We compare the value-at-risk (VaR) bounds obtained from several models fitted to simulated long memory conditional variance processes. We show that most VaR comparison tests and loss functions may lead to the choice of a misspecified model that produces incorrect risk conditional coverage. The...

The purpose of this paper is to bridge two strands of the literature, one pertaining to the objective or physical measure used to model an underlying asset and the other pertaining to the risk-neutral measure used to price derivatives. We propose a generic procedure using simultaneously the...

Multi-period forecasts of stock market return volatilities are often used in many applied areas of finance where long horizon measures of risk are necessary. Yet, very little is known about how to forecast variances several periods ahead, as most of the focus has been placed on one-period ahead...

We examine whether the sign and magnitude of discretely sampled high frequency returns have impact on future volatility predictions. We first let the 'data speak', namely with minimal interference we capture the mapping between returns over short horizons and future volatility over longer...

It is difficult to define news, and many definitions are model-based since part of what is announced is anticipated. Therefore, news is typically defined as a residual within the context of some type of prediction model, and the prediction model locks in the sampling frequency that is the...

We provide theoretical explanations for (1) the empirical stylized fact recognized at least since Taylor (1986) and Ding, Granger, and Engle (1993) that absolute returns show more persistence than squared returns and (2) the empirical funding reported in recent work by Ghysels, Santa-Clara, and...

It is common practice to use the sum of frequently sampled squared returns to estimate volatility, yielding so called realized volatility. Unfortunately, returns are contaminated by market microstructure noise. Several noise-corrected realized volatility measures have been proposed. We assess to...

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a much neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures...

We explore Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider applications in macroeconomics and finance, among other...

This paper introduces a new estimation for the dynamics of betas. It combines two previously separate approaches in the literature, data-driven filters and parametric methods. Namely, we show how to estimate the parametric beta dynamics by instrumental variables combined with block-sampling -...