Statistical risk models are dependent on the return sampling frequency and window. Traditionally, risk modelling is performed using daily or monthly returns. While the relatively short horizon may be appropriate for asset managers concerned with near term risk, most asset owners and solutions providers have investment horizons measured in quarters or years. If variance grows with time, and correlations are independent of the return period, modeling risk using shorter period returns is appropriate. However, if variance is not proportional to time (especially if it’s not due to a simple serial correlation structure), or if correlations are sampling period dependent, then longer-horizon risk estimates based on standard risk modeling approaches may be biased. The problem is compounded because market cycles and regimes cause volatilities and correlations to vary over time.
This presentation shows how to use Cumulative Contribution Charts to visualize the relationships between investment horizon and volatility and the behavior of volatilities and correlations through time. With that information the researcher can select the sampling period and window that reflects the investment horizon and expected market environment.