Market Timing with Option-Implied Distributions: A Forward-Looking Approach

Abstract

We address the empirical implementation of the static asset allocation problem by developing a forward-looking approach that uses information from market option prices. To this end, we extract constant maturity S&P 500 implied distributions and transform them to the corresponding risk-adjusted ones. Then we form optimal portfolios consisting of a risky and a risk-free asset and evaluate their out-of-sample performance. We find that the use of risk-adjusted implied distributions times the market and makes the investor better off than if she uses historical returns' distributions to calculate her optimal strategy. The results hold under a number of evaluation metrics and utility functions and carry through even when transaction costs are taken into account. Not surprisingly, the reported market timing ability deteriorated during the recent subprime crisis. An extension of the approach to a dynamic asset allocation setting is also presented. This paper was accepted by Wei Xiong, finance.asset allocation, option-implied distributions, market timing, performance evaluation, portfolio choice, risk aversion

Risk-Adjusted Option-Implied Moments

“…However, disappointment aversion has an axiomatic foundation and is easier to apply to portfolio problems. 14 Applications of disappointment aversion comprise the classical problem of allocating funds between stocks and bonds (Ang, Bekaert, and Liu (2005)), the study of economic benefits from giving investors access to options (Driessen and Maenhout (2007)), and the analysis of market timing strategies (Kostakis, Panigirtzoglou, and Skiadopoulos (2011)). Although these studies provide important results on the effects of disappointment aversion, very little is known about the magnitude of disappointment aversion and its estimation from market data.…”

“…More recent developments have moved forward from simple Black-Scholes volatilities to model-free implied volatilities (Britten-Jones and Neuberger (2000) and Jiang and Tian (2005)) and higher-order implied moments (Bakshi, Kapadia, and Madan (2003) and Neuberger (2012)). Implied moments are used extensively in a variety of applications, like forecasting (see the survey articles by Poon and Granger (2003), Christoffersen, Jacobs, and Chang (2012), and Giamouridis and Skiadopoulos (2012)), risk measurement (Buss and Vilkov (2012), Chang, Christoffersen, Jacobs, and Vainberg (2012), and Baule, Korn, and Saßning (2013)) and portfolio selection (Aït-Sahalia and Brandt (2008), Kostakis, Panigirtzoglou, andSkiadopoulos (2011), DeMiguel, Plyakha, Uppal, andVilkov (2013), Kempf, Korn, and Saßning (2014), and Schneider (2014)).…”

Forward-Looking Measures of Higher-Order Dependencies with an Application to Portfolio Selection

“…Kostakis, Panigirtzoglou, and Skiadopoulos (2011) pal, and Vilkov (2012) show that implied skewness can be used to improve the performance of parametric portfolio policies. However, they make no attempt to exploit higher-order co-moments.…”

The Pricing Kernel Puzzle: Survey and Outlook

“…15 Their finding, that the forward-looking physical distributions then produces better portfolios than the historical 15 The methodology is very similar to Bliss and Panigirtzoglou (2004). Zdorovenin and Pezier (2011) use a close variant, too, and are subject to the same critique as Kostakis, Panigirtzoglou, and Skiadopoulos (2011).…”