The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks - Brent in Europe and WTI in America are compared. OVX index is...

This paper presents a new procedure for forecasting recessions utilizing short-term (slope) dynamics present in the yield curve. Building on a large body of literature chronicling the relationship between the shape of the yield curve and the business cycle, this paper employs Dynamic...

The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long term dynamics in the conditional (co)volatilities of asset returns, in line with the empirical evidence suggesting that their level is changing over time as a function of economic conditions. Herein...

The OGARCH specification is the leading model for a class of multivariate GARCH (MGARCH) models that are based on linear combinations of univariate GARCH specifications. Most MGARCH models in this class adopt a spectral decomposition of the covariance matrix, allowing for heteroskedasticity on...

Several unique data sets are brought together to build approximate daily realized volatility estimates back to the early 1930's. Estimators are tested extensively on modern data to see how well they line up with common estimators using high frequency pricing information. Estimators are also...

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...

This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the...

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

Should long-term investors account for time-variation in model parameters? We develop a time-varying Vector Autoregressive model that can handle time-variation in intercepts, slopes, volatility and correlation, the leverage effect in volatility and fat tails. Long-term investors should take...