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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01zw12z858k
Title: Option Trading Strategies to Harvest the Volatility Risk Premium
Authors: Tang, Andrew
Advisors: Hubert, Emma
Department: Operations Research and Financial Engineering
Certificate Program: Finance Program
Class Year: 2023
Abstract: The Volatility Risk Premium is one of the alternative risk premiums that has been widely researched by academia. It is based on empirical evidence that option implied volatility is on average higher than the subsequent realized volatility of the underlying security and thus a premium can be earned by trading options or swaps. The Volatility Risk Premium is negatively skewed with a high kurtosis, and is the compensation from option buyers to sellers for bearing the risk of significant market decline and an increase in realized volatility. This paper complements existing academic research by examining the statistical properties of the Volatility Risk Premium, exploring option trading strategies to harvest the Volatility Risk Premium in the Equity and Interest Rate markets, and backtesting the historical performance of such trading strategies. Specifically, the profitability of selling delta-hedged options and the diversification benefits when combined with traditional risk premiums in an investor's portfolio are studied. The Volatility Risk Premium is time-varying in nature which rises to high levels following extraordinary market events and can fall to low levels and remain low after an extended period of calm markets. This paper also attempts to develop ways to enhance the risk-adjusted performance of the trading strategies by dynamically adjusting the notional size of the options traded based on the percentile rank of the prevailing option implied volatility as an indicator for the magnitude of the Volatility Risk Premium in the subsequent period of time.
URI: http://arks.princeton.edu/ark:/88435/dsp01zw12z858k
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Operations Research and Financial Engineering, 2000-2024

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