

STRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans

STRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans CMM Subject Support Strad: FINANCE Uit 3 Loas ad Mortgages: Text m e p STRAND: FINANCE Uit 3 Loas ad Mortgages TEXT Cotets Sectio 3.1 Aual Percetage Rate (APR) 3.2 APR for Repaymet of Loas 3.3 Credit Purchases

More information

Success through excellence!

Success through excellence! IIPC Cosultig AG IRR Attributio Date: November 2011 Date: November 2011 - Slide 1 Ageda Itroductio Calculatio of IRR Cotributio to IRR IRR attributio Hypothetical example Simple example for a IRR implemetatio

More information

1 + r. k=1. (1 + r) k = A r 1

1 + r. k=1. (1 + r) k = A r 1 Perpetual auity pays a fixed sum periodically forever. Suppose a amout A is paid at the ed of each period, ad suppose the per-period iterest rate is r. The the preset value of the perpetual auity is A

More information

AY Term 2 Mock Examination

AY Term 2 Mock Examination AY 206-7 Term 2 Mock Examiatio Date / Start Time Course Group Istructor 24 March 207 / 2 PM to 3:00 PM QF302 Ivestmet ad Fiacial Data Aalysis G Christopher Tig INSTRUCTIONS TO STUDENTS. This mock examiatio

More information

Parametric Density Estimation: Maximum Likelihood Estimation

Parametric Density Estimation: Maximum Likelihood Estimation Parametric Desity stimatio: Maimum Likelihood stimatio C6 Today Itroductio to desity estimatio Maimum Likelihood stimatio Itroducto Bayesia Decisio Theory i previous lectures tells us how to desig a optimal

More information

Section 3.3 Exercises Part A Simplify the following. 1. (3m 2 ) 5 2. x 7 x 11

Section 3.3 Exercises Part A Simplify the following. 1. (3m 2 ) 5 2. x 7 x 11 123 Sectio 3.3 Exercises Part A Simplify the followig. 1. (3m 2 ) 5 2. x 7 x 11 3. f 12 4. t 8 t 5 f 5 5. 3-4 6. 3x 7 4x 7. 3z 5 12z 3 8. 17 0 9. (g 8 ) -2 10. 14d 3 21d 7 11. (2m 2 5 g 8 ) 7 12. 5x 2

More information

point estimator a random variable (like P or X) whose values are used to estimate a population parameter

point estimator a random variable (like P or X) whose values are used to estimate a population parameter Estimatio We have oted that the pollig problem which attempts to estimate the proportio p of Successes i some populatio ad the measuremet problem which attempts to estimate the mea value µ of some quatity

More information

5 Statistical Inference

5 Statistical Inference 5 Statistical Iferece 5.1 Trasitio from Probability Theory to Statistical Iferece 1. We have ow more or less fiished the probability sectio of the course - we ow tur attetio to statistical iferece. I statistical

More information

CreditRisk + Download document from CSFB web site:

CreditRisk + Download document from CSFB web site: CreditRis + Dowload documet from CSFB web site: http://www.csfb.com/creditris/ Features of CreditRis+ pplies a actuarial sciece framewor to the derivatio of the loss distributio of a bod/loa portfolio.

More information

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory Olie appedices from Couterparty Risk ad Credit Value Adjustmet a APPENDIX 8A: Formulas for EE, PFE ad EPE for a ormal distributio Cosider a ormal distributio with mea (expected future value) ad stadard

More information

An Empirical Study of the Behaviour of the Sample Kurtosis in Samples from Symmetric Stable Distributions

An Empirical Study of the Behaviour of the Sample Kurtosis in Samples from Symmetric Stable Distributions A Empirical Study of the Behaviour of the Sample Kurtosis i Samples from Symmetric Stable Distributios J. Marti va Zyl Departmet of Actuarial Sciece ad Mathematical Statistics, Uiversity of the Free State,

More information

Today: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3)

Today: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3) Today: Fiish Chapter 9 (Sectios 9.6 to 9.8 ad 9.9 Lesso 3) ANNOUNCEMENTS: Quiz #7 begis after class today, eds Moday at 3pm. Quiz #8 will begi ext Friday ad ed at 10am Moday (day of fial). There will be

More information

14.30 Introduction to Statistical Methods in Economics Spring 2009

14.30 Introduction to Statistical Methods in Economics Spring 2009 MIT OpeCourseWare http://ocwmitedu 430 Itroductio to Statistical Methods i Ecoomics Sprig 009 For iformatio about citig these materials or our Terms of Use, visit: http://ocwmitedu/terms 430 Itroductio

More information

FINM6900 Finance Theory How Is Asymmetric Information Reflected in Asset Prices?

FINM6900 Finance Theory How Is Asymmetric Information Reflected in Asset Prices? FINM6900 Fiace Theory How Is Asymmetric Iformatio Reflected i Asset Prices? February 3, 2012 Referece S. Grossma, O the Efficiecy of Competitive Stock Markets where Traders Have Diverse iformatio, Joural

More information

18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013

18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013 18.S096 Problem Set 5 Fall 2013 Volatility Modelig Due Date: 10/29/2013 1. Sample Estimators of Diffusio Process Volatility ad Drift Let {X t } be the price of a fiacial security that follows a geometric

More information

Maximum Empirical Likelihood Estimation (MELE)

Maximum Empirical Likelihood Estimation (MELE) Maximum Empirical Likelihood Estimatio (MELE Natha Smooha Abstract Estimatio of Stadard Liear Model - Maximum Empirical Likelihood Estimator: Combiatio of the idea of imum likelihood method of momets,

More information

Problem Set 1a - Oligopoly

Problem Set 1a - Oligopoly Advaced Idustrial Ecoomics Sprig 2014 Joha Steek 6 may 2014 Problem Set 1a - Oligopoly 1 Table of Cotets 2 Price Competitio... 3 2.1 Courot Oligopoly with Homogeous Goods ad Differet Costs... 3 2.2 Bertrad

More information

Driver s. 1st Gear: Determine your asset allocation strategy.

Driver s. 1st Gear: Determine your asset allocation strategy. Delaware North 401(k) PLAN The Driver s Guide The fial step o your road to erollig i the Delaware North 401(k) Pla. At this poit, you re ready to take the wheel ad set your 401(k) i motio. Now all that

More information

Optimizing of the Investment Structure of the Telecommunication Sector Company

Optimizing of the Investment Structure of the Telecommunication Sector Company Iteratioal Joural of Ecoomics ad Busiess Admiistratio Vol. 1, No. 2, 2015, pp. 59-70 http://www.aisciece.org/joural/ijeba Optimizig of the Ivestmet Structure of the Telecommuicatio Sector Compay P. N.

More information

Exam 2. Instructor: Cynthia Rudin TA: Dimitrios Bisias. October 25, 2011

Exam 2. Instructor: Cynthia Rudin TA: Dimitrios Bisias. October 25, 2011 15.075 Exam 2 Istructor: Cythia Rudi TA: Dimitrios Bisias October 25, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 You are i charge of a study

More information

for a secure Retirement Foundation Gold (ICC11 IDX3)* *Form number and availability may vary by state.

for a secure Retirement Foundation Gold (ICC11 IDX3)* *Form number and availability may vary by state. for a secure Retiremet Foudatio Gold (ICC11 IDX3)* *Form umber ad availability may vary by state. Where Will Your Retiremet Dollars Take You? RETIREMENT PROTECTION ASSURING YOUR LIFESTYLE As Americas,

More information

The ROI of Ellie Mae s Encompass All-In-One Mortgage Management Solution

The ROI of Ellie Mae s Encompass All-In-One Mortgage Management Solution The ROI of Ellie Mae s Ecompass All-I-Oe Mortgage Maagemet Solutio MAY 2017 Legal Disclaimer All iformatio cotaied withi this study is for iformatioal purposes oly. Neither Ellie Mae, Ic. or MarketWise

More information

0.1 Valuation Formula:

0.1 Valuation Formula: 0. Valuatio Formula: 0.. Case of Geeral Trees: q = er S S S 3 S q = er S S 4 S 5 S 4 q 3 = er S 3 S 6 S 7 S 6 Therefore, f (3) = e r [q 3 f (7) + ( q 3 ) f (6)] f () = e r [q f (5) + ( q ) f (4)] = f ()

More information

NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE)

NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE) NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE) READ THE INSTRUCTIONS VERY CAREFULLY 1) Time duratio is 2 hours

More information

A Bayesian perspective on estimating mean, variance, and standard-deviation from data

A Bayesian perspective on estimating mean, variance, and standard-deviation from data Brigham Youg Uiversity BYU ScholarsArchive All Faculty Publicatios 006--05 A Bayesia perspective o estimatig mea, variace, ad stadard-deviatio from data Travis E. Oliphat Follow this ad additioal works

More information

Decision Science Letters

Decision Science Letters Decisio Sciece Letters 3 (214) 35 318 Cotets lists available at GrowigSciece Decisio Sciece Letters homepage: www.growigsciece.com/dsl Possibility theory for multiobective fuzzy radom portfolio optimizatio

More information

A random variable is a variable whose value is a numerical outcome of a random phenomenon.

A random variable is a variable whose value is a numerical outcome of a random phenomenon. The Practice of Statistics, d ed ates, Moore, ad Stares Itroductio We are ofte more iterested i the umber of times a give outcome ca occur tha i the possible outcomes themselves For example, if we toss

More information

Online appendices from The xva Challenge by Jon Gregory. APPENDIX 10A: Exposure and swaption analogy.

Online appendices from The xva Challenge by Jon Gregory. APPENDIX 10A: Exposure and swaption analogy. APPENDIX 10A: Exposure ad swaptio aalogy. Sorese ad Bollier (1994), effectively calculate the CVA of a swap positio ad show this ca be writte as: CVA swap = LGD V swaptio (t; t i, T) PD(t i 1, t i ). i=1

More information

2013/4/9. Topics Covered. Principles of Corporate Finance. Time Value of Money. Time Value of Money. Future Value

2013/4/9. Topics Covered. Principles of Corporate Finance. Time Value of Money. Time Value of Money. Future Value 3/4/9 Priciples of orporate Fiace By Zhag Xiaorog : How to alculate s Topics overed ad Future Value Net NPV Rule ad IRR Rule Opportuity ost of apital Valuig Log-Lived Assets PV alculatio Short uts ompoud

More information

Management fee structure of the (public) Italian real estate funds. Le strutture commissionali dei fondi immobiliari quotati in Italia.

Management fee structure of the (public) Italian real estate funds. Le strutture commissionali dei fondi immobiliari quotati in Italia. Maagemet fee structure of the (public) Italia real estate fuds. Le strutture commissioali dei fodi immobiliari quotati i Italia. Massimo Biasi Uiversity of Macerata ad atholic Uiversity of Mila massimo.biasi@uimc.it

More information

Financial Analysis. Lecture 4 (4/12/2017)

Financial Analysis. Lecture 4 (4/12/2017) Fiacial Aalysis Lecture 4 (4/12/217) Fiacial Aalysis Evaluates maagemet alteratives based o fiacial profitability; Evaluates the opportuity costs of alteratives; Cash flows of costs ad reveues; The timig

More information

BUSINESS PLAN IMMUNE TO RISKY SITUATIONS

BUSINESS PLAN IMMUNE TO RISKY SITUATIONS BUSINESS PLAN IMMUNE TO RISKY SITUATIONS JOANNA STARCZEWSKA, ADVISORY BUSINESS SOLUTIONS MANAGER RISK CENTER OF EXCELLENCE EMEA/AP ATHENS, 13TH OF MARCH 2015 FINANCE CHALLENGES OF MANY FINANCIAL DEPARTMENTS

More information

TIME VALUE OF MONEY 6.1 TIME VALUE OF MONEY

TIME VALUE OF MONEY 6.1 TIME VALUE OF MONEY C h a p t e r TIME VALUE O MONEY 6. TIME VALUE O MONEY The idividual s preferece for possessio of give amout of cash ow, rather tha the same amout at some future time, is called Time preferece for moey.

More information

MATH : EXAM 2 REVIEW. A = P 1 + AP R ) ny

MATH : EXAM 2 REVIEW. A = P 1 + AP R ) ny MATH 1030-008: EXAM 2 REVIEW Origially, I was havig you all memorize the basic compoud iterest formula. I ow wat you to memorize the geeral compoud iterest formula. This formula, whe = 1, is the same as

More information

Portfolio Optimization

Portfolio Optimization 13 Portfolio Optimizatio 13.1 Itroductio Portfolio models are cocered with ivestmet where there are typically two criteria: expected retur ad risk. The ivestor wats the former to be high ad the latter

More information

ACTUARIAL RESEARCH CLEARING HOUSE 1990 VOL. 2 INTEREST, AMORTIZATION AND SIMPLICITY. by Thomas M. Zavist, A.S.A.

ACTUARIAL RESEARCH CLEARING HOUSE 1990 VOL. 2 INTEREST, AMORTIZATION AND SIMPLICITY. by Thomas M. Zavist, A.S.A. ACTUARIAL RESEARCH CLEARING HOUSE 1990 VOL. INTEREST, AMORTIZATION AND SIMPLICITY by Thomas M. Zavist, A.S.A. 37 Iterest m Amortizatio ad Simplicity Cosider simple iterest for a momet. Suppose you have

More information

MS-E2114 Investment Science Exercise 2/2016, Solutions

MS-E2114 Investment Science Exercise 2/2016, Solutions MS-E24 Ivestmet Sciece Exercise 2/206, Solutios 26.2.205 Perpetual auity pays a xed sum periodically forever. Suppose a amout A is paid at the ed of each period, ad suppose the per-period iterest rate

More information

III. RESEARCH METHODS. Riau Province becomes the main area in this research on the role of pulp

III. RESEARCH METHODS. Riau Province becomes the main area in this research on the role of pulp III. RESEARCH METHODS 3.1 Research Locatio Riau Provice becomes the mai area i this research o the role of pulp ad paper idustry. The decisio o Riau Provice was supported by several facts: 1. The largest

More information

Global. Portfolio Analysis. Beating Benchmarks. A Stockpicker s Reality: Part II. Global. November 30, 1999

Global. Portfolio Analysis. Beating Benchmarks. A Stockpicker s Reality: Part II. Global. November 30, 1999 Global Global Portfolio Aalysis November 3, 999 Aalysts Steve Strogi steve.strogi@gs.com (New York) - 357-476 Melaie Petsch melaie.petsch@gs.com (New York) - 357-69 Greg Shareow greg.shareow@gs.com (New

More information

0.07. i PV Qa Q Q i n. Chapter 3, Section 2

0.07. i PV Qa Q Q i n. Chapter 3, Section 2 Chapter 3, Sectio 2 1. (S13HW) Calculate the preset value for a auity that pays 500 at the ed of each year for 20 years. You are give that the aual iterest rate is 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01

More information

Structuring the Selling Employee/ Shareholder Transition Period Payments after a Closely Held Company Acquisition

Structuring the Selling Employee/ Shareholder Transition Period Payments after a Closely Held Company Acquisition Icome Tax Isights Structurig the Sellig Employee/ Shareholder Trasitio Period Paymets after a Closely Held Compay Acquisitio Robert F. Reilly, CPA Corporate acquirers ofte acquire closely held target compaies.

More information

Productivity depending risk minimization of production activities

Productivity depending risk minimization of production activities Productivity depedig risk miimizatio of productio activities GEORGETTE KANARACHOU, VRASIDAS LEOPOULOS Productio Egieerig Sectio Natioal Techical Uiversity of Athes, Polytechioupolis Zografou, 15780 Athes

More information

1031 Tax-Deferred Exchanges

1031 Tax-Deferred Exchanges 1031 Tax-Deferred Exchages About the Authors Arold M. Brow Seior Maagig Director, Head of 1031 Tax-Deferred Exchage Services, MB Fiacial Deferred Exchage Corporatio Arold M. Brow is the Seior Maagig Director

More information

Introduction to Probability and Statistics Chapter 7

Introduction to Probability and Statistics Chapter 7 Itroductio to Probability ad Statistics Chapter 7 Ammar M. Sarha, asarha@mathstat.dal.ca Departmet of Mathematics ad Statistics, Dalhousie Uiversity Fall Semester 008 Chapter 7 Statistical Itervals Based

More information

Standard Deviations for Normal Sampling Distributions are: For proportions For means _

Standard Deviations for Normal Sampling Distributions are: For proportions For means _ Sectio 9.2 Cofidece Itervals for Proportios We will lear to use a sample to say somethig about the world at large. This process (statistical iferece) is based o our uderstadig of samplig models, ad will

More information

Faculdade de Economia da Universidade de Coimbra

Faculdade de Economia da Universidade de Coimbra Faculdade de Ecoomia da Uiversidade de Coimbra Grupo de Estudos Moetários e Fiaceiros (GEMF) Av. Dias da Silva, 65 300-5 COIMBRA, PORTUGAL gemf@fe.uc.pt http://www.uc.pt/feuc/gemf PEDRO GODINHO Estimatig

More information

Class Sessions 2, 3, and 4: The Time Value of Money

Class Sessions 2, 3, and 4: The Time Value of Money Class Sessios 2, 3, ad 4: The Time Value of Moey Associated Readig: Text Chapter 3 ad your calculator s maual. Summary Moey is a promise by a Bak to pay to the Bearer o demad a sum of well, moey! Oe risk

More information

This article is part of a series providing

This article is part of a series providing feature Bryce Millard ad Adrew Machi Characteristics of public sector workers SUMMARY This article presets aalysis of public sector employmet, ad makes comparisos with the private sector, usig data from

More information

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Meas ad Proportios Itroductio: I this chapter we wat to fid out the value of a parameter for a populatio. We do t kow the value of this parameter for the etire

More information