International crash risk premium Chen, Steven Shu-Hsiu
Journal of international financial markets, institutions & money,
July 2024, 2024-07-00, Volume:
94
Journal Article
Peer reviewed
This paper investigates the international crash risk and the cross-section of stock index returns. We use the ex-ante model-free negative skewness measured by country-specific index options, proposed ...in Bakshi et al. (2003), as a proxy of the crash risk. We find that a country’s stock index with a high crash risk relates to a higher stock return as a risk premium across countries. The international crash risk premium exists robustly after controlling for volatility risk, macroeconomic variables, sensitivities to the international risk factors, and realized return moments. In contrast, other international risk premiums do not exist based on the exposure of such control variables. Based on the crash risk premium, we construct international stock trading strategies by sorting option-implied skewness across countries that outperform benchmark strategies by sorting the above control variables.
•A strong positive relationship exists between crash risk and the cross-section of subsequent index returns across countries, indicating the existence of an international crash risk premium.•The international stock portfolio exposed to crash risk earns economically and statistically meaningful risk premiums in the cross-section.•The international crash risk premium exists robustly after controlling for common international risk sources and characteristics, including volatility risk, macroeconomic variables, sensitivities to the international risk factors, and realized return moments.•By sorting crash risk or option-implied skewness, international stock trading strategies across countries outperform benchmark strategies by sorting the above control variables.
We compare risk-neutral densities from equity index options across several countries during the early phase of the COVID-19 pandemic. The initial reaction in all analyzed markets was late, abrupt and ...simultaneous. Only a few weeks later, densities started to differ across markets.
•We use risk-neutral densities to measure market expectations regarding COVID-19.•Markets reacted late after ignoring several warnings by the World Health Organization.•Stock indices reacted strongly and simultaneously after the Lombardy lockdown.•From mid-March onwards, risk-neutral densities develop differently across countries.
We develop deep learning models to learn the hedge ratio for S&P500 index options from options data. We compare different combinations of features and show that with sufficient training data, a ...feedforward neural network model with time to maturity, the Black-Scholes delta and market sentiment as inputs performs the best in the out-of-sample test under daily hedging. This model significantly outperforms delta hedging and a data-driven hedging model. Our results also demonstrate the importance of market sentiment for hedging.
State-of-the-art stochastic volatility models generate a "volatility smirk" that explains why out-of-the-money index puts have high prices relative to the Black-Scholes benchmark. These models also ...adequately explain how the volatility smirk moves up and down in response to changes in risk. However, the data indicate that the slope and the level of the smirk fluctuate largely independently. Although single-factor stochastic volatility models can capture the slope of the smirk, they cannot explain such largely independent fluctuations in its level and slope over time. We propose to model these movements using a two-factor stochastic volatility model. Because the factors have distinct correlations with market returns, and because the weights of the factors vary over time, the model generates stochastic correlation between volatility and stock returns. Besides providing more flexible modeling of the time variation in the smirk, the model also provides more flexible modeling of the volatility term structure. Our empirical results indicate that the model improves on the benchmark Heston stochastic volatility model by 24% in-sample and 23% out-of-sample. The better fit results from improvements in the modeling of the term structure dimension as well as the moneyness dimension.
I develop a new spectrum of moment bounds on the pricing kernel. They stem from the solution of an optimization problem that is complementary to Hansen and Jagannathan (1991) approach. Economically, ...they measure the discrepancy between what an optimizing agent could achieve if all assets (that are priced by the pricing kernel) were tradable and what she can actually achieve in the real-world market. Through the lens of these bounds, I examine leading macro-finance models using index option returns. I show, in a model-free fashion, the difficulty of several classes of models in meeting option-implied bounds. I highlight the unique information that my bounds provide compared with existing approaches.
Many studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We ...investigate whether these forecasting improvements translate into economic value added. To do so, we develop a new class of affine discrete-time option valuation models that use daily returns as well as realized volatility. We derive convenient closed-form option valuation formulas, and we assess the option valuation properties using Standard & Poor’s (S&P) 500 return and option data. We find that realized volatility reduces the pricing errors of the benchmark model significantly across moneyness, maturity, and volatility levels.
This study employs the meshfree radial basis point interpolation (RBPI) for pricing real estate derivatives contingent on real estate index. This method combines radial and polynomial basis ...functions, which can guarantee the interpolation scheme with Kronecker property and effectively improve accuracy. An exponential change of variables, a mesh refinement algorithm and the Richardson extrapolation are employed in this study to implement the RBPI. Numerical results are presented to examine the computational efficiency and accuracy of our method.
•We model the real estate index options within the arbitrage-free framework.•We consider the non-tradable and the mean-reverting characteristics of the index.•We employ the radial basis point interpolation to price of real estate index options.•Both European and American options contingent on real estate index can be priced.
We study options market participants' trading behaviour before and after the options multiplier increases. After the options multiplier increases, the options market becomes more efficient. By ...analysing the high-frequency microstructure dataset, we show that local retail and local institutional investors who trade in both options and futures markets trade more after the change in the multiplier. Our results imply that the increase in the market efficiency may be caused by fewer speculators. In addition, lottery stocks are traded more actively after the options multiplier increase.
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