"It depends"What's in your portfolio, and what's your forecast horizon?If all your equities are US equities, then all the close-of business prices happen at the same time. You can look at calculating VaR based on daily return timeseries. You want a good number of observations to make the volatility estimate of any one stock reasonable. So maybe 252 days is good.If your equities are global, then close-of-business prices are staggered across time zones. You're probably better basing the calculation on weekly returns. So to keep a good estimate of individual stock volatilities, you're going to need a longer historical period (252 weeks?).If you're predicting a 1-day VaR, then you want to try & base it on the recent past to try and 'capture the current volatility regime'. Maybe 1 year of daily returns is good.If you're predicting a 1-month VaR, then maybe you want to use 5 years of data to allow for changing volatility regimes.If you use exponetially weighted observations in your model, you also need to use a time period that is consistent with your half-life. Riskmetrics gives a rule of thumb that your history shoul dbe 3 to 6 times your half life. This means that the earliest observation gets 0.13 to 0.02 times as much weight as the latest observation.