Treat outliers for risk models with due care!
By Anne Opschoor - Assistant Professor of Finance
06-04-2018 | 15:14
Risk managers, pension funds, asset managers and banks nowadays use advanced models to assess the risk of investment portfolios. Much scientific progress has been made over the past decade to develop new techniques to measure the risk of such portfolios. In recent years scientists and professionals have started to use so-called high-frequency data to measure the risk. High-frequency data are frequent measurements of for instance stock prices or exchange rates. You can think of measurements every minute, every second, or in some cases even every millisecond. Such measurements often result in more accurate risk assessments than traditional daily measurements.
An important issue, however, is how to deal with so-called outliers in high-frequency data. An outlier is an anomalous measurement in the data. Think of a temporary crash in markets due to a faulty algorithm, a typo by a trader, or any other reason. Such anomalous events occur more often than you would think. For instance, in May 2010 there was a famous flash-crash that unsettled the main U.S. financial markets. Within the time span of 36 minutes, the Dow Jones index dropped by 9%(!!) and subsequently recovered. Such big swings within the day result in enormous swings in risk measures and incorrect risk forecasts for subsequent days.
Anne Opschoor, Andre Lucas, Pawel Janus, and Dick van Dijk have developed a new technique to deal with such anomalous observations in high-frequency data. Their paper New HEAVY Models for Fat-Tailed Realized Covariances and Returns has been accepted in the Journal of Business and Economic Statistics. The core novelty of the approach is that anomalous event do not automatically inflate risk forecasts as in traditional models. Instead, the model trades off whether the increased risk is due to a true increase in risk, or to an incidental, anomalous event. The authors use statistical techniques calibrated on financial data to properly make this trade-off.
The model has been tested on a long time series of 30 U.S. stocks over the period 2000-2014. During that period, we have seen big events like the financial crisis of 2008, but also peak events like the May 2010 flash-crash. Using the new techniques, risk forecasts are significantly better than with the most recent competing methods. Moreover, the methods are relatively straightforward to implement, which should increase this research's impact.
Read the article: Opschoor, A., Janus, P., Lucas, A., & Van Dijk, D. (2018). New HEAVY Models for Fat-Tailed Realized Covariances and Returns. Journal of Business and Economic Statistics, 1-15
About Anne Opschoor
Anne Opschoor is an Assistant Professor at the Department of Finance at Vrije Universiteit Amsterdam (tenure track). He got tenure in December 2016. Under supervision of Dick van Dijk and Michel van der Wel, he obtained his PhD at the Tinbergen Institute/Econometric Institute at the Erasmus University Rotterdam in February 2014. He holds a master's degree in econometrics with honors from Erasmus University Rotterdam.