Research Article
Return Intervals Approach to Financial Fluctuations
@INPROCEEDINGS{10.1007/978-3-642-02466-5_1, author={Fengzhong Wang and Kazuko Yamasaki and Shlomo Havlin and H. Stanley}, title={Return Intervals Approach to Financial Fluctuations}, proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1}, proceedings_a={COMPLEX PART 1}, year={2012}, month={5}, keywords={Financial marekts Econophysics Volatility Return interval Scaling Long-term correlation}, doi={10.1007/978-3-642-02466-5_1} }- Fengzhong Wang
Kazuko Yamasaki
Shlomo Havlin
H. Stanley
Year: 2012
Return Intervals Approach to Financial Fluctuations
COMPLEX PART 1
Springer
DOI: 10.1007/978-3-642-02466-5_1
Abstract
Financial fluctuations play a key role for financial markets studies. A new approach focusing on properties of return intervals can help to get better understanding of the fluctuations. A return interval is defined as the time between two successive volatilities above a given threshold. We review recent studies and analyze the 1000 most traded stocks in the US stock markets. We find that the distribution of the return intervals has a well approximated scaling over a wide range of thresholds. The scaling is also valid for various time windows from one minute up to one trading day. Moreover, these results are universal for stocks of different countries, commodities, interest rates as well as currencies. Further analysis shows some systematic deviations from a scaling law, which are due to the nonlinear correlations in the volatility sequence. We also examine the memory in return intervals for different time scales, which are related to the long-term correlations in the volatility. Furthermore, we test two popular models, FIGARCH and fractional Brownian motion (fBm). Both models can catch the memory effect but only fBm shows a good scaling in the return interval distribution.
