The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

We build a new class of discrete-time models that are relatively easy to estimate using
returns and/or options. The distribution of returns is driven by two factors: dynamic volatility …

Using index options and returns from 1996 to 2009, I estimate discrete-time models where
asset returns follow a Brownian increment and a Lévy jump. Time variations in these models …

We attempt to answer two questions in this paper:(i) How did jumps in equity returns change
after the 2008–2009 financial crisis—in particular, were there significant changes in jump …

Relatively little is known about the empirical performance of infinite-activity Lévy jump
models, especially with non-affine volatility dynamics. We use extensive empirical data sets …

Over the past few years, the electricity derivatives market has experienced a substantial
growth in the volume of trade and the diversity of available products. This has led to a rich …

This paper studies how to utilize individual ratings and credit performance for portfolio credit
risk analysis and surveillance. We model the default intensity of firms using a proportional …

We examine time-varying jump risk for modeling stock price dynamics and cross-sectional
option prices. We explore jump-diffusion specifications with two independently evolving …

The purpose of this paper is to apply the stochastic volatility model driven by infinite activity
Lévy processes to option pricing which displays infinite activity jumps behaviors and time …

We propose a methodology to measure the parameter estimation risk and model
specification risk of pricing models, as well as model selection risk of model classes, based …