This paper examines the joint time series of the Samp;P 500 index and near-the-money short-dated option prices with an arbitrage-free model, capturing both stochastic volatility and jumps. Jump-risk premia uncovered from the joint data respond quickly to market volatility, becoming more...

In this study we examine a causal relationship between series of returns and traded volumes in high-frequency data. Our analysis is based on the methodology of Ghysels, Gourieroux and Jasiak (2000), who develop a qualitative framework in which dynamics of financial series are restricted to...

Realized volatility is a nonparametric ex-post estimate of the return variation. The most obvious realized volatility measure is the sum of finely-sampled squared return realizations over a fixed time interval. In a frictionless market the estimate achieves consistency for the underlying...

This paper applies recently developed procedures to monitor and date so-called "financial market dislocations", defined as periods in which substantial deviations from arbitrage parities take place. In particular, we focus on deviations from the triangular arbitrage parity for exchange rate...

In this paper we present both a new formulation of the HARCH process and a study of the forecasting accuracy of ARCH-type models for predicting short-term volatility. Using high frequency data, the market volatility is expressed in terms of partial volatilities which are formally exponential...

This study suggests an alternative method to estimate time-varying country risk. We first apply a new multivariate stochastic volatility (SV) model to a set of emerging stock markets. To estimate the SV model, we use a Bayesian Markov chain Monte Carlo simulation procedure. By applying the...

This paper introduces a solution that combines the Kalman and particle fi lters to the challenging problem of estimating integrated volatility using high-frequency data where the underlying prices are perturbed by a mixture of random noise and price discreteness. An explanation is presented of...

We represent risk factors as sums of orthogonal components capturing fluctuations with cycles of different length. The representation leads to novel spectral factor models in which systematic risk is allowed (without being forced) to vary across frequencies. Frequency-specific systematic risk is...

This paper examines the effect of presidential cycles on financial market correlations using monthly data for the U.S. stock and government bond returns over the historical period of 1791:09-2017:12. Utilizing a dynamic conditional correlation generalized autoregressive conditional...

This paper considers Markov error-correction (MEC) models in which deviations from the long-run equilibrium are characterised by different rates of adjustment. To motivate our analysis and illustrate the various issues involved, our discussion is structured around the analysis of the long-run...