Asset Allocation and International Investments Edited by Greg N Gregoriou ASSET ALLOCATION AND INTERNATIONAL INVESTMENTS Also edited by Greg N Gregoriou ADVANCES IN RISK MANAGEMENT DIVERSIFICATION AND PORTFOLIO MANAGEMENT OF MUTUAL FUNDS PERFORMANCE OF MUTUAL FUNDS Asset Allocation and International Investments Edited by GREG N GREGORIOU Selection and editorial matter © Greg N Gregoriou 2007 Individual chapters © contributors 2007 All rights reserved No reproduction, copy or transmission of this publication may be made without written permission No paragraph of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1T 4LP Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages The authors have asserted their rights to be identified as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988 First published 2007 by PALGRAVE MACMILLAN Houndmills, Basingstoke, Hampshire RG21 6XS and 175 Fifth Avenue, New York, N.Y 10010 Companies and representatives throughout the world PALGRAVE MACMILLAN is the global academic imprint of the Palgrave Macmillan division of St Martin’s Press, LLC and of Palgrave Macmillan Ltd Macmillan® is a registered trademark in the United States, United Kingdom and other countries Palgrave is a registered trademark in the European Union and other countries ISBN-13: 978–0–230–01917–1 ISBN-10: 0–230–01917–X This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Asset allocation and international investments / edited by Gerg N Gregoriou p.cm — (Finance and capital markets) Includes bibliographical references and index ISBN 0–230–01917–X Asset allocation Investments, Foreign Globalization—Economic aspects Portfolio management I Gregoriou, Greg N., 1956– II Series HG4529.5.A83 2006 332.67’3—dc22 2006045369 10 16 15 14 13 12 11 10 09 08 07 Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham and Eastbourne To my mother Evangelia and in loving memory of my father Nicholas This page intentionally left blank Contents Acknowledgments xi Notes on the Contributors xii Introduction xvii Time-Varying Downside Risk: An Application to the Art Market Rachel Campbell and Roman Kräussl 1.1 Introduction 1.2 Art as an investment 1.3 Previous empirical studies 1.4 Empirical analysis 1.5 Data 1.6 Methodology 1.7 Results 1.8 Discussion 1.9 Conclusion International Stock Portfolios and Optimal Currency Hedging with Regime Switching 10 11 13 16 Markus Leippold and Felix Morger 2.1 2.2 2.3 2.4 2.5 Introduction The model Estimation results Discussion Conclusion 16 18 21 26 39 vii viii CONTENTS The Determinants of Domestic and Foreign Biases: An Empirical Study Fathi Abid and Slah Bahloul 3.1 Introduction 3.2 Theoretical framework of domestic and foreign biases 3.3 Data and preliminary statistics 3.4 The determinants of domestic and foreign biases 3.5 The empirical analysis 3.6 Additional tests 3.7 Conclusion The Critical Line Algorithm for UPM–LPM Parametric General Asset Allocation Problem with Allocation Boundaries and Linear Constraints 42 42 44 46 56 67 71 74 80 Denisa Cumova, David Moreno and David Nawrocki 4.1 Introduction 4.2 The upside potential–downside risk portfolio model 4.3 An empirical example 4.4 Conclusion Currency Crises, Contagion and Portfolio Selection 80 82 92 94 96 Arindam Bandopadhyaya and Sushmita Nagarajan 5.1 5.2 5.3 5.4 5.5 Introduction Stock market average rates of return and average volatility Stock market correlations Portfolio performance Conclusion Bond and Stock Market Linkages: The Case of Mexico and Brazil 96 97 99 100 101 103 Arindam Bandopadhyaya 6.1 6.2 6.3 6.4 Introduction The estimation equations and data Results Conclusion The Australian Stock Market: An Empirical Investigation 103 105 109 112 114 Adeline Chan and J Wickramanayake 7.1 7.2 7.3 Introduction Existing evidence Hypothesis 114 115 118 CONTENTS 7.4 7.5 7.6 The data Data analysis and results Conclusion ix 119 127 132 The Price of Efficiency – So, What Do You Think About Emerging Markets? 137 Zsolt Berényi 8.1 8.2 8.3 8.4 8.5 Introduction Higher moment performance analysis – the theory The efficiency gain/loss methodology Testing results Conclusion Liquidity and Market Efficiency Before and After the Introduction of Electronic Trading at the Sydney Futures Exchange 137 138 140 143 149 151 Mark Burgess and J Wickramanayake 9.1 Introduction 9.2 Review of the literature 9.3 Options data volume as a proxy for liquidity 9.4 Sample design 9.5 Analysis of results 9.6 Conclusion 10 How Does Systematic Risk Impact Stocks? A Study of the French Financial Market 151 152 154 159 165 178 183 Hayette Gatfaoui 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Introduction Theoretical framework Empirical study The impact of systematic risk Further investigation Market benchmark comparison Conclusion 11 Matrix Elliptical Contoured Distributions versus a Stable Model: Application to Daily Stock Returns of Eight Stock Markets 183 185 187 190 195 201 209 214 Taras Bodnar and Wolfgang Schmid 11.1 Introduction 11.2 Small sample tests 11.3 Analysis of the power functions 11.4 Empirical study 11.5 Conclusion 214 216 221 222 224 230 THE MODIFIED SHARPE RATIO AND CANADIAN HEDGE FUNDS 12.3 DATA AND METHODOLOGY The dataset we use contains hedge fund returns for fifty funds in Canada However, the majority of the funds commenced operations in 2001 and have been discarded because of the small number of data points available at the time of writing Only nine live Canadian hedge funds reporting monthly performance figures spanning the period January 1998–December 2002 have been investigated We obtain data from Beck and Nagy (2003) This period contains the extreme market event of August 1998 as well as the September 11, 2001 terrorist attacks We use the Extreme Metrics software and assume a risk-free rate of percent to compute the results, using a 95 percent VaR probability This means that the investor is able to borrow and reinvest in the market portfolio at zero cost in order to move along the capital market line This assumption simplifies the ranking of assets, especially when some of them have an average return below the risk-free rate, which yields a negative Sharpe or modified Sharpe ratio The difference between the traditional and modified Sharpe ratios is that, in the latter, the standard deviation is replaced by the modified VaR (at 95 percent) in the denominator The traditional Sharpe ratio is generally defined as the excess return per unit of standard deviation, as represented by the following equation: Sharpe ratio = R p − RF σ (12.1) where RP = return of the portfolio; RF = risk-free rate; and σ = standard deviation Since Equation (12.1) presents several limitations for non-normal distribution, a modified Sharpe ratio can be defined in term of modified VaR, as follows: Modified Sharpe ratio = Rp − RF , MVaR (12.2) with 1 MVaR = W[µ − {zc + (zc2 − 1)S + (zc3 − 3zc )K 24 − (2zc3 − 5zc )S2 }σ] 36 (12.3) where RP = return of the portfolio; RF = risk-free rate; σ = standard deviation; Zc = is the critical value for probability (1 − α) − 1.96 for a 95 percent probability; S = skewness; and K = excess kurtosis The detailed derivation of the formula for modified VaR is beyond the scope of this chapter Readers are guided to Favre and Galeano (2002) for a more detailed explanation GREG N GREGORIOU 231 12.4 EMPIRICAL RESULTS 12.4.1 Descriptive statistics Table 12.1 displays monthly statistics on mean return, standard deviation, skewness, excess kurtosis, normal and modified VaR, Jarque–Bera statistic and compounded returns of the hedge funds during the examination period The average of the compounded returns and mean monthly returns are greatest in the highest group and least in the lowest group – an expected finding In addition, we find that positive skewness is more pronounced in the lowest group, yielding more positive monthly returns, whereas the top group has the least average positive skewness A likely explanation is that smaller hedge funds can better control skewness in negative extreme market events, and on average will have less negative monthly returns The lowest group (see Table 12.1, Panel C) has the highest volatility and lowest returns, which could be attributed to hedge funds taking on more risk to achieve greater returns while increasing assets under management 12.4.2 Performance discussion Market risk and performance results are also presented in Table 12.1 First we observe that the middle group has, in absolute value, the lowest normal and modified VaR, so is less exposed to extreme market losses Furthermore, we find that the non-normality when skewness and kurtosis are considered simultaneously using the Jarque–Bera tend to be the largest for small hedge funds With regard to performance, we notice that the lowest group has the lowest traditional Sharpe and modified Sharpe ratios It appears that medium-sized hedge funds can a better job in controlling riskadjusted performance than either small or large funds Since medium-sized hedge funds receive a greater inflow of money than small funds, they can alter their allocation more frequently However, there exists a huge difference of assets under management between large and medium funds When receiving a vast inflow of capital, large hedge funds could be overwhelmed and might experience trouble in producing superior risk-adjusted returns than medium-sized hedge funds Capacity constraints may exist, since the Toronto Stock Exchange is relatively small compared to the US markets, and trading securities may further restrict large Canadian hedge funds, thus making trading sporadic, especially when leverage and short-selling is involved Smaller hedge funds with fewer assets might have no choice but to hold their portfolio for a long period of time, irrespective of changing economic conditions Fund name 232 Table 12.1 Descriptive statistics of Canadian hedge funds, 1998–2002 Assets Mean Std dev Skewness Excess Modified Normal Traditional Modified Jarque–Bera Compound (millions (%) (%) kurtosis VaR VaR Sharpe sharpe statistic return $) 99% 99% ratio ratio (%) Panel A: Sub-sample 1: Top funds Arrow Clocktower 325 1.4 3.5 0.2 0.00 −6.3 −6.8 0.18 0.16 Goodwood Fund 200 1.6 4.6 0.4 0.5 BPI Global Opportunities 195 1.5 5.9 0.9 1.1 −8.2 −9.1 0.15 −8.3 −12.3 0.14 Average 240 1.5 4.67 0.5 0.53 −7.60 −9.4 −3.9 0.26 127.73 0.13 1.92 137.07 0.09 10.88 119.97 0.16 0.13 4.35 128.26 −4.6 0.14 0.12 0.97 65.10 Panel B: Sub-sample 2: Middle funds −0.4 Horizons Mondiale 125 0.9 2.3 0.2 Horizons Univest 107 0.9 0.7 0.2 1.2 −0.8 −0.9 0.75 0.66 3.74 74.44 82 1.8 5.7 0.00 3.4 −11.5 −16.2 0.13 0.09 28.57 162.52 104.67 1.2 2.9 0.13 1.4 5.4 7.2 0.34 0.29 11.09 100.69 0.10 0.07 24.79 112.03 Vertex Average Panel C: Sub-sample 3: Bottom funds Friedberg TT Equity Hedge 1.6 8.0 1.1 2.3 −11.9 −17.1 Horizons Strategic 1.8 7.0 3.2 18.4 −0.1 −14.5 0.10 0.09 948.02 77.22 Hillsdale Market Neutral ($US) 0.2 4.2 0.4 1.8 −9.6 −9.7 −0.02 −0.02 10.18 6.18 Average 3.67 1.2 6.4 1.57 7.5 −7.2 −13.76 0.06 0.05 327.66 65.14 GREG N GREGORIOU 233 When we compare the results between the traditional and the modified Sharpe ratios, we find that the traditional Sharpe ratio is higher, confirming that tail risk is underestimated 12.5 CONCLUSION It is of critical importance to understand that complications will arise when a traditional measure of risk-adjusted performance, such as the traditional Sharpe ratio, is used to investigate fat tails and non-normal returns of hedge funds Institutional investors must use the modified Sharpe ratio to measure the risk-adjusted returns correctly; and the modified VaR is recommended to measure extreme negative returns because the normal VaR only considers the first two moments of a distribution, namely mean and standard deviation The modified VaR, however, takes into consideration the third and fourth moments of a distribution – skewness and kurtosis Using both the modified Sharpe and modified VaR will enable investors to obtain a more accurate picture without any bias Furthermore, the modified VaR is lower than the normal VaR because of negative skewness in hedge fund returns and the small excess positive kurtosis The statistics we have presented can be applied to all hedge fund and commodity trading adviser (CTA) classifications to evaluate non-normal returns We believe many institutional investors wanting to add hedge funds and funds of hedge funds to traditional stock and bond portfolios must request additional and more appropriate statistics such as the modified Sharpe ratio in analyzing the returns of hedge funds NOTE The standard VaR, which assumes normality and uses the traditional standard deviation measure, looks only at the tail of the distribution of extreme events This is common when examining mutual funds, but when applying this technique to funds of hedge funds, difficulties arise because of the non-normality of returns (Favre and Galeano, 2002) The modified VaR takes into consideration the mean, standard deviation, skewness and kurtosis to evaluate correctly the risk-adjusted returns of funds of hedge funds Computing the risk of a traditional investment portfolio consisting of 50% stocks and 50% bonds with the traditional standard deviation measure could underestimate the risk by as much as 35% (Favre and Singer, 2002) REFERENCES Agarwal, V and Naik, N (2004) “Risks and Portfolio Decisions Involving Hedge Funds”, Review of Financial Studies 17(1): 63–98 Beck, P and Nagy, M (2003) Hedge Funds for Canadians (Toronto: John Wiley) 234 THE MODIFIED SHARPE RATIO AND CANADIAN HEDGE FUNDS Favre, L and Galeano, J A (2002) “Mean-Modified Value-at-Risk with Hedge Funds”, Journal of Alternative Investments, 5(2): 21–5 Favre, L and Singer, A (2002) “The Difficulties in Measuring the Benefits of Hedge Funds”, Journal of Alternative Investments, 5(1): 31–42 Jorion, P (2000) Value at Risk (New York: McGraw-Hill) Markowitz, H (1952) “Portfolio Selection”, Journal of Finance, 77(1): 77–91 Rockafellar, R T and Uryasev, S (2001) “Conditional Value-at-Risk for General Loss Distributions”, Research Report, ISE Dept, University of Florida Index ABN Amro accounting standard index 59–60, 66, 67–74, 75–7 Ackert, L.F 157, 164, 174, 175 African bloc 47, 54, 56 Agarwal, V 229 Ahearne, A 42, 43, 63, 64, 71–2, 78 Air Liquide 190–209, 210 alpha art market 9–10 Jensen’s alpha 197–8 American bloc 47, 54, 56 Amin, G 140 analysis of variance (ANOVA) 161, 162, 167–73 Andersen, T.G 215, 224 Ang, A 17, 33, 37 Anthony, J.H 155 anti-director rights 59–60, 66, 67–74, 75–7 arbitrage pricing theory (APT) 114–15, 116–17 Argentina 104 art 1–15 art market 3–4; defining a bubble in 5–6 data 6–8 empirical studies 4–5 methodology 9–10 results 10–11 Art Market Research (AMR) database Artzner, P 191 Asia/Pacific bloc 47, 54, 56 Asian currency crisis 96–102 at the money (ATM) option contracts 160, 167, 168–9, 172 Ates, A 153 auction houses Augmented Dickey–Fuller (ADF) test 163, 174 Australian Statex Actuaries Price Index 157 Australian Stock Exchange (ASX) 151–2 All Ordinaries Index 154, 157, 158, 159, 161, 165–7, 168, 169, 175, 176–8 lead–lag relationship with SFE 155, 158–9, 164–5, 176–8 multifactor model 114–36; cross-sectional regressions 124–7; data 119–27; data analysis and results 127–32; parsimonious model 119, 130–2; portfolio characteristics 124; returns to be explained 123–4 Austria 47 automation 151–82 analysis of results 165–78; ANOVA results 167–73; descriptive statistics 165–7; price discovery analysis 174–8 sample design 159–65; cointegration 163–5; data sources 159–60; methodology 160; model and statistical procedures 160–3 Baig, T 97, 104 Barbone, L 96, 104 Bauer, R Baumol, W bear state 23–6, 27 Beck, P 230 behavioral anomalies 12–13 235 236 INDEX behavioral theory 80–1 Bekaert, G 17, 33, 37 Belgium 47, 63 benchmarks benchmark assets in efficiency gain/loss methodology 140–1; return distribution 144 market benchmarks for French stocks 201–9; forecasting performance 205–9 Benjamin, W 12 bequests 13 Berk, J.B 211, 215 Berkowitz, J 22 beta 197–8, 203–5, 206 bilateral trades 65–6, 67–74, 75–7 Black, F 17, 185, 186 Black and Scholes volatility bias 189 Blattberg, R.C 222 blocs 47, 54, 56 Bodnar, T 215, 220 Bohn, H 67 bond market–stock market linkages 103–15 book-to-market value 118–19, 121–2, 123–4, 125, 126–32, 133 Bortoli, L 153 bounded rationality 80 Brady bonds 103–13 stripped-yield spreads 104, 105, 107, 108, 109–11 Brailsford, T 154, 197 Brandt, M.W 21 Brazilian bond market–stock market linkages 103–13 Brooks, C 158, 159 Brownian motion 185–6 bubbles 11–14 art market 1–2, 5, 8, 10–11; defining a bubble in the art market 5–6 Internet 32–3 real estate “bubbliness”, degree of 13–14 CAC40 index 187–209, 210–11 call pricing formula 186–7 Campbell, J.Y 184, 186, 197, 200, 210 Campbell, R.A 4, Canadian hedge funds 228–34 capital asset pricing model (CAPM) 114, 115–16, 183, 197, 215 international (ICAPM) 42 capital controls 43, 44, 57–8, 59–60, 63, 67–74, 75–7 capital flow restrictions 57–8, 63, 67–74, 75–7 Carmichael, B 64 Case, K.E causality analysis impact of systematic risk on French stocks 191–5; Granger causality test 193–5; VAR specification 191–3 lead–lag relationship 164–5, 176–8 Central and Eastern European investment funds 143–50 certainty-equivalent compensation 28–30 Chamberlain, G.A 215 Chan, K 43, 44, 45, 46, 47, 55, 56, 63, 64, 65, 67, 69, 74, 78, 127 Chanel, O Chen, J 17 Chen, N.F 116, 117, 118, 119, 125, 126, 127, 130, 132 Cheng, C.S 119 China 63 Chordia, T 105 Clare, A.D 127, 130 Clark, P.K 154 cocoa prices 156–7 Coen, A 64 cointegration 156–8, 163–5, 175–8 Johansen test 163–4, 175–6 lead–lag relationship 155, 158–9, 164–5, 176–8 unit root tests 163, 174 collateral, art as compensation, certainty-equivalent 28–31 Connor, G 117 contagion 104 currency crises and portfolio selection 96–102 Cooper, I 44–5, 47 Coordinated Portfolio Investment Survey (CPIS) dataset 43 Copeland, L 154, 156 corner portfolios 87, 91 correlation studies correlation coefficient and home bias 67–74, 75–7 impact of systematic risk on French stocks 190–1 peer group analysis 148–9 stock market returns 97, 99–100, 101 cost-of-carry 158, 180 creditworthiness 103–4 INDEX critical line UPM/LPM portfolio optimization algorithm (CLA) 80–95 derivation 82–4 efficient segments on the efficient frontier 88; adjacent efficient segments 89–92 empirical example 92–4 Kuhn–Tucker conditions 84–7 cross-sectional regressions 124–32 CRR (Chen, Roll and Ross) macroeconomic variables 122–3, 126, 127–32, 133 cumulative wealth 32–4 currency crises 96–102, 104 portfolio performance 100–1 stock market average rates of return and average volatility 97–9, 101 stock market correlations 97, 99–100, 101 currency hedging 16–41 economic importance of regimes 28–31 estimation results for regime-switching models 21–6, 27 optimal foreign investment 38, 39 out-of-sample test for regime-switching strategies 31–7, 39 Cyert, R.M 80 Dahlquist, M 43, 72 daily financial data 214 matrix elliptical contoured distributions 222–4 Dales, A 17 Danone 190–209, 210 DAX 157 DeBondt, W.F.M 117 default probabilities 104 depreciation of the dollar 33, 36 descriptive statistics automation of SFE 161, 165–7 Canadian hedge funds 231, 232 developed countries 47, 54, 56 Diacogiannis, G.P 116 Diamandis, P 116 disposition effect 13 distributional price 139 dividend-paying framework 187 dollar, depreciation of the 33, 36 domestic bias 42–79 data sources 46 237 determinants of 56–67; capital control 57–8, 59–60, 63; economic development 56–63; familiarity 61–2, 65–6; information costs 61–2, 64; investor protection 59–60, 66; investors’ behavior 61–2, 65; other variables 67; stock market development 57–8, 63–4 results of empirical analysis 67–9 statistics on 47–56 statistics on investor holdings 46–53, 54 theoretical framework 44–6 world float portfolio 72–4, 75 double-lognormal (DLN) framework 138, 142–3, 144, 150 Dow Jones STOXX market indices 189 downside risk 229 time-varying 1–15 Durbin–Watson (DW) test 130, 131 Dybvig, P.H 139 East Asian stock markets 96–102 economic development 43, 44, 56–63, 67–77 economic importance of regimes 28–31 Ederington, L.H 143 Edison, H 63 efficiency gain/loss 138, 140–3, 149–50 benchmark 141–2 definition 140–1 higher moment performance characteristics 145–7 underlying 142–3 efficient frontiers 87–94 efficient segments on 88; adjacent efficient segments 89–92 mean-variance and UPM/LPM models 92–4 efficient market hypothesis (EMH) 114 semi-strong form 156, 175–6, 180 Eichenberger, R 12, 13 Eichengreen, B 96 electronic trading see automation elliptical distributions see matrix elliptical contoured distributions emerging markets Brady bonds see Brady bonds domestic and foreign biases 47, 54, 56, 64, 67–77 performance evaluation 137–50 238 INDEX endowment effect 12 Engle, R.F 163 Epps, M.L 154 Epps, T.W 154 Erb, C.B 96–7 European bloc 47, 54, 56 expected inflation, change in 122–3, 126, 127–32, 133 expropriation, risk of 59–60, 66, 67–77 extreme value theory (EVT) Faff, R.W 118, 119, 192, 197 Fama, E.F 115, 117–19, 121, 123, 125, 126, 127, 130, 132, 156, 158, 180, 211 familiarity 43, 44, 61–2, 65–78 Fang, K.T 219, 225 far in the money (FITM) option contracts 160, 168–9 Faruquee, H 43 Favre, L 229, 230 firm-attribute factors 211 multifactor models 117, 118–19; ASX 121–32, 133 Fishburn, P.C 81 Fleming, J 97, 105 foreign bias 42–79 data sources 46 determinants of 56–67; capital control 57–60, 63; economic development 56–63; familiarity 61–2, 65–6; information costs 61–2, 64; investor protection 59–60, 66; investors’ behavior 61–2, 65; other variables 67; stock market development 57–8, 63–4 results of empirical analysis 69–71 statistics on 47–56 statistics on investor holdings 46–53, 54 theoretical framework 44–6 world float portfolio 72–4, 76 foreign direct investment (FDI) 56–63, 67–77 Forni, L 96, 104 France 47 impact of systematic risk on stocks in French financial market 183–213 Fraser, P 116, 118, 119 French, K.R 43, 65, 115, 117–19, 121, 123, 125, 126, 127, 130, 132, 183, 211 Freund, W.C 156, 161, 163 Frey, B.S 12, 13 Friedman, M 81 Friend, I 116 Frino, A 154 Froot, K 16 Fund of Art Funds futures see Sydney Futures Exchange (SFE) gamma estimates 9, 10–11, 13 Galeano, J.A 229, 230 Garcia, R 17 GDP per capita 5663, 6777 Gehrig, T 64 Genỗay, R 1834 geographical proximity 47, 61–2, 65, 67–74, 75–7 Germany 47 currency hedging and regime switching 21–6, 27, 33–6; optimal hedge ratio 36–7 DAX 157 Giannetti, M 66 Gibbons, M.R 215 Glassman, D.A 42 Glen, J 17 Glosten, L.R 140 Goetzmann, W.N Goldfajn, I 97, 104 Gonedes, N.J 222 Gourieroux, C 199 Goyal, A 200, 210 Granger, C.W.J 163 Granger causality test automation of SFE and lead–lag relationship 164–5, 176–8 systematic risk and French stocks 193–5 Gray, S 17 Groenewold, N 116, 118, 119, 157, 174, 175 Grünbichler, A 158 Guidolin, M 17, 28 Gupta, A.K 216, 217 Halliwell, J 118, 119, 123 Hamilton, J.D 20 Hartmann, P 96–7 Harvey, C.R 96–7 He, J 119, 131–2 hedge funds, Canadian 228–34 hedging, currency see currency hedging; optimal currency hedging hidden regime switches 20–1 high correlation state 25–6, 27 INDEX higher moment performance analysis 138–40, 145–7 portfolio replication 139–40 rationale 139 role of higher moments 138–9 Hill, B home bias 42–79 Ahearne measure 71–2, 73 causes 56–67 data and preliminary statistics 44–56 empirical analysis 67–71 theoretical framework of domestic and foreign biases 44–6 world float portfolio 72–4, 77 Hong Kong 97–100 horse race (out-of-sample test) 31–7, 39 house prices Huberman, G 43, 65 Huisman, R Hungarian investment funds 143–50 in the money (ITM) option contracts 160, 168–9 Indonesia 97–100 industrial production growth rate, unexpected change in 122–3, 126, 127–32, 133 inflation change in expected 122–3, 126, 127–32, 133 unexpected inflation rate 122–3, 126, 127–32, 133 information costs 43, 44, 61–2, 64, 67–78 information flow 154, 155, 164–5 informational efficiency 152, 179 intensity of capital control 63, 67–74, 75–7 interest rates risk-free 188–9 unexpected change in term structure 122–3, 126, 127–32, 133 international capital asset pricing model (ICAPM) 42 International Finance Corporation (IFC) 63 Internet bubble 32–3 investor behavior domestic and foreign biases 43, 44, 61–2, 65, 67–77 UPM/LPM critical line algorithm 80–95 investor protection 67–77 Izvorski, I 104 239 43, 44, 59–60, 66, Jagannathan, R 140, 197 Japan 5, 8, 97–100 Jarnecic, E 155 Jarque–Bera test statistic 22, 24, 145, 231, 232 Jasiak, J 199 Jegadeesh, N 117 Jensen, C.M 184, 197 Jensen-type regressions 197–8, 201–3, 204, 207, 208 Johansen, S 164, 175 Johansen cointegration test 163–4, 175–6 Jorion, P 17, 229 judicial system efficiency 59–60, 66, 67–77 Juselius, K 164, 175 Kahneman, D 12, 13, 81 Kantner, M 226 Kaplan, P.D 81 Kaplanis, E 44–5, 47 Karolyi, G.A 96 Karpoff, J.M 154 Kat, H.M 139, 140 Kelly, J.M 105 Kempf, A 157 Kilka, M 43 Knif, J 183, 192, 197 known characteristic function 216, 217–18 known location vector 216, 218, 219 Kofman, P 152, 158 Korea 97–100 Korn, O 157 Koskinen, Y 66 Koutmos, G 183, 192, 197 Kuhn–Tucker conditions 84–7 kurtosis 229, 231, 232, 233 La Porta, R 66 Lagrange multiplier tests 130, 131 language, common 61–2, 65, 67–77 law, rule of 59–60, 66, 67–77 lead–lag relationship 155, 158–9, 164–5, 176–8 Lee, J 153 legal system, type of 66, 67–77 leptokurtic distributions 189, 191 240 INDEX likelihood ratio tests 22, 24 Lintner, J 42, 115 liquidity 151–82 options data volume as a proxy for 154–9; price discovery and operational efficiency of a market structure 156–9 liquidity ratios 162, 165–7, 168, 169 and market volatility 169–71 London Futures and Options Exchange 156–7 Long-Term Capital Management (LTCM) 228 Longstaff, F.A 21 L’Oréal 190–209 loss aversion 12 low correlation state 25–6, 27 lower partial moment (LPM) model 81 see also upside potential–downside risk portfolio model MacBeth, J.D 119, 125 MacKinlay, C 115 MacKinnon, J.G 174 macroeconomic variables 117, 118–19 multifactor model for ASX 122–3, 126, 127–32, 133 Malaysia 97–100 Mananyi, A 156–7 March, J.G 80 market efficiency 156 market factor 185–7, 189–90 impact of systematic risk on French stocks 190–211 market return index 126, 127–32, 133 market structure 151–82 dynamics of a changing market structure 152–3 price discovery and operational efficiency of 156–9 Markowitz, H 81, 82, 87, 88, 228 Massimb, M 152–3 Masson, P 96 matrix elliptical contoured distributions 214–27 analysis of the power functions 221–2 empirical study 222–4 small sample tests 216–20; further statistics 220; known type of elliptical symmetry 217–18; unknown type of elliptical symmetry 218–19 McKenzie, M.D 192 mean–variance analysis 81, 228 critical line UPM/LMP model and 92–4 Meese, R 17 Mei, J Mele, A 199 Merton, R.C 130 Mexico bond market–stock market linkages 103–13 currency crisis 96, 104 Min, H 103, 112 mixture of distributions (MDH) hypothesis 154, 161, 170 modified Sharpe ratio 228–34 modified VaR (MvaR) 228, 229, 230, 231, 232, 233 Mody, A 96 MONEP (Marché des Options Négociables de Paris) 188 moneyness portfolios 154–5, 159–60, 161, 167, 168–9 monsoonal effect 96 Moser, J 152, 158 Moses, M Mossin, J 115 Muirhead, R.J 219, 225 multifactor arbitrage pricing theory (APT) 114–17 multifactor models (MFM) 114–36 data 119–27; cross–sectional regressions 124–7; CRR macroeconomic variables 122–3; explanatory returns 119–22; portfolio characteristics 124; returns to be explained 123–4 data analysis and results 127–32 existing evidence 115–18 parsimonious model for ASX 119, 130–2 multivariate t-distributions 221–2, 225–6 Nagy, M 230 Naik, N 229 Nawrocki, D 81 New Zealand Gross Index 157 Ng, L.K 119, 131–2 no-opportunity arbitrage valuation principle 186 non-linearity 195–201 Norway 63 NZSE-40 Index 157 INDEX omission bias 13 open outcry 152, 153 operational efficiency 152, 153, 179 of a market structure 156–9 opportunity cost effect 12 optimal currency hedging 16–41 certainty equivalent compensation 28–31 estimation results for regime-switching models 21–6, 27 horse race for regime-switching strategies 31–7, 39 optimal foreign investment 38, 39 optimal hedge ratios 36–7 optimal weights 35–6 options benchmark assets 141–2; return distribution 144 option moneyness portfolios 154–5, 159–60, 161, 167, 168–9 options data volume as a proxy for liquidity 154–9 portfolio replication 139–40 pricing 186–7; dividend framework 187; no-dividend framework 186–7 out-of-sample test (horse race) 31–7, 39 out of the money (OTM) option contracts 160, 168–9 Owen, J 215 ownership effect 12 Pacific/Asia bloc 47, 54, 56 Palaro, H.P 139 parsimonious multifactor model 119, 130–2 payoff distribution pricing model 139 peer group analysis 148–9 Perez-Quiros, G 17 perfect knowledge 18–20 performance Canadian hedge funds 231–3 forecasting 205–9 performance evaluation 137–50 efficiency gain/loss methodology 140–3 higher moment performance analysis 138–40 testing results 143–9; basic performance characteristics 145; data for the analysis 143–4; higher moment performance 241 characteristics 145–7; peer group analysis 148–9; return distribution of the benchmark asset 144 Perold, A 16 Perron, P 17 Phelps, B 152–3 Philippines 97–100, 104 Phillip–Perron (PP) test 163, 174 phone call costs 61–2, 64, 67–77 Pirrong, C 153 Pitts, M 154 Portes, R 64 portfolio replication 139–40, 145–7, 150 Post, T 81 Poterba, J 43, 65, 183 power functions 221–2 price art price indices 6–8 and trading volume 154 price discovery analysis 156–9, 174–9 prospect theory 81 Rabinovitch, R 215 Racine, M.D 157, 164, 174, 175 ratio analysis 162, 172–3, 179 real-estate bubble real GDP growth rate 56–63, 67–77 regime switching 16–41 economic importance of regimes 28–31 estimation results 21–6, 27; data 21–2, 23; parameter estimates 22–6; specification test 22, 24 implications on asset allocation 26–38 model 18–21; portfolio selection under hidden regime switches 20–1; portfolio selection with perfect knowledge of the active state 18–20 optimal foreign investment 38, 39 strategies in competition 31–7, 39; cumulative wealth and Sharpe ratio 32–4; optimal hedge ratio 36–7; optimal weights 35–6 regression analysis cross-sectional regressions 124–32 impact of systematic risk on French stocks 196–209 liquidity and automation of SFE 162–3, 172–3 Reinganum, M.R 115, 116, 119 242 INDEX Renault 190–209, 210 replication, portfolio 139–40, 145–7, 150 return correlations 96 East Asian economies 97, 99–100, 101 returns Australian stock market 123–4, 127–32 bond and stock market linkages 105–12 East Asian stock markets average rates of return 97, 97–9, 101 return distribution of benchmark asset for Hungarian investment funds 144 reverse S-shaped utility functions 81 Rey, H 64 Richardson, M 161 Riddick, L.A 42 risk downside see downside risk upside 229 upside potential–downside risk portfolio model 80–95 risk aversion 81, 92, 93, 94 risk-free interest rate 188–9 risk premiums, unexpected change in 122–3, 126, 127–32, 133 risk-seeking behavior 81 Rockafellar, R.T 229 Roll, R 114, 116, 184, 197, 209 Ross, S.A 114, 116 Rubinstein, M 154, 160, 161 rule of law 59–60, 66, 67–74, 75–7 S&P 500 index 157 Samorodnitsky, G 226 Samuelson, W 12 Santa-Clara, P.P 200, 210 Sarkisson, S 65 Sarno, L 164 Savage, L.J 81 SBF120 index 190–209, 210 SBF250 index 190–209, 210 scale factor 185–7, 189–90 Schill, M 65 Schmid, W 215, 220 Schneider 190–209 Scholes, M 185, 186 Schulman, E 16 Schwartz, E 21 Selỗuk, F 1834 self-deception theory 13 Sharpe, W.F 42, 115, 183, 197, 209 Sharpe ratio 138, 228, 229, 230, 231, 232, 233 CEE investment funds 147, 150 modified 228–34 regime switching and optimal currency hedging 32–4 Shefrin, H 13 short selling 145–7 Shyy, G 153 Siegel, J.J Siegel, L.B 81 Simon, H.A 80 simple regression analysis 196–9, 201, 202, 203, 204, 206, 207, 208 Singapore 63 Singer, A 229 size 118–19, 121–33 skewness 229, 231, 232, 233 Smith, T 161 Société Générale 190–209, 210 Solnik, B 117 Sortino, F 81 spillover effect 96 SPI futures/All Ordinaries Index ratio 162–3, 172–3 Statex Actuaries Accumulation Index 157 statistical multifactor models 117 Statman, M 13 status quo bias 12 Stiglitz, J.E stock index dynamic 185–6 stock market capitalization 57–8, 64, 67–77 stock market development 43, 44, 57–8, 63–4, 67–77 stock markets linkages to bond markets 103–13 currency crises, contagion and portfolio selection 96–102 Strong, N 65 Struthers, J.J 156–7 Stulz, R.M 96, 183 sunk cost effect 12–13 Sydney Futures Exchange (SFE) 151–82 lead–lag relationship with ASX 155, 158–9, 164–5, 176–8 Share Price Index (SPI) 157, 158, 159, 161, 165–7, 175, 176–8 SPI futures/All Ordinaries Index ratio 162–3, 172–3 INDEX symmetric stable distributions 221, 222, 225–6 systematic risk 183–213 empirical study 187–90; data 187–9; induction of systematic risk 189–90 impact 190–5; causality 191–3; correlation 190–1; Granger causality test 193–5 market benchmark comparison 201–9; basic empirical study 201–5; forecasting performance 205–9 non-linearity 195–201; simple regression analysis of asset returns 196–9; volatility impact 199–201 theoretical framework 185–7; option pricing 186–7; valuation setting 185–6 Szego, G 191 tail index estimator 8, 9–12 Taiwan 97–100 Taqqu, M.S 226 Tauchen, G 154 Taylor, M.P 164 term structure, unexpected change in 122–3, 126, 127–32, 133 Tesar, L 64, 67 Thaler, R.H 12, 13, 117 Thomas, S.H 127, 130 Thomson 190–209 time series properties 174–5 time-varying downside risk 1–15 Timmerman, A 17, 28 Titman, S 117 Toronto Stock Exchange (TSE) 156, 231 Totalfina Elf 190–209, 210 trade, scaled by GDP 56–63, 67–77 trading volume 154, 155, 161, 165–7 market volatility and 169–71 transaction costs 64, 67–77 Treynor, J 183 Turkington, J 157, 158 Turner, C 17 Tversky, A 12, 81 two-year return 67–77 underlying distribution 142–3 unexpected inflation rate 122–3, 126, 127–32, 133 uniqueness of art works 243 unit root tests 163, 174 United Kingdom art market 6–8, 10 currency hedging and regime switching 21–6, 27, 33–6; optimal hedge ratio 36–7 United States art market 6–8, 10 regime switching and currency hedging 21–7, 33–6 stock market levels and returns 107 unknown characteristic function 218–19 unknown location vector 216–19 upper partial moment/lower partial moment (UPM/LPM) ratio 81 see also upside potential–downside risk portfolio model upside potential–downside risk portfolio model 80–95 efficient segments on the efficient frontier 88; adjacent efficient segments 89–92 empirical example 92–4 Kuhn–Tucker conditions 84–7 upside risk 229 upward bias 209 Uryasev, S 229 Valéo 190–209, 210 valuation 185–6 value, art and 3–4 value-at-risk (VaR) 228, 229, 231, 232, 233 modified (MvaR) 228, 229, 230, 231, 232, 233 Van Vliet, P 81 Varga, T 216, 217 variance decompositions 109, 110, 111 vector autoregressive (VAR) models 164 Brady bonds 105–12 systematic risk and French stocks 191–3, 194 Venezuela 43, 47, 104 Vivendi 190–209, 210 volatility Asian currency crisis 97–9, 101 automation of SFE 161, 169–71 Black and Scholes volatility bias 189 244 INDEX volatility continued impact of systematic risk on French stocks 199–205, 207, 208 volatility parameter 185, 189–90 Whaley, R 189 Whitcher, B 183-4 White, H 127 world float portfolio Walsh, D 157, 158 Wang, G.H.K 153 Wang, Z 197 Warnock, F 63 Weber, M 43 Werner, I 64 Xu, X 65 Zeckhauser, R 12 Zhang, Y.T 219, 225 Zhou, C 64 Zhou, G 215 72–7 ... allocation and international investments / edited by Gerg N Gregoriou p.cm — (Finance and capital markets) Includes bibliographical references and index ISBN 0–230–01917–X Asset allocation Investments, ... 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