Forecasting market volatility
Accurately managing risk, in financial markets, depends on predicting volatility. While many different models make such predictions, defining the appropriate framework of choosing the right model is challenging. The EU-funded MODEL_PREDICTABILITY (Volatility forecasting evaluation framework) project compared and evaluated a set of competing models. Researchers worked to develop a model selection framework, incorporating the evaluations and method for evaluation, for use in volatility forecasting. The three-year project concluded in November 2014. Team members defined a set of models for forecasting volatility, which included certain assumptions about errors' distribution. After calculating the errors for each model, the team selected the one yielding lowest errors. The group also confirmed that its methodology for evaluating error was suitable. Models were next applied to estimating volatility of major European stock market indices, plus certain currency exchange markets. For each trading day, the models were re-estimated and their error rates compared. In general, a certain ARFIMA-GARCH model proved most accurate. The project also provided training and career development for its research staff. MODEL_PREDICTABILITY estimated the volatility forecasting value of various models. The best performer was identified.
Keywords
Forecasting, market volatility, managing risk, financial markets, volatility forecasting
