Good Volatility, Bad Volatility and Option Pricing

Abstract

Advances in variance analysis permit the splitting of the total quadratic variation of a jump diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions, and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions. To appraise the economic gain of this decomposition, we design a new and flexible option pricing model in which the underlying asset price exhibits distinct upside and downside semi-variance dynamics driven by their model-free proxies. The new model outperforms common benchmarks, especially the alternative that splits the quadratic variation into diffusive and jump components.

Good Volatility, Bad Volatility, and the Cross Section of Stock Returns

“…The paper is also related to the recent work of Feunou, Jahan-Parvar, and Tedongap (2013), Feunou and Okou (2019), Feunou, Lopez Aliouchkin, Tedongap, and Xu (2017), and Feunou, Jahan-Parvar, and Okou (2018) on downside volatility. The last two studies, in particular, rely on similar ideas and techniques to those used here for decomposing both the actual realized volatility and the options implied volatility, and in turn the volatility risk premium defined as the difference between the expected future volatility and the options implied volatility, into up and downside components.…”

Realized bipower variation, jump components, and option valuation

“…Based on the proposed model, we derive a closed-form solution for option pricing under the condition of a nonmonotonic pricing kernel. Our results indicate that the new model has superior option pricing performance to its nested models, including the jump model of Christoffersen et al (2015) and affine realized semivariance model of Feunou and Okou (2019). The models accommodating jumps, high-frequency information, and accounting for variance risk premium perform well compared with traditional benchmark models.…”

Effects of structural changes on the prediction of downside volatility in futures markets

“…Baruník et al (2014, 2016) use the downside‐realized semivariance to measure downside volatility and study volatility spillovers. Feunou and Okou (2016) employ downside‐realized semivariance as the proxy for bad (downside) volatility to analyze S&P 500 index option pricing. It is also used to study volatility forecasting (Chen & Ghysels, 2011; Patton & Sheppard, 2015; Sévi, 2014), cross‐section of stock returns (Bollerslev et al, 2016), time‐varying volatility asymmetry (Ceylan, 2014), and modeling of the volatility–volume relationship (Chevallier & Sévi, 2012).…”