Trading Strategies And Extreme Return Predictions Based On The Recurrence Interval Analysis

With the further development of economic globalization and the continuous innovation of financial instruments,the financial market has achieved rapid development.At the same time,its instability has also increased significantly,and the exposure to risks has also become more and more obvious.After 1997,the financial crises,such as the Asian financial crisis,the sub-prime mortgage crisis and the European debt crisis,not only caused huge losses to investors,but also seriously damaged the financial systems of various countries and even undermined the real economy,which brought huge challenges to the financial supervision authorities.There-fore,how to effectively predict and prevent potential risks,especially the financial risks caused by the extreme fall of asset prices,is a hot issue that is of great concern to all sectors of the society.In recent years,econophysics has carried out extensive research on financial extreme events and proposed relatively simple methods of financial risk assessment by cross-using the theories and methods of statistical physics,applied mathematics,and complex systems theory.Here,we build an early warning model to predict the recurrence of financial extremes based on the distribution of recurrence intervals between consecutive historical extremes.It is found that the recurrence intervals exhibit the characteristics of positive skewness,fat-tailed distributions,and positive autocorrelations.Both in-sample and out-of-sample tests indicate that our model has great predicting power.We further design two trading strategies,including a long strategy and a short strategy,to check whether our model is able to provide significant profits.The back tests reveal that the two trading strategies can efficiently avoid the decline stage,and the long strategy earn higher profits.