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Simulated Likelihood Estimators for Discretely Observed Jump-Diffusions

Giesecke, Kay - 2019

This paper develops an unbiased Monte Carlo approximation to the transition density of a jump-diffusion process with state-dependent drift, volatility, jump intensity, and jump magnitude. The approximation is used to construct a likelihood estimator of the parameters of a jump-diffusion observed...

Persistent link: https://www.econbiz.de/10012904646

EmmPack 1.01 : C/C++ code for use with Ox for estimation of univariate stochastic volatility models with the efficient method of moments

Sluis, Pieter J. van der - 1998

Econometric estimation using simulation techniques, such as the efficient method of moments, may betime consuming. The …

Persistent link: https://www.econbiz.de/10010533201

A Bayesian Beta Markov Random Field Calibration of the Term Structure of Implied Risk Neutral Densities

Casarin, Roberto - 2015

We build on Fackler and King (1990) and propose a general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates. The model is a Bayesian dynamic beta Markov random field which allows for possible...

Persistent link: https://www.econbiz.de/10013031557

Tail distribution of the maximum of correlated Gaussian random variables

Botev, Zdravko I.; Mandjes, Michel; Ridder, Ad - 2015

In this article we consider the efficient estimation of the tail distribution of the maximum of correlated normal random variables. We show that the currently recommended Monte Carlo estimator has difficulties in quantifying its precision, because its sample variance estimator is an inefficient...

Persistent link: https://www.econbiz.de/10011431354

Inference using simulated neural moments

Creel, Michael D. - In: Econometrics : open access journal 9 (2021) 4, pp. 1-15

This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals...

Persistent link: https://www.econbiz.de/10012642418

Risk Analysis Probability of Default : A Stochastic Simulation Model

Montesi, Giuseppe - 2015

We present a stochastic simulation model for estimating forward-looking corporate probability of default and loss given … identify the default condition, and solve the model by Monte Carlo simulation. First, we present the model; then we show how to …

Persistent link: https://www.econbiz.de/10013023044

Simulation-Based Likelihood Inference for Limited Dependent Processes

Manrique, Aurora; Shephard, Neil G. - 1999

simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis …. The central tools we use to deal with the time series dimension of the models are the scan sampler and the simulation …

Persistent link: https://www.econbiz.de/10014197180

Markov-Chain Approximation and Estimation of Nonlinear, Non-Gaussian State Space Models

Farmer, Leland - 2014

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

Persistent link: https://www.econbiz.de/10013048908

Estimation of the Truncated Bivariate Normal Stochastic Frontier Model by EM Algorithm

Das, Arabinda - 2009

it to estimate a truncated bivariate normal cost frontier using live data. We also carry out Monte Carlo simulation …

Persistent link: https://www.econbiz.de/10013158500

Control Variates for Variance Reduction in Indirect Inference : Interest Rate Models in Continuous Time

Calzolari, Giacomo; Di Iorio, Francesca; Fiorentini, … - 2018

Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for … variance suffers from an additional component, which depends on the stochastic simulation involved in the estimation procedure …. To reduce this undesirable effect, one could increase the number of simulations (or the length of each simulation) and …

Persistent link: https://www.econbiz.de/10014197185