Financial markets embed expectations of central bank policy into asset prices. This paper compares two approaches that extract a probability density of market beliefs. The first is a simulated moments estimator for option volatilities described in [Mizrach, B., 2002. When Did the Smart Money in Enron Lose Its’ Smirk? Rutgers University Working Paper #2002-24]; the second is a new approach developed by [Haas, M., Mittnik, S., Paolella, M.S., 2004a. Mixed normal conditional heteroskedasticity, J. Financial Econ. 2, 211–250] for fat-tailed conditionally heteroskedastic time series. In an application to the 1992–1993 European Exchange Rate Mechanism crises, we find that both the options and the underlying exchange rates provide useful information for policy makers.

Stefan Mittnik, Peter A. Zadrozny, Asymptotic Distributions of Impulse Responses, Step Responses, and Variance Decompositions of Estimated Linear Dynamic Models, Econometrica, Vol. 61, No. 4 (Jul., 1993), pp. 857-870

Financial-market risk, commonly measured in terms of asset-return volatility, plays a fundamental role in investment decisions, risk management and regulation. In this paper, we investigate a new modeling strategy that helps to better understand the forces that drive market risk. We use componentwise gradient boosting techniques to identify financial and macroeconomic factors influencing volatility and to assess the specific nature of their influence. Componentwise boosting is capable of producing parsimonious models from a, possibly, large number of predictors and—in contrast to other related techniques—allows a straightforward interpretation of the parameter estimates. Considering a wide range of potential risk drivers, we apply boosting to derive monthly volatility predictions for the equity market represented by S&P 500 index. Comparisons with commonly-used GARCH and EGARCH benchmark models show that our approach substantially improves out-of-sample volatility forecasts for short- and longer-run horizons. The results indicate that risk drivers affect future volatility in a nonlinear fashion.