This paper evaluates the role of various volatility specifications, such as multiple stochastic volatility (SV) factors and jump components, in appropriate modeling of equity return distributions. We use estimation technology that facilitates non-nested model comparisons and use a long data set...

In this paper we examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules that mimic monetary policymaking decisions. Our approach is to build real-time data sets and simulate a real-time policy-setting environment in which we assume that policy...

Our objective is volatility forecasting, which is core to many risk management problems. We provide theoretical explanations for (i) the empirical stylized fact recognized at least since Taylor (1986) and Ding, Granger, and Engle (1993) that absolute returns show more persistence than squared...

Using a unique data set of detailed balance sheet information on mutual funds, we find that most mutual funds using derivatives do so to a very limited extent that has little discernable impact on returns. However, there exist two types of funds that make more extensive use of derivatives,...

This paper deals with the estimation of the risk-return trade-off. We use a MIDAS model for the conditional variance and allow for possible switches in the risk-return relation through a Markov-switching specification. We find strong evidence for regime changes in the risk-return relation. This...

Policy impact studies often suffer from endogeneity problems. Consider the case of the ECB Securities Markets Programme. If Eurosystem interventions were triggered by sudden and strong price deteriorations, looking at daily (or weekly) price changes may bias downwards the correlation between...

We consider estimating volatility risk factors using large panels of filtered or realized volatilities. The data structure involves three types of asymptotic expansions. There is the cross-section of volatility estimates at each point in time, namely i = 1,…, N observed at dates t = 1,…, T:...

It is well known that temporal aggregation has adverse effects on Granger causality tests. Time series are often sampled at different frequencies. This is typically ignored, and data are merely aggregated to the common lowest frequency. We develop a set of Granger causality tests that explicitly...

Can we design statistical models to predict corporate earnings which either perform as well as, or even better than analysts? If we can, then we might consider automating the process, and notably apply it to small and international firms which typically have either sparse or no analyst coverage....

We examine the effects of mixed sampling frequencies and temporal aggregation on standard tests for cointegration. We find that the effects of aggregation on the size of the tests may be severe. Matching sampling schemes of all series generally reduces size, and the nominal size is obtained when...