Assessing the Forecast Ability of Risk-Neutral Densities and Real-World Densities from Emerging Markets Currencies

This paper empirically evaluates Risk-Neutral Densities (RND) and Real-World Densities (RWD) as predictors of future outcomes of emerging markets currencies. The dataset consists of volatility surfaces from 11 emerging market currencies, with approximately six years of daily data, using options with one-month expiration. Therefore, there is a strong overlapping in data, which is tackled with specific econometric techniques. Results of the out-of-sample assessment show that both RND and RWD underweight the tails of the actual distribution. This is probably due to the lack of options with extreme strikes. Although the RWDs perform better than RND in terms of Kolmogorov distance, they still have problems in fitting the tails of actual data. Thus, the risk-aversion adjustment may improve the forecast ability, but it does not solve the tails misfitting