Detecting Regime Change in Computational Finance by Jun Chen;Edward P K Tsang;

Author:Jun Chen;Edward P K Tsang; [Неизв.]
Language: eng
Format: epub
ISBN: 9781000220360
Publisher: CRC Press (Unlimited)
Published: 2021-08-30T21:00:00+00:00

4.4 RESULTS AND DISCUSSIONS

So far, we have explained that under a given threshold, DC summarised a data set into trends. HMM classified these trends into regimes, with each regime comprising a sequence of trends. Each trend defined a TMV and a T value. We computed the average TMV and average T values for each regime in each data set. In this section, we compared the normalised TMV and T values of the two regimes, from different markets and time periods.

For each data set, we computed the average TMV and T values for all the trends of each regime. For example, we computed the average normalised TMV and T values of all trends in Regime 1 in the data of GBP–USD, which is summarised under the threshold 0.1%, and the same is done for Regime 2. Each market regime in each data set will occupy a position within the two-dimensional (T-TMV) indicator space. This will allow us to see whether Regimes 1 and 2 occupy different regions of the indicator space. If they do, then it is possible to define the region of normal regime and abnormal regime.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.