Bootstrap VaR: This workbook uses CrystalBall2000 to produce bootstrap simulations from an inputted sample of profit/loss observations, from which a bootstrapped VaR can be inferred. Choleski: This workbook illustrates the Choleski decomposition of a 6x6 variance-covariance matrix. Component VaR: This workbook estimates the component (or decomposed) VaR using a variance-covariance approach assuming that P/L is distributed as multivariate normal, for chosen confidence level and holding period. Cornish-Fisher VaR and ETL: This workbook estimates VaR and expected tail loss (ETL) for near-normally distributed profit/loss, using the Cornish-Fisher adjustment for non-normality. ETL estimator: This workbook estimates expected tail loss (ETL) for 4 alternative distributions (normal, Student-t, lognormal, and Gumbel) of profit/loss or return. Gumbel VaR: This workbook estimates VaR assuming P/L is Gumbel-distributed. It estimates (and, where appropriate, plots) VaR(s) for a given confidence level and holding period, a range of confidence levels and a given holding period, a given confidence level and a range of holding periods, and ranges of both confidence level and holding period. Historical simulation: This workbook estimates and plots historical-simulation VaRs and ETLs for a range of confidence levels. Lognormal VaR: This workbook estimates VaR assuming that arithmetic returns are lognormally distributed. It estimates (and, where appropriate, plots) VaR(s) for a given confidence level and holding period, a range of confidence levels and a given holding period, a given confidence level and a range of holding periods, and ranges of both confidence level and holding period. Monte Carlo simulation VaR: This workbook estimates normal VaR and the VaR of a long European call option by Monte Carlo simulation. Normal VaR: This workbook estimates VaR assuming that P/L and/or arithmetic returns are normally distributed. It estimates VaR and ETL for a given confidence level and holding period, and estimates and plots VaRs for a range of confidence levels and a given holding period, a given confidence level and a range of holding periods, and ranges of both confidence level and holding period. Options VaR: This workbook estimates the VaR of vanilla Black-Scholes options using analytical methods. t VaR: This workbook estimates VaR assuming that P/L is Student-t distributed. It estimates (and, where appropriate, plots) VaR(s) for a given confidence level and holding period, a range of confidence levels and a given holding period, a given confidence level and a range of holding periods, and ranges of both confidence level and holding period. Variance-covariance VaR: This workbook uses a varianace-covariance approach to estimate the VaR of a portfolio assuming that returns are distributed as multivariate normal. Backtest: This workbook computes an illustrative Kupiec backtest. |