How Does the Market Variance Risk Premium Vary Over Time? Evidence from S&amp;P 500 Variance Swap Investment Returns

Konstantinidi, Eirini; Skiadopoulos, George

doi:10.2139/ssrn.2502822

Cited by 4 publications

(5 citation statements)

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“…The daily returns are obtained from Travis L. Johnson's website, https://www.travislakejohnson.com/data.html. The detailed definitions of two variance‐asset returns are given in the appendix of Johnson (2017b, 2017c).…”

Section: Methodsmentioning

confidence: 99%

“…The S&P 500 options are downloaded from OptionMetrics (2017a, 2017b). The S&P 500 synthetic variance swaps and the S&P 500 straddles are obtained from Johnson's (2017b, 2017c) website at https://www.travislakejohnson.com/data.html. The next‐day closing price of S&P 500 index and the trading volume of S&P 500 futures are from Bloomberg (2017a, 2017b).…”

Section: Data Availability Statementmentioning

confidence: 99%

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Term spreads of implied volatility smirk and variance risk premium

Guo

Ruan

Gehricke

et al. 2023

Journal of Futures Markets

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In this paper, we study the pattern of S&P 500 index options implied volatility (IV) curves and their predictive ability for the variance risk premium (VRP).We explore this predictability employing by the Zhang and Xiang IV factor estimation. We show that the level factor term spread significantly predicts the VRP, proxied by straddle returns and variance swap returns, in both in-sample and out-of-sample tests. The predictability is more pronounced for straddle returns rather than variance swap returns.

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Section: Methodsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Term spreads of implied volatility smirk and variance risk premium

Guo

Ruan

Gehricke

et al. 2023

Journal of Futures Markets

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“…Extracting distribution information from option prices, like higher moments, has a long history, let us quote without pretending to be exhaustive the work of Carr and Madan (), and has found many applications; see among many others the works of Bakshi et al () for individual options; Bakshi and Madan () and Carr and Wu ()) for a variance risk premium analysis of equity index options (options on S&P500, S&P100 and other major indexes as well as equity); Byun and Kim (), Fleming (), Konstantinidi and Skiadopoulos (), and Neumann and Skiadopoulos () for forecasting aspects (using S&P500 options); Ammann and Buesser () for variance risk premium properties for the foreign exchange market; investors’ risk aversion analysis as in Duan and Zhang (); Kostakis, Panigirtzoglou, and Skiadopoulos () (using S&P500 options); and variance risk premiums in commodity markets in Prokopczuk, Symeonidis and Wese‐Simen ().…”

Section: Pricing Formulasmentioning

confidence: 99%

Variance and skew risk premiums for the volatility market: The VIX evidence

Fonseca

2018

Journal of Futures Markets

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We extract variance and skew risk premiums from volatility derivatives in a model‐free way and analyze their relationships along with volatility index and equity index returns. These risk premiums can be synthesized through option trading strategies. Using a time series of option prices on the VIX, we find that variance swap excess return can be partially explained by volatility index and equity index excess returns while these latter variables carry little information for the skew swap excess return. The results sharply contrast with those obtained for the equity index option market underlining very specific characteristics of the volatility derivative market.

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“…A number of studies have examined the predictive power of several economic variables for equity returns and second moment of distribution (e.g. Harvey and Whaley, 1992 for the S&P 100 market; Schwert, 1989, Glosten, Jagannathan and Runkle, 1993, Perez-Quiros and Timmermann, 2001 for the volatility; Bakshi and Madan, 2006, Konstantinidi and Skiadopoulos, 2013and Feunou et al, 2014 for the volatility risk premium; Sheppard, 2008 andPalandri, 2009 for correlations). Based on these findings, the ability of factors related to general macroeconomic as well as stock market specific conditions to explain the time variation and predict the correlation risk premium is explored.…”

Section: Economic Determinants and Time Variation Of Crpmentioning

confidence: 99%

“…All variables are obtained from Datastream. Konstantinidi and Skiadopoulos (2013) propose the incorporation of the TED spread measured as the difference of the three-month Libor and the three-month Treasury bill. However, tests for multicollinearity of dependent values have suggested, as expected, high correlation of the TED spread with the yield curve slope and the Libor.…”

Section: Economic Determinants and Time Variation Of Crpmentioning

confidence: 99%

Correlation modelling with application to risk management

Markopoulou¹,

Μαρκοπούλου²

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Accurate estimation and prediction of correlation is of paramount importance in asset allocation, risk management and hedging applications, particularly in light of recent studies that provide evidence of increased correlation during periods of high volatility, leading to diminishing diversification benefits in states of nature that are most needed. The time-variability of the correlation process has fuelled extensive literature on dynamic correlation modelling. In an attempt to depart from correlation estimation based on projections from historical data, two alternative measures of correlation, namely the implied and the realized correlation, have been proposed in the recent literature. Remarkably, in contrast to volatility estimates, existing studies on the informational efficiency and forecasting performance of respective correlation measures are rather limited.This thesis focuses on exploring the dynamics that govern the evolution of correlation risk premium and its components, namely implied and realized correlation, and assessing the impact of predictability to portfolio allocation, hedging and trading decisions. First, the time-variation and certain distribution characteristics of the correlation risk premium, defined as the difference of realized and implied correlation, are examined. The information content of market –specific and macroeconomic variables, which have been previously reported as proxies of the business cycles, in predicting future premium is also evaluated. Secondly, a model-free measure of implied correlation is proposed and the question of predictable dynamics in the evolution of the series is investigated both in statistical and economic terms. A trading strategy designed to exploit daily changes of the series sets the foundation for addressing the efficient market hypothesis. Finally, based on the distributional properties of realized volatility, correlation and hedge ratio, an alternative forecasting methodology is applied to predictthe realized hedge ratio and to explore the additive value of intraday data in a dynamichedging context while the hedging performance is compared in terms of portfoliooptimization and risk management.The thesis has reached a number of conclusions. First, correlation and correlationrisk premium vary substantially over time and increase sharply during turbulent periods,while culminated during the Asian and Russian financial crisis in 1997-1998 and thesubprime mortgage crisis of 2007-2009. The previously documented correlation riskpremium is no longer significant during the recent 2007 – 2009 crisis, suggesting thedisappearance of arbitrage opportunities. Secondly, the predictability of model-freeimplied correlation series suggested by statistical measures cannot be exploited in termsof economic gains, suggesting that the S&P 100 options market is efficient. Finally,forecasting the dynamics of the realized hedge ratio directly reveals predictable patternsin the evolution of the hedge ratio, resulting in improved hedging performance, in termsof both economics gains and risk measures.

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How Does the Market Variance Risk Premium Vary Over Time? Evidence from S&P 500 Variance Swap Investment Returns

Cited by 4 publications

References 66 publications

Term spreads of implied volatility smirk and variance risk premium

Term spreads of implied volatility smirk and variance risk premium

Variance and skew risk premiums for the volatility market: The VIX evidence

Correlation modelling with application to risk management

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