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The Information Content of Decomposed Implied Volatility and Skewness Measures

Bevilacqua, Mattia (2019) The Information Content of Decomposed Implied Volatility and Skewness Measures. Doctor of Philosophy (PhD) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

This research investigates how decomposed forward-looking measures extracted from equity options in the U.S. contribute to a more directional understanding of the financial market volatility characteristics and their connectedness. It shows how different components of implied volatility and skewness, namely, upside or positive and downside or negative, extracted only from call options and put options, respectively, might contain a more refined set of information compared to the aggregate measures. The information enclosed in the decomposed components of risk measures is able to enrich the set of financial tools which market participants and investors have at their disposal. The new set of directional information can be most certainly used to improve financial stability and predict future economic activity or future levels of uncertainty indicators. Overall, our empirical findings suggest that uncertainties related to upside or downside measures, proxies for good or bad market events and news, can be used to achieve better asset pricing and equity market premium predictability as well as a better understanding of the volatility connectedness in the financial markets.

When decomposed, implied volatility measures contain different information able to provide new insights of the separate determinants of volatilities among the macroeconomic and financial variables. The same is found to be true when risk-neutral and physical measures are joined together to compute the volatility risk premia. By decomposing implied volatility measures, this study also shows that by doing this enables new light to be shed in the financial connectedness area through examining asymmetric volatility characteristics. Lastly, implied volatility measures extracted from single stocks in the U.S. financial sector further confirm the asymmetry characteristics in connectedness. This enhances the understanding of the financial institutions network and how this can be used for future economic activity and future levels of uncertainty predictability. Lastly, we show that different components of the implied skewness can be used to improve financial stability, provide a more prudent measure of tail risk, and contribute to asset pricing and uncertainty predictability.

The first chapter focuses only on the volatility series which is, however, considered both in its implied measure, realized measure and, merging the two, in its risk premium. Thus, both implied and realized volatility are computed model-free and decomposed into upside and downside components, thereby allowing us to compute volatility risk premia accordingly. The chapter analyzes the role of macroeconomic and financial determinants in explaining stock market volatility in the U.S. market. We distinguish the behaviour of each component of the implied volatility and risk premium in relation to their different determinants. The downside implied volatility appears to be linked more towards uncertainty and geopolitical risk indexes, whereas upside implied volatility is driven more by consumption and GDP. A mixed frequency Granger causality approach uncovers causality relationships between volatilities and risk premia and macro variables and vice versa, a finding which is not detected with a conventional low-frequency VAR model. In the second chapter, we disentangle implied skewness related to downward movements from the implied skewness associated with upward movements of the U.S. equity index. We decompose the implied skewness measure into its positive and negative components. The positive SKEW index is extracted from the S&P 500 call options, whereas the negative SKEW index is extracted from the S&P 500 put options. The information content of our measures is not captured by other risk and tail risk measures. They provide an additional powerful tool next to the second moment index and its components which might further improve asset pricing and economic predictability. The positive SKEW index is more connected with market sentiment indicators, whereas the negative SKEW index is proposed as a tail risk measure. We show that the decomposed SKEW measures are useful in predicting the S&P 500 as well as individual stocks future equity risk premium, mainly in the short-term. The decomposed SKEW indexes are also informative when added to Fama-French asset pricing models. We show that the negative SKEW can play a significant role in predicting uncertainty indicators up to a one-year horizon. In the last chapter, single stocks implied volatilities are decomposed for the U.S. financial sector showing how they might be input, separately, into connectedness indexes with the aim of increasing their predictability power for macroeconomic and uncertainty indicators. Basically, we study how shocks to forward-looking expectations of investors buying call and put options transmit across the financial system. We introduce a new contagion measure named asymmetric fear connectedness (AFC) that captures the information related to "fear" in the two sides of the options market, that can be used as a forward-looking systemic risk monitoring tool. The decomposed connectedness measures provide timely predictive information for near future macroeconomic conditions and uncertainty indicators and they contain additional valuable information not enclosed in the aggregate connectedness measure.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Tunaru, Radu
Thesis advisor: Morelli, David
Uncontrolled keywords: Financial Economics, Asset Pricing, Volatility, Contagion, MacroFinance, Financial Econometrics
Subjects: H Social Sciences > HF Commerce > HF5351 Business
Divisions: Faculties > Social Sciences > Kent Business School
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 06 Aug 2019 16:10 UTC
Last Modified: 07 Aug 2019 08:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/75687 (The current URI for this page, for reference purposes)
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