Vaasan yliopiston opinnäytteet

Kauppatieteellinen tiedekunta, 2008

Li, Yanshuang

Evaluation of VaR calculation methods

Ohjaaja/Valvoja (DI):
Professor Timo Rothovius
Tutkinto:
Master of Administrative Sciences
Laitos:
Laskentatoimen ja rahoituksen laitos
Pääaine:
Finance and Accounting
Koulutusohjelma:
Master's Degree Programme in Finance
Tutkielman kieli:
Englanti
Sivumäärä:
70
This paper evaluates different VaR calculation methods in measuring Chinese stock market in terms of the acceptability, variability, accuracy and measurement error of VaR models. Three VaR calculation methods based on 5 different models are evaluated, namely Variance-Covariance methods (VC) based on EARCH model (VCEA), RiskMetrics (VCRM) model, Monte Carlo Simulation (MC) modified with EARCH (MCEA) model and RiskMetrics model (MCRM) and historical simulation (HS).

The main findings of this paper are: First, HS method and VCRM method are unacceptable in calculating VaR in Chinese stock market based on the coverage test suggested by Christoffersen for 125-day evaluation window while only HS is unacceptable for a 50-day evaluation sample. Second, MCEA method has the lowest variability, HS has the highest variability and the variability of MCRM and VCEA are lower than MCEA but higher than VCRM for 125-day and 50-day evaluation windows based on RMSRB. Third, the accuracy of MCEA is the highest among all calculation method used in the paper for 125-day evaluation window while the accuracy of MCEA, MCRM and VCEA is high and similar for 50-day evaluation window. HS and VCRM model have relatively low accuracy for both evaluation windows. Finally, there is measurement error using HS method for 125-day evaluation window based on Hitt test. It can be conclude that, MC method performs well in calculating VaR Chinese Stock market while HS is an inappropriate method based on the results of four aspects of evaluation test, however performance of each VaR calculation method is affected by the length of evaluation window.
Avainsanat:
VaR, Evaluation, Performance
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