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Econometric Methods and Monte Carlo Simulations for Financial Risk Management

Baltaev, Alexander LU and Chavdarov, Ivaylo (2013) NEKN02 20131
Department of Economics
Abstract
Value-at-Risk (VaR) forecasting in the context of Monte Carlo simulations is evaluated.
A range of parametric models is considered, namely the traditional Generalized Autore-
gressive Conditional Heteroscedasticity (GARCH) model, the exponential GARCH and
the GJR-GARCH, which are put in the context of the Gaussian and Student-t distri-
butions. The returns of the S&P 500 provide the basis for the study. Monte Carlo
simulations are then applied in the estimation and forecasting of index returns. Two
forecasting periods are employed with respect to the Global Financial Crisis (GFC). The
forecasting accuracy of the various models will be evaluated in order to determine the
applicability of these VaR estimation techniques in dierent... (More)
Value-at-Risk (VaR) forecasting in the context of Monte Carlo simulations is evaluated.
A range of parametric models is considered, namely the traditional Generalized Autore-
gressive Conditional Heteroscedasticity (GARCH) model, the exponential GARCH and
the GJR-GARCH, which are put in the context of the Gaussian and Student-t distri-
butions. The returns of the S&P 500 provide the basis for the study. Monte Carlo
simulations are then applied in the estimation and forecasting of index returns. Two
forecasting periods are employed with respect to the Global Financial Crisis (GFC). The
forecasting accuracy of the various models will be evaluated in order to determine the
applicability of these VaR estimation techniques in dierent market conditions. Results
reveal that: (i) no model has consistent performance in both volatile and stable mar-
ket conditions; (ii) asymmetric volatility models oer better performance in the post
crisis forecasting period; (iii) all models underestimate risk in highly unstable market
conditions. (Less)
Please use this url to cite or link to this publication:
author
Baltaev, Alexander LU and Chavdarov, Ivaylo
supervisor
organization
course
NEKN02 20131
year
type
H1 - Master's Degree (One Year)
subject
keywords
Value-at-Risk, GARCH, Monte Carlo, Global Financial Crisis.
language
English
id
3920617
date added to LUP
2013-07-29 11:38:34
date last changed
2013-07-29 11:38:34
@misc{3920617,
  abstract     = {Value-at-Risk (VaR) forecasting in the context of Monte Carlo simulations is evaluated.
A range of parametric models is considered, namely the traditional Generalized Autore-
gressive Conditional Heteroscedasticity (GARCH) model, the exponential GARCH and
the GJR-GARCH, which are put in the context of the Gaussian and Student-t distri-
butions. The returns of the S&P 500 provide the basis for the study. Monte Carlo
simulations are then applied in the estimation and forecasting of index returns. Two
forecasting periods are employed with respect to the Global Financial Crisis (GFC). The
forecasting accuracy of the various models will be evaluated in order to determine the
applicability of these VaR estimation techniques in dierent market conditions. Results
reveal that: (i) no model has consistent performance in both volatile and stable mar-
ket conditions; (ii) asymmetric volatility models oer better performance in the post
crisis forecasting period; (iii) all models underestimate risk in highly unstable market
conditions.},
  author       = {Baltaev, Alexander and Chavdarov, Ivaylo},
  keyword      = {Value-at-Risk,GARCH,Monte Carlo,Global Financial Crisis.},
  language     = {eng},
  note         = {Student Paper},
  title        = {Econometric Methods and Monte Carlo Simulations for Financial Risk Management},
  year         = {2013},
}