Expected stock return and conditional skewness

Motivated by the parsimonious jump-diffusion model of Zhang, Zhao and Chang (2010), we show that the aggregate market returns can be predicted by the conditional skewness of returns and the variance risk premium, a difference between the physical and risk-neutral variance of market returns, even though the variance is supposed to be constant only if jump exists. The magnitude of the predictability is particularly striking at the intermediate quarterly return horizon, even combing other predictor variables, like P/D ratio, the default spread and the consumption-wealth ratio (CAY). We also find that the third central moments are significant in explaining the variance risk premium, which further implies that the potential link between the variance risk premium and the excess market return is the third central moments, not the skewness.postprintThe 9th China International Conference in Finance (CICF 2011), Wuhan, China, 4-7 July 2011

Expected stock returns and the conditional skewness

Motivated by the parsimonious jump-di®usion model of Zhang, Zhao and Chang (2010), we show that the aggregate market returns can be predicted by the conditional skewness of returns and the variance risk premium, a di®erence between the physical and risk-neutral variance of market returns, even though the variance is supposed to be constant only if jump exists. The magnitude of the predictability is particularly striking at the intermediate quarterly return horizon, even combing other predictor variables, like P/D ratio, the default spread and the consumption-wealth ratio (CAY). We also ¯nd that the third central moments are signi¯cant in explaining the variance risk premium, which further implies that the potential link between the variance risk premium and the excess market return is the third central moments, not the skewness.postprintThe 2011 China International Conference in Finance, Wuhan, China, 4-7 July 2011.2011中国金融国际年会, 中国, 武汉, 2011年7月4日至7日

Can Derivative Information Predict Stock Price Jumps?

This study examines the predictability of jumps in stock prices using options-trading information, the futures basis spread, the cross-sectional standard deviation of returns on components in the stock index, and exchange rates. A stock price jump was defined as a large fluctuation in the stock price that deviated from the distribution thresholds of the past rates of return. This empirical analysis shows that the implied volatility spread between ATM call and put options was a significant predictor for both upward and downward jumps, whereas the volatility skew was less significant. In addition, the futures basis spread was moderately significant for downward stock price jumps. Both the cross-sectional standard deviation of the rates of return on component stocks in the KOSPI 200 and the won-dollar exchange rates were significant predictors for both upward and downward jumps

Market excess returns, variance and the third cumulant

In this paper, we develop an equilibrium asset pricing model for the market excess return, variance and the third cumulant by using a jump-diffusion process with stochastic variance and jump intensity in Cox, Ingersoll and Ross' (1985) production economy. Empirical evidence with S&P 500 index and options from January 1996 to December 2005 strongly supports our model prediction that lower the third cumulant, higher the market excess returns. Consistent with existing literature, the theoretical mean-variance relation is supported only by regressions on risk-neutral variance. We further demonstrate empirically that the third cumulant explains significantly the variance risk premium.published_or_final_versio

Volatility derivatives: Expected option returns

This thesis establishes how option returns are influenced by the underlying index volatility. To elaborate it, call, put and straddle options of the Standard & Poor’s 500 index were object of study. The elaboration of this study focused, mainly, on the Black-Scholes/Capital Asset Pricing Model, and, along it, was found some curious facts that contradict previous conclusions collected for this theme. Either way, for zero-beta at-the-money straddle options the expected returns obtained were negative, contradicting Black-Scholes/Capital Asset Pricing Model assumptions. The results indicate that in addition to market risk there is another risk associated with option contracts pricing.Este trabalho teve como principal preocupação estabelecer a ligação entre os retornos das opções e a volatilidade do índice subjacente. Por outras palavras, compreender se e como ambos os componentes se influenciam. Foram estudados diferentes tipos de opções, como a opção de compra, a opção de venda e a opção straddle (conjugação de ambas), tendo como base o índice Standard & Poor’s 500. A elaboração deste estudo foca-se maioritariamente no Modelo de Precificação de Ativos Financeiros, mais conhecido por Capital Asset Pricing Model e, ao longo do mesmo, diversos factos que contradizem conclusões já alcançadas por outros autores para este tema foram possíveis de provar diversos factos que contradizem conclusões já alcançadas por outros autores para este tema. Os resultados obtidos, especialmente para as opções straddle beta-zero, contrariam as premissas de Black-Scholes/Modelo de Precificação de Ativos Financeiros uma vez que os retorns esperados obtidos foram negativos. Desta forma, os mesmos indicam que, para além do risco de mercado, existe outro tipo de risco associado ao preço dos contratos das opções

Volatility spreads and earnings announcement returns

Prior research documents that volatility spreads predict stock returns. If the trading activity of informed investors is an important driver of volatility spreads, then the predictability of stock returns should be more pronounced during major information events. This paper investigates whether the predictability of equity returns by volatility spreads is stronger during earnings announcements. Volatility spreads are measured by the implied volatility differences between pairs of strike price and expiration date matched put and call options and capture price pressures in the option market. During a two-day earnings announcement window, the abnormal returns to the quintile that includes stocks with relatively expensive call options is more than 1.5 percent greater than the abnormal returns to the quintile that includes stocks with relatively expensive put options. This result is robust after measuring volatility spreads in alternative ways and controlling for firm characteristics and lagged equity returns. The degree of announcement return predictability is stronger when volatility spreads are measured using more liquid options, the information environment is more asymmetric, and stock liquidity is low

Option-implied information and return prediction

We prove the existence of a negative variance risk premium for major US stock indexes and stocks, except for relatively high market capitalization stocks. A zero net investment strategy based on log variance risk premium yields an annualized Sharpe ratio of 0.38 and an annualized certainty equivalent of 4.68%. We find that both the log variance risk premium and option-implied betas are negatively priced in contemporaneous and future returns, which is counter intuitive for option-implied betas, given the expected risk return relation

Recent Volatility in U.S. Equity Markets: A Review of Key Contributing Factors and Relationships

This paper is a review of volatility trends, factors, and relationships in U.S. equity markets, with emphasis on the period of time from 1980 to the present, when volatility has been at higher levels than what had been observed earlier. Both finance academics and investment professionals are affected by this ‘high-volatility’ environment, as it impacts the traditional relationships that connect risk and return, and can therefore alter both individual asset and portfolio allocation decisions. Based on a thorough review of the literature on a stock’s idiosyncratic volatility, we explain why it has increased in recent times, discuss factors that affect volatility level, and provide an overview of the empirical relationship between current volatility levels and future expected return. At the end of each section, we pose a related idea for future research – there are ten such ideas offered. The primary purposes of the paper are to convince the reader that volatility is an important investment consideration, to identify the major findings in recent volatility research, and to highlight some unanswered volatility questions for future academics and practitioners to explore

Essays on Return and Volatility on World Stock Markets

This research is mainly focused on investigating volatility dynamics of world stock returns. More specifically, the main goal is to capture co-movements and analyze dynamic transmission mechanisms of volatility of stock returns across the world. Understanding the mechanisms linking international equity markets is important for not only policymakers but also fund managers who make investment decisions based on the international risk diversification. But existence of co-movements in world stock markets is lack of evidence in the existing literature. Chapter 1 gives a detailed literature review and clarifies the marginal contribution of this research. The chapter begins with introducing the importance of related research on this topic. Secondly, a number of influential literatures on the related field are reviewed. It shows that the existing literature is not able to capture a clear trend of co-movement across world stock markets. The problem could be resulted from model selections, data construction, and sample sizes and etc. Those questions are addressed in this dissertation research. In Chapter 2, co-movements across worldwide stock markets are investigated. A dynamic factor model is designed to decompose stock return volatility into three orthogonal factors: the world factor, the regional factor and the local factor. The three factors are assumed to be well suited for explaining all the variation of volatility. Fourteen countries are included in the empirical study in order to cover both developed and emerging stock markets. The historical volatility growth decomposition is conducted to analyze contributions made by different factors to the volatility growth for each market. The results show that there exist co-movements which are able to account for more than 50% of variation of volatility for most of countries. The world factor turns out to be significant for North American and Latin American markets; nevertheless the regional factor is important for Europe and Asia. In Chapter 3, a modified dynamic factor model is conducted to investigate spill-over effects between different stock markets or regions. It begins with examining the dominant position of the U.S. in world stock markets, followed by analysis on the effect of U.S. stock market on Asian markets. Linkage between Asian stock markets and Latin American markets are also investigated. Moreover, the author extended the time horizon and adjusted the sample of countries in order to examine effects of financial integration on world stock markets. The results show that the dominance of the U.S. stock market in world stock markets has been getting weaker since international financial markets became more integrated. Emerging stock markets have become more independent of developed markets after financial globalization

Risk-Neutral Skewness, Informed Trading, and the Cross Section of Stock Returns

AbstractIn this article, we use volatility surface data from options contracts to document a strong, robust, and positive cross-sectional relation between risk-neutral skewness (RNS) and subsequent stock returns. The differential return between high- and low-RNS stocks amounts to 0.17% per week. Preannouncement RNS is positively related to earnings announcement returns, and the positive RNS&ndash;return relation is more pronounced for other nonscheduled news releases. This suggests that it is informed trading that drives the positive relation between RNS and subsequent stock returns. We also find that RNS contains incremental information beyond trading signals captured by option-implied volatility and volume.</jats:p