The Handbook of News Analytics in Finance

10.3 A STATE-VARIABLE APPROACH TO RISK ASSESSMENT

Our approach to short-horizon risk forecasting is different. We prefer to continue to use the existing risk models that are estimated from low-frequency return observations. Rather than depending on recent high-frequency data observations, we choose to ask ourselves a simple question: “How are conditions different now than they were on average during the sample period used for estimation?” This question is almost exactly congruent to our opening definition of news.

In this method, new information that is not part of the risk model is used to adjust various component parameters of the risk forecast to short-term conditions. This approach has multiple benefits. We sidestep almost all of the statistical complexities that arise with the use of high-frequency data. We get to keep the existing factor structure of any model, so risk reporting remains familiar and intuitive. Since our long-term and short-term forecasts are based on the same factor structure, we can also quickly estimate new forecasts for any length time horizon that falls between the two horizons.

Our first application of this approach was to incorporate option-implied volatility as a conditioning variable. Consider the hypothetical situation of a high-profile CEO of a major global corporation being killed in an automobile accident. To create a new forecast of the covariance of that company's stock with some other company or stock index would require waiting through a considerable ...