Risk management has become an important topic for financial institutions, regulators, nonfinancial corporations and asset managers. Value-at-Risk (VaR) is a measure for gauging the market risks of a particular portfolio. VaR shows the maximum loss over a given time horizon at a given confidence level. Economic and market conditions vary from time to time. It is reasonable to expect that the return process of a portfolio and its stochastic volatility will depend in some way on time. Therefore, a viable VaR estimate should have the ability to self-revise the procedure in order to adapt the changes in market conditions. This includes modifications of the procedures for estimating both volatility and quantiles. The aim of this paper is to propose and to empirically test a time-dependent semi-parametric model for estimating VaR in order to enhance the flexibility of local approximations.
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