Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-20T00:58:21.461Z Has data issue: false hasContentIssue false
  • English
  • Français

Improving Portfolio Selection Using Option-Implied Volatility and Skewness

Published online by Cambridge University Press:  02 January 2014

Victor DeMiguel ,
Yuliya Plyakha ,
Raman Uppal  and
Grigory Vilkov
Victor DeMiguel
Affiliation:
avmiguel@london.edu, London Business School, 6 Sussex Place, Regent’s Park, London NW1 4SA, United Kingdom;
Yuliya Plyakha
Affiliation:
plyakha@gmx.de, Luxembourg School of Finance, University of Luxembourg,4, rue Albert Borschette, L-1246 Luxembourg;
Raman Uppal
Affiliation:
raman.uppal@edhec.edu, Edhec Business School, 10 Fleet Place, Ludgate, London EC4M 7RB, United Kingdom and Center for Economic and Policy Research (CEPR);
Grigory Vilkov
Affiliation:
vilkov@vilkov.net, Goethe University Frankfurt, Finance Department, Grüneburgplatz 1 / Uni-Pf H 25, Frankfurt am Main,D-60323, Germany.
Get access Rights & Permissions [Opens in a new window]

Abstract

Our objective in this paper is to examine whether one can use option-implied information to improve the selection of mean-variance portfolios with a large number of stocks, and to document which aspects of option-implied information are most useful to improve their out-of-sample performance. Portfolio performance is measured in terms of volatility, Sharpe ratio, and turnover. Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility. Using option-implied correlation does not improve any of the metrics. Using option-implied volatility, risk premium, and skewness to adjust expected returns leads to a substantial improvement in the Sharpe ratio, even after prohibiting short sales and accounting for transaction costs.

Type
Research Articles
Information
Journal of Financial and Quantitative Analysis , Volume 48 , Issue 6 , December 2013 , pp. 1813 - 1845
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aït-Sahalia, Y., and Brandt, M. W.. “Consumption and Portfolio Choice with Option-Implied State Prices.” NBER Working Paper (2008).Google Scholar
Andersen, T. G.; Bollerslev, T.; and Diebold, F. X.. “Parametric and Nonparametric Measurement of Volatility.” In Handbook of Financial Econometrics, Aït-Sahalia, Y., and Hansen, L. P., eds. Amsterdam: North Holland (2009).Google Scholar
Ang, A.; Bali, T. G.; and Cakici, N.. “The Joint Cross Section of Stocks and Options.” Working Paper, Fordham University (2010).Google Scholar
Bakshi, G. S.; Kapadia, N.; and Madan, D. B.. “Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options.” Review of Financial Studies, 16 (2003),101–143.Google Scholar
Bali, T. G., and Hovakimian, A.. “Volatility Spreads and Expected Stock Returns.” ManagementScience, 55 (2009), 1797–1812.Google Scholar
Black, F., and Scholes, M. S.. “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy, 81 (1973), 637–659.CrossRefGoogle Scholar
Bollerslev, T.; Gibson, M.; and Zhou, H.. “Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities.” Finance and Economics Discussion Series, 2004-56, Board of Governors of the Federal Reserve System, Washington, DC (2004).Google Scholar
Bollerslev, T.; Tauchen, G.; and Zhou, H.. “Expected Stock Returns and Variance Risk Premia.” Review of Financial Studies, 22 (2009), 4463–4492.Google Scholar
Boyer, B. H.; Mitton, T.; and Vorkink, K.. “Expected Idiosyncratic Skewness.” Review of Financial Studies, 23 (2010), 169–202.Google Scholar
Brandt, M. W.; Santa-Clara, P.; and Valkanov, R.. “Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns.” Review of Financial Studies, 22 (2009), 3411–3447.Google Scholar
Buss, A., and Vilkov, G.. “Measuring Equity Risk with Option-Implied Correlations.” Review of Financial Studies, 25 (2012), 3113–3140.Google Scholar
Carr, P., and Wu, L.. “Variance Risk Premiums.” Review of Financial Studies, 22 (2009), 1311–1341.Google Scholar
Chan, L. K. C.; Karceski, J.; and Lakonishok, J.. “On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model.” Review of Financial Studies, 12 (1999), 937–974.Google Scholar
Corsi, F. “A Simple Approximate Long-Memory Model of Realized Volatility.” Journal of Financial Econometrics, 7 (2009), 174–196.Google Scholar
Cox, J. C., and Huang, C.-F.. “Optimal Consumption and Portfolio Policies When Asset Prices Follow a Diffusion Process.” Journal of Economic Theory, 49 (1989), 33–83.Google Scholar
Cremers, M., and Weinbaum, D.. “Deviations from Put-Call Parity and Stock Return Predictability.” Journal of Financial and Quantitative Analysis, 45 (2010), 335–367.Google Scholar
DeMiguel, V.; Garlappi, L.; Nogales, F.; and Uppal, R.. “A Generalized Approach to PortfolioOptimization: Improving Performance by Constraining Portfolio Norms.” Management Science, 55 (2009), 798–812.Google Scholar
DeMiguel, V.; Garlappi, L.; and Uppal, R.. “Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?” Review of Financial Studies, 22 (2009), 1915–1953.Google Scholar
Driessen, J.; Maenhout, P.; and Vilkov, G.. “The Price of Correlation Risk: Evidence from Equity Options.” Journal of Finance, 64 (2009), 1375–1404.Google Scholar
Efron, B., and Tibshirani, R.. An Introduction to the Bootstrap. Boca Raton, FL: Chapman and Hall (1993).Google Scholar
Elton, E. J.; Gruber, M. J.; and Spitzer, J.. “Improved Estimates of Correlation Coefficients and Their Impact on Optimum Portfolios.” European Financial Management, 12 (2006), 303–318.Google Scholar
Fama, E. F., and French, K. R.. “The Cross-Section of Expected Stock Returns.” Journal of Finance, 47 (1992), 427–465.Google Scholar
French, K. R. “Presidential Address: The Cost of Active Investing.” Journal of Finance, 63 (2008), 1537–1573.Google Scholar
Goyal, A., and Saretto, A.. “Cross-Section of Option Returns and Volatility.” Journal of Financial Economics, 94 (2009), 310–326.CrossRefGoogle Scholar
Grundy, B. D., and Martin, S.. “Understanding the Nature of the Risks and the Source of the Rewards to Momentum Investing.” Review of Financial Studies, 14 (2001), 29–78.CrossRefGoogle Scholar
Guidolin, M., and Timmermann, A.. “International Asset Allocation under Regime Switching, Skew and Kurtosis Preferences.” Review of Financial Studies, 21 (2008), 889–935.CrossRefGoogle Scholar
Harvey, C. R., and Siddique, A.. “Conditional Skewness in Asset Pricing Tests.” Journal of Finance, 55 (2000), 1263–1295.Google Scholar
Jacobs, H.; Muller, S.; and Weber, M.. “How Should Private Investors Diversify? An Empirical Evaluation of Alternative Asset Allocation Policies to Construct a ‘World Market Portfolio’.” Working Paper, University of Mannheim (2010).CrossRefGoogle Scholar
Jagannathan, R., and Ma, T.. “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps.” Journal of Finance, 58 (2003), 1651–1684.Google Scholar
Jegadeesh, N., and Titman, S.. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, 48 (1993), 65–91.Google Scholar
Jobson, J. D.; Korkie, R.; and Ratti, V.. “Improved Estimation for Markowitz Portfolios Using James-Stein Type Estimators.” Proceedings of the American Statistical Association, 41 (1979), 279–284.Google Scholar
Jorion, P. “Bayes-Stein Estimation for Portfolio Analysis.” Journal of Financial and Quantitative Analysis, 21 (1986), 279–292.Google Scholar
Kostakis, A.; Panigirtzoglou, N.; and Skiadopoulos, G.. “Market Timing with Option-Implied Distributions: A Forward-Looking Approach.” Management Science, 57 (2011), 1231–1249.Google Scholar
Kraus, A., and Litzenberger, R. H.. “Skewness Preference and the Valuation of Risk Assets.” Journal of Finance, 31 (1976), 1085–1100.Google Scholar
Ledoit, O., and Wolf, M.. “Honey, I Shrunk the Sample Covariance Matrix.” Journal of Portfolio Management, 30 (2004a), 110–119.Google Scholar
Ledoit, O., and Wolf, M.. “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices.” Journal of Multivariate Analysis, 88 (2004b), 365–411.Google Scholar
Ledoit, O., and Wolf, M.. “Robust Performance Hypothesis Testing with the Sharpe Ratio.” Journalof Empirical Finance, 15 (2008), 850–859.Google Scholar
Merton, R. C. “Optimum Consumption and Portfolio Rules in a Continuous-Time Model.” Journalof Economic Theory, 3 (1971), 373–413.Google Scholar
Merton, R. C. “Theory of Rational Option Pricing.” Bell Journal of Economics and Management Science, 4 (1973), 141–183.Google Scholar
Newey, W. K., and West, K. D.. “A Simple, Positive-Semidefinite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica, 55 (1987), 703–708.Google Scholar
Pástor, L.. “Portfolio Selection and Asset Pricing Models.” Journal of Finance, 55 (2000), 179–223.CrossRefGoogle Scholar
Politis, D., and Romano, J.. “The Stationary Bootstrap.” Journal of the American Statistical Association, 89 (1994), 1303–1313.Google Scholar
Poon, S.-H., and Granger, C. W. J.. “Practical Issues in Forecasting Volatility.” Financial Analysts Journal, 61 (2005), 45–56.Google Scholar
Rehman, Z., and Vilkov, G.. “Risk-Neutral Skewness: Return Predictability and Its Sources.” Working Paper, Goethe University Frankfurt (2009).Google Scholar
Rubinstein, M. E. “The Fundamental Theorem of Parameter-Preference Security Valuation.” Journal of Financial and Quantitative Analysis, 8 (1973), 61–69.Google Scholar
Vanden, J. M. “Information Quality and Options.” Review of Financial Studies, 21 (2008), 2635–2676.Google Scholar
Xing, Y.; Zhang, X.; and Zhao, R.. “What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?” Journal of Financial and Quantitative Analysis, 45 (2010), 641–662.Google Scholar
Zumbach, G. “Inference on Multivariate ARCH Processes with Large Sizes.” Available at http://dx.doi.org/10.2139/ssrn.1346416 (2009).CrossRefGoogle Scholar