Generalized Method of Moments Estimator Based On Semiparametric Quantile Regression Imputation

In this article, we consider an imputation method to handle missing response values based on semiparametric quantile regression estimation. In the proposed method, the missing response values are generated using the estimated conditional quantile regression function at given values of covariates. We adopt the generalized method of moments for estimation of parameters defined through a general estimation equation. We demonstrate that the proposed estimator, which combines both semiparametric quantile regression imputation and generalized method of moments, has competitive edge against some of the most widely used parametric and non-parametric imputation estimators. The consistency and the asymptotic normality of our estimator are established and variance estimation is provided. Results from a limited simulation study and an empirical study are presented to show the adequacy of the proposed method

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

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,

Are Crop Yield Distributions Negatively Skewed? A Bayesian Examination

Crop Production/Industries,

A calibration experiment in a longitudinal survey with errors-in-variables

The National Resources Inventory (NRI) is a large-scale longitudinal survey conducted to assess trends and conditions of nonfederal land. A key NRI estimate is year-to-year change in acres of developed land, where developed land includes roads and urban areas. In 2003, a digital data collection procedure was implemented replacing a map overlay. Data from an NRI calibration experiment are used to estimate the relationship between data collected under the old and new protocols. A measurement error model is postulated for the relationship, where duplicate measurements are used to estimate one of the error variances. If any significant discrepancy is detected between new and old measures, some parameters that govern the algorithm under new protocol can be changed to alter the relationship. Parameters were calibrated so overall averages nearly match for the new and old protocols. Analyses on the data after initial parameter calibration suggest that the relationship is a line with an intercept of zero and a slope of one, therefore the parameters currently used are acceptable. The paper also provides models of the measurement error variances as functions of the proportion of developed land, which is essential for estimating the effect of measurement error for the whole NRI data

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

A Bayesian Analysis of Return Dynamics with Stochastic Volatility and Levy Jumps

We develop Bayesian Markov chain Monte Carlo methods for inferences of continuoustime models with stochastic volatility and infinite-activity Levy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Levy jumps, and (ii) affine jumpdiffusion models fail to adequately approximate the behavior of infinite-activity jumps. In particular, the affine jump-diffusion models fail to capture the infinitely many small Levy jumps which are too big for Brownian motion to model and too small for compound Poisson process to capture. Empirical studies show that infinite-activity Levy jumps are essential for modeling the S&P 500 index returns