Parameter estimation and model testing for Markov processes via conditional characteristic functions

Markov processes are used in a wide range of disciplines, including finance. The transition densities of these processes are often unknown. However, the conditional characteristic functions are more likely to be available, especially for L\'{e}vy-driven processes. We propose an empirical likelihood approach, for both parameter estimation and model specification testing, based on the conditional characteristic function for processes with either continuous or discontinuous sample paths. Theoretical properties of the empirical likelihood estimator for parameters and a smoothed empirical likelihood ratio test for a parametric specification of the process are provided. Simulations and empirical case studies are carried out to confirm the effectiveness of the proposed estimator and test.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ400 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Are Crop Yield Distributions Negatively Skewed? A Bayesian Examination

Crop Production/Industries,

Speculation and Volatility Spillover in the Crude Oil and Agricultural Commodity Markets: A Bayesian Analysis

This paper assesses the roles of various factors influencing the volatility of crude oil prices and the possible linkage between this volatility and agricultural commodity markets. Stochastic volatility models are applied to weekly crude oil, corn and wheat futures prices from November 1998 to January 2009. Model parameters are estimated using Bayesian Markov chain Monte Carlo methods. The main results are as follows. Speculation, scalping, and petroleum inventories are found to be important in explaining oil price variation. Several properties of crude oil price dynamics are established including mean-reversion, a negative correlation between price and volatility, volatility clustering, and infrequent compound Poisson jumps. We find evidence of volatility spillover among crude oil, corn and wheat markets after the fall of 2006. This could be largely explained by tightened interdependence between these markets induced by ethanol production.Gibbs sampling, Merton jump, leverage effect, stochastic volatility, Demand and Price Analysis, Financial Economics, Resource /Energy Economics and Policy, G13, Q4,

Mcmc Estimation Of Lévy Jump Models Using Stock And Option Prices

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136259/1/j.1467-9965.2010.00439.x.pd

Return Dynamics with Levy Jumps: Evidence from Stock and Option Prices

We examine the performances of Levy jump models and affine jump-diffusion models in capturing the joint dynamics of stock and option prices. We discuss the change of measure for infinite-activity Levy jumps and develop efficient Markov chain Monte Carlo methods for estimating model parameters and latent volatility and jump variables using stock and option prices. Using daily returns and option prices of the S&P 500 index, we show that models with infinite-activity Levy jumps in returns significantly outperform affine jump-diffusion models with compound Poisson jumps in returns and volatility in capturing both the physical and the risk-neutral dynamics of the S&P 500 index