Harvesting the volatility smile in a large emerging market: A Dynamic Nelson-Siegel approach

While there is a large literature on modeling volatility smile in options markets, most such studies are eventually focused on the forecasting performance of the model parameters and not on the applicability of the models in a trading environment. Drawing on the analogy of volatility smile like a term structure in the context of interest rates in fixed-income markets, we evaluate the performance of the Dynamic Nelson-Siegel (DNS) approach to modeling the dynamics of volatility smile in a trading environment against competing alternatives. Using model-based mispricing as our sorting criterion, and deploying a trading strategy of going long the options in the upper deciles and going short the options in the lower deciles, we show that dynamic models consistently outperform their static counterparts, with the worst dynamic model outperforming the best static model in terms of the percentage of mean returns from the trading portfolios and the Sharpe ratio. Specifically, we find that the DNS model consistently outperforms all other competing specifications on most of our selected criteria.