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This paper examines model specification issues and estimates diffusive and jump risk premia using S&P futures option prices from 1987 to 2003. We first develop a time series test to detect the presence of jumps in volatility, and find strong evidence in support of their presence. Next, using the cross section of option prices, we find strong evidence for(More)
This article develops a direct filtration-based maximum likelihood methodology for estimating the parameters and realizations of latent affine processes. Filtration is conducted in the transform space of characteristic functions, with a version of Bayes' rule used for recursively updating the joint characteristic function of latent variables and the data(More)
This paper provides an empirical analysis of the role of jumps in continuous-time models of the short rate. Statistically, if jumps are present di¤usion models are mis-speci…ed and I develop a test to detect jump-induced misspeci…cation. After …nding evidence for jumps, I introduce a nonparametric jump-di¤usion model and develop an estimation methodology.(More)
In this paper we empirically compare different term structure models when it comes to the pricing and hedging of caps and swaptions. We analyze the influence of the number of factors on the pricing and hedging results, and investigate which type of data !interest rate data or derivative price data! should be used to estimate the model parameters to obtain(More)
Particle learning provides a simulation-based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive and propagation rule to build a resampling-sampling framework. Predictive inference and sequential Bayes factors are a direct by-product. Our approach(More)
This paper analyzes sequential learning in the context of predictive regression models. To do this, we develop new particle based methods for sequential learning about parameters, state variables, hypotheses, and models. This sequential perspective allows us to quantify how investor's views about predictability and models varies over time, and naturally(More)
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State(More)
This paper provides a methodology for computing optimal filtering distributions in discretely observed continuous-time jump-diffusion models. Although it has received little attention, the filtering distribution is useful for estimating latent states, forecasting volatility and returns, computing model diagnostics such as likelihood ratios, and parameter(More)