Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility

Common Drifting Volatility in Large Bayesian VARs∗

The general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar. We propose two models in which conditional volatilities feature comovement and study them using U.S. macroeconomic data. The first model specifies the conditional volatilities as driven by a single common unobserved factor, plus an idiosyncratic component. We label this model BVAR wi...

Online Monitoring for Industrial Processes Quality Control Using Time Varying Parameter Model

A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation...

Bayesian Analysis of a Stochastic Volatility Model

Bayesian changepoint and time-varying parameter learning in regime switching volatility models

BAYESIAN CHANGEPOINT AND TIME-VARYING PARAMETER LEARNING IN REGIME SWITCHING VOLATILITY MODELS This dissertation proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based volatility models with an unknown number ...

Discrete Time Stochastic Volatility Model

Title of dissertation: Discrete Time Stochastic Volatility Model Guojing Tang, Doctor of Philosophy, 2009 Dissertation directed by: Professor Dilip B. Madan Professor Benjamin Kedem In this dissertation we propose a new model which captures observed features of asset prices. The model reproduces the skewness and fat tails of asset returns by introducing a discretized variance gamma process as t...