Value at Risk Through GARCH Models and Montecarlo Simulation: Evidence from the Colombian Stock Market
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Abstract
This paper evaluates the performance of several volatility models for estimating one-day-ahead Value-at-Risk (VaR) of twenty-four stocks return series in Colombia, using a number of distributional assumptions. Because all return series exhibit volatility clustering and long-range memory, GARCH-type models including models under normal, T-Student and generalized error distribution are examined. The findings corroborate the difficulty of choosing a single model for calculating VaR, but validate the use of parametric models with normal distribution and Montecarlo simulation in emerging financial markets.
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References
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