We estimate a reduced-form model of credit risk that incorporates stochastic volatility in default intensity via stochastic time-change. Our Bayesian MCMC estimation method overcomes nonlinearity in the measurement equation and state-dependent volatility in the state equation. We implement on firm-level time-series of CDS spreads, and find strong in-sample evidence of stochastic volatility in t...

This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process. Novel algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measureme...

The topic of this thesis is estimation of nonlinear dynamical systems, focusing on the use of methods such as particle filtering and smoothing. There are three areas of contributions: software implementation, applications of nonlinear estimation and some theoretical extensions to existing algorithms. The common theme for all the work presented is the pyParticleEst software framework, which has ...

In this paper, the particle filtering problem is investigated for a class of stochastic systems with multiple sensors under signal relays. To improve performance transmissions, relay deployed between each sensor and remote filter. Both amplify-and-forward (AF) decode-and-forward (DF) relays are considered certain transmission protocols. Stochastic series employed to describe multiplicative chan...

Robot localization using odometry and feature measurements is a nonlinear estimation problem. An efficient solution is found using the extended Kalman filter, EKF. The EKF however suffers from divergence and inconsistency when the nonlinearities are significant. We recently developed a new type of filter based on an auxiliary variable Gaussian distribution which we call the antiparticle filter ...

We consider situations where the applicability of sequential Monte Carlo particle filters is compromised due to expensive evaluation weights. To alleviate this problem, we propose a new filter algorithm based on multilevel approach. show that resulting bootstrap (MLBPF) retains strong law large numbers as well central limit theorem classical under mild conditions. Our numerical experiments demo...

The problem of the optimal allocation (in expected mean square error sense) a measurement budget for particle filtering is addressed. We propose three different intermittent filters, whose optimality criteria depend on information available at time decision making. For first, stochastic program filter, times are given by policy that determines whether should be taken based measurements already ...