The theory of conditional copulas provides a means of constructing flexible multivariate density models, allowing for time-varying conditional densities of each individual variable, and for time-varying conditional dependence between the variables. Further, the use of copulas in constructing...

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal, et al., 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one...

We propose new refined measures of the local covariation between the return on an asset and a risk factor. Our proposed "granular betas" generalize the notion of up- and down-side betas to multi-factor functional measures of covariation. We then show how the resulting granular beta functions may...

We propose a new decomposition of the traditional market beta into four semibetas depending on the signed covariation between the market and individual asset returns. Consistent with the pricing implications from a mean-semivariance framework, we show that higher semibetas defined by negative...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast...

We develop an unobserved components approach to study surveys of forecasts containing multiple forecast horizons. Under the assumption that forecasters optimally update their beliefs about past, current and future state variables as new information arrives, we use our model to extract...

A volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper we outline some stylised facts aboutvolatility that should be incorporated in a model; pronounced persistence and meanreversion, asymmetry such that the...

volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper we outline some stylised facts aboutvolatility that should be incorporated in a model; pronounced persistence and meanreversion, asymmetry such that the sign...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast...