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Option-implied moments, like implied volatility, contain useful information about an underlying asset's return distribution but are derived under the risk-neutral probability measure. This paper provides a direct way of converting risk-neutral moments into the corresponding physical moments, which are required for many applications. The main result is a representation of physical moments in terms of observed option prices and a representative investor's preferences. As an empirical application of this result, we provide implied estimates of the representative stock market investor's disappointment aversion using S&P 500 index option prices. We find that disappointment aversion has a procyclical pattern. It is high in times of high index levels and declines when the index falls. We confirm the view that investors with high risk aversion and disappointment aversion leave the stock market during times of turbulence and reenter it after a period of high returns.
In this paper I provide empirical evidence that index options implied higher moments can predict the index returns and Sharpe ratio. Specifically, I present a method to recover option implied subjective moments of the S &P500 index under the assumption of no arbitrage and logarithmic utility. Using index options prices and return data, I test the logarithmic utility assumption and obtain risk aversion estimates not statistically different from one at investment horizons of three to nine months. Under logarithmic utility, I show that the recovered subjective variance has forecasting power controlling for past realized variance. Interestingly, the risk neutral variance is larger than the subjective variance over the entire sample, an empirical fact that quantifies the implied variance premium for a log utility investor. Lastly, I also find that the forward looking Sharpe ratio implied by option prices has forecasting power; this finding can be adopted as a risk--adjusted market timing indicator to improve the return performance of either a passive indexing or a diversified portfolio investment strategy. For example, as a long term investor would rebalance their portfolio periodically to optimize or maintain their asset allocation targets (see for example, cite{ang2014asset}), they could use the option implied Sharpe ratio as a ``gauge'' of the overall market { it price level}. As such, they could take advantage of periods where there is a particularly high expected Sharpe ratio on the market to buy more of the market index when it is at lower valuation levels. Thus, this gauge serves as a reinforcing mechanism to buy low and sell high for periodic portfolio rebalancing.
"This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment suggests that the procedure works well in practice. Implementing the procedure with actual S&P 500 option-implied volatilities and high-frequency five-minute-based realized volatilities results in significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of underlying macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns"--Abstract.
We empirically estimate the option implied coefficient of risk aversion of the market maker for European S&P 500 index options (SPX), involving asset allocation and option market making problems in the presence of proportional transaction costs in trading the underlying asset. We assume that the market maker has constant relative risk aversion utility and holds a two-asset portfolio consisting of the underlying and the riskless asset for a fixed, finite investment horizon which exceeds the option maturity, and she enters a position in the option market with an optimized portfolio. We follow the discrete time approach of Czerwonko and Perrakis (2016a, 2016b) to derive the market maker's simple investment policy and value functions, and apply a value matching condition to find option upper and lower bounds. Data on the S&P 500 index and the SPX options is collected over the period 1996-2016, 244 months in total, and the major variable, volatility, is re-estimated under the physical distribution. By matching observed SPX prices with numerically derived reservation prices, we estimate the level of implied risk aversion. Results show that in general, the market maker has lower risk aversion compared to investors who she trades with in order to accomplish a trade. A pattern that high risk aversion precedes rare market events is also exhibited, suggesting that a market maker may adopt a waiting policy if market events can be anticipated due to the information asymmetry.
Considering a pure exchange economy with habit formation utility, the theoretical part of this dissertation explores the equilibrium relationships between the market pricing kernel, the market prices of risks and the market risk aversion under a continuous time stochastic volatility model completed by liquidly traded put options. We demonstrate with these equilibrium relations that the risk neutral pricing partial differential equation is a restricted version of the fundamental pricing equation provided in Garman (1976). We also show that in this completed market stochastic volatility cannot explain the documented empirical pricing kernel puzzle (Jackwerth (2000)). Instead, a habit formation utility offers a possible explanation of the puzzle. The derived quantitative relation between the market prices of risks and the market risk aversion also provides a new way to extract empirical market risk aversion. Based upon this theoretical relation between market prices of risks and the market risk aversion in a Heston model, we empirically extract the market prices of risks and risk aversion from the options market using cross-sectional fitting. Specifically we consider a restricted model where only the volatility risk is allowed to freely change and an unrestricted model where all model parameters are allowed to freely change. For the restricted model, we determine other parameters by Efficient Method of Moments (EMM). Using European call options data, we find an implied risk aversion smile, indicating that individual groups of investors trading options with different strike prices have different risk aversions. We also extracted an average or aggregated market risk aversion by minimizing the mean squared pricing error across all strikes. This represents the risk aversion level for the whole market in the sense of "averaging". None of these risk aversions are negative across moneyness, hence indicating that adding stochastic volatility to the model will not reproduce the documented pricing kernel puzzle. In addition, the market price of volatility risk is small in values compared with the market price of asset risk, implying that the major driving factor of market risk aversion and pricing kernel is the asset risk. This is consistent with the sensitivity analysis conducted on the option prices with respect to the market prices of risks. For the unrestricted model, we observe similar behavior for the two market prices of risks using a different data set, S&P500 index futures options. We find that the asset risk and volatility risk premium generally move opposite across the strikes. The variation of volatility risk decreases and the absolute values converge to zero with longer time to maturity. So the asset risk dominates the pricing more for options with longer maturities.
This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment confirms that the procedure works well in practice. Implementing the procedure with actual Samp;P 500 option-implied volatilities and high-frequency five-minute-based realized volatilities indicates significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.
Rational investors are in general risk averse. An important implication of this risk aversion is that investors may demand compensation for certain risks they take - risk premiums. Moment risk premiums are an example of such risk premiums. They are defined as the difference between a particular statistical moment of the risk-neutral return distribution and the corresponding moment of the physical return distribution. It is the goal of this dissertation to study the measurement, structure, and investment implications of moment risk premiums in option markets. With respect to the measurement ...