Econometric Methods and Monte Carlo Simulations for Financial Risk Management
(2013) NEKN02 20131Department 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:
http://lup.lub.lu.se/student-papers/record/3920617
- author
- Baltaev, Alexander LU and Chavdarov, Ivaylo
- supervisor
-
- Karl Larsson LU
- Birger Nilsson LU
- organization
- course
- NEKN02 20131
- year
- 2013
- 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}}, language = {{eng}}, note = {{Student Paper}}, title = {{Econometric Methods and Monte Carlo Simulations for Financial Risk Management}}, year = {{2013}}, }