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Geweke, John; Whiteman, Charles - 2006

Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general …. In Bayesian forecasting, one simply takes a subset of the unknown quantities to be future values of some variables of … interest. This chapter presents the principles of Bayesian forecasting, and describes recent advances in computational …

Persistent link: https://www.econbiz.de/10014023705

Adaptive polar sampling with an application to a Bayes measure of value-at-risk

Bauwens, Luc; Bos, Charles S.; Dijk, Herman K. van - 1999

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlomethod for Bayesian analysis of models with ill-behaved posteriordistributions. In order to sample efficiently from such a distribution,a location-scale transformation and a transformation to polarcoordinates are used. After...

Persistent link: https://www.econbiz.de/10011302625

Bootstrap methods for inference in the Parks model

Moundigbaye, Mantobaye; Messemer, Clarisse; Parks, … - 2019

This paper shows how to bootstrap hypothesis tests in the context of the Parks (Efficient estimation of a system of regression equations when disturbances are both serially and contemporaneously correlated 1967) estimator. It then demonstrates that the bootstrap outperforms Parks's top...

Persistent link: https://www.econbiz.de/10012018487

Bootstrap methods for inference in the Parks model

Moundigbaye, Mantobaye; Messemer, Clarisse; Parks, … - 2020

This paper shows how to bootstrap hypothesis tests in the context of the Parks’s (1967) Feasible Generalized Least Squares estimator. It then demonstrates that the bootstrap outperforms FGLS(Parks)’s top competitor. The FGLS(Parks) estimator has been a workhorse for the analysis of panel...

Persistent link: https://www.econbiz.de/10012160012

Efficient numerical pricing of American call options using symmetry arguments

Stentoft, Lars - In: Journal of risk and financial management : JRFM 12 (2019) 2/59, pp. 1-26

regression and simulation-based least-squares Monte Carlo method by using put-call symmetry. The results show that, for a large …

Persistent link: https://www.econbiz.de/10012022212

The Impact of Effect Size Heterogeneity on Meta-Analysis: A Monte Carlo Experiment

Koetse, Mark J.; Florax, Raymond J.G.M.; de Groot, … - 2007

In this paper we use Monte Carlo simulation to investigate the impact of effect size heterogeneity on the results of a …

Persistent link: https://www.econbiz.de/10010325529

Modelling Stochastic Volatility with Leverage and Jumps : A Simulated Maximum Likelihood Approach via Particle Filtering

Malik, Sheheryar; Pitt, Michael K. - 2011

In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space...

Persistent link: https://www.econbiz.de/10014185810

Value Function Approximation or Stopping Time Approximation : A Comparison of Two Recent Numerical Methods for American Option Pricing using Simulation and Regression

Stentoft, Lars - 2012

In Longstaff and Schwartz (2001) a method for American option pricing using simulation and regression is suggested, and … since then the method has rapidly gained importance. However, the idea of using regression and simulation for American …

Persistent link: https://www.econbiz.de/10014212073

The Impact of Effect Size Heterogeneity on Meta-Analysis : A Monte Carlo Experiment

Koetse, Mark J.; Florax, Raymond J. G. M.; Groot, Henri … - 2007

In this paper we use Monte Carlo simulation to investigate the impact of effect size heterogeneity on the results of a …

Persistent link: https://www.econbiz.de/10014225495

Monte Carlo Algorithms for Optimal Stopping and Statistical Learning - Convergence Rates and Sample Complexity

Egloff, Daniel - 2003

In this article we extend the by now classical Longstaff-Schwartz algorithm for approximately solving high dimensional optimal stopping problems. We reformulate the problem of optimal stopping in discrete time as a generalized statistical learning problem. Within this setup we apply modern...

Persistent link: https://www.econbiz.de/10014075561