The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed income applications. We investigate if the unscented Kalman filter should be used to capture nonlinearities, and compare the performance of the Kalman filter to that...

Forecasting the evolution of security co-movements is critical for asset pricing and portfolio allocation. Hence, we investigate patterns and trends in correlations over time using weekly returns for developed markets (DMs) and emerging markets (EMs) during the period 1973-2012. We show that it...

The cross-section of stock returns has substantial exposure to risk captured by higher moments in market returns. We estimate these moments from daily S&P 500 index option data. The resulting time series of factors are thus genuinely conditional and forward-looking. Stocks with high...

Characterizing the dependence between companies' defaults is a central problem in the credit risk literature, and the dependence structure is a first order determinant of the value of credit portfolios and structured credit products such as collateralized debt obligations (CDO), as well as the...

We specify and estimate no-arbitrage models that value sovereign CDS contracts by assuming that the country's default intensity depends on observable economic and financial indicators. We estimate these models using a sample of twenty-eight countries, three CDS maturities, and over a decade of...

We propose no-arbitrage term structure models in which the volatility factors followGARCH processes. The models’ tractability is similar to that of canonical affine termstructure models, but they capture the conditional variances of yields much more accurately.We estimate a model with one...

The cross-section of options holds great promise for identifying return distributions and risk premia, but estimating dynamic option valuation models with latent state variables is challenging when using large option panels. We propose a particle MCMC framework with a novel filtering approach...

Rather than assuming a fixed recovery rate in estimation, we estimate recovery rates from CDS spreads, using three years of daily data on 152 corporates. We use a quadratic pricing model which ensures nonnegative default probabilities and recovery rates. The estimated cross-section of recovery...

Observable covariates are useful for predicting default under the natural measure, but several findings question their value for explaining credit spreads under the pricing measure. We introduce a discrete time no-arbitrage model with observable covariates, which allows for a closed form...

Many studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We investigate whether these forecasting improvements translate into economic value added. To do so we...