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Estimation of sentiment effects in financial markets : a simulated method of moments approach

Chen, Zhenxi; Lux, Thomas - 2015

We take the model of Alfarano et al. (Journal of Economic Dynamics & Control 32, 2008, 101-136) as a prototype agent-based model that allows reproducing the main stylized facts of financial returns. The model does so by combining fundamental news driven by Brownian motion with a minimalistic...

Persistent link: https://www.econbiz.de/10010501936

Clarifications to questions and criticisms on the Johansen-Ledoit-Sornette bubble model

Sornette, Didier; Woodard, Ryan; Yan, Wanfeng; Zhou, … - 2011

The Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with finite-time singular crash hazard rates has been developed to describe the dynamics of financial bubbles and crashes. It has been applied successfully to a large variety of financial bubbles in many different markets....

Persistent link: https://www.econbiz.de/10009273112

Simulated ML estimation of financial agent-based models

Baruník, Jozef; Kukačka, Jiří - 2016

This paper proposes computational framework for empirical estimation of Financial Agent-Based Models (FABMs) that does not rely upon restrictive theoretical assumptions. We customise a recent methodology of the Non-Parametric Simulated Maximum Likelihood Estimator (NPSMLE) based on kernel...

Persistent link: https://www.econbiz.de/10011448663

Robust Value at Risk Prediction : Appendix

Mancini, Loriano - 2010

This appendix extends simulation and empirical results reported in Mancini and Trojani (2010). It discusses the choice … forecast evaluation; provides additional Monte Carlo simulation results on GARCH model estimation and VaR prediction; extends …

Persistent link: https://www.econbiz.de/10013138328

Clarifications to Questions and Criticisms on the Johansen-Ledoit-Sornette Bubble Model

Sornette, Didier - 2013

Persistent link: https://www.econbiz.de/10013089336

Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Creel, Michael - 2009

accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of … moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing …

Persistent link: https://www.econbiz.de/10013154330

Hybrid Historical Simulation VaR and ES : Performance in Developed and Emerging Markets

Zikovic, Sasa - 2009

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...

Persistent link: https://www.econbiz.de/10013155427

Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood

Kristensen, Dennis - 2008

We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed-form representation of the likelihood function. Using the...

Persistent link: https://www.econbiz.de/10012722610

Optimization in a Simulation Setting : Use of Function Approximation in Debt Strategy Analysis

Bolder, David Jamieson - 2008

The stochastic simulation model suggested by Bolder (2003) for the analysis of the federal government's debt … nonetheless complicated by two challenges. First, performing optimization with traditional techniques in a simulation setting is … address a number of policy questions that could not be fully addressed with the current stochastic simulation engine …

Persistent link: https://www.econbiz.de/10012725872

Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood

Kristensen, Dennis - 2006

We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated...

Persistent link: https://www.econbiz.de/10012734210