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Bootstrapping to Compute Value-at-Risk Standard Errors
Bootstrapping to Compute Value-at-Risk Standard Errors
We obtain daily log-return data for the S&P 500 (with dividends reinvested) for the past twenty years, bootstrap low quantiles of the data, and then construct various confidence intervals around those estimated low quantiles. These low quantiles are related to a measure of risk called Value-at-Risk (VaR).
Value-at-Risk is a popular measure of risk used by both researchers and practitioners. For example, a bank's daily VaR at the 1% level is maximum loss not exceeded with 99% probability over a one-day time period. VaR has been a component of both the Basel I and Basel II recommendations on banking laws and regulations issued by the Basel Committee on Banking Supervision.