Testing of Binary Regime Switching Models using Squeeze Duration Analysis

TL;DR

This paper introduces a new statistical test for binary regime switching models, effectively distinguishing between different model assumptions using squeeze duration analysis, with applications to Indian sector indices.

Contribution

The paper develops a novel discriminating statistic for testing binary regime switching models and demonstrates its effectiveness through systematic experiments and real data application.

Findings

Sampling distribution varies significantly across model assumptions

The test successfully differentiates GBM from Markov and semi-Markov models

Effective application to Indian sectoral indices

Abstract

We have developed a statistical technique to test the model assumption of binary regime switching extension of the geometric Brownian motion (GBM) model by proposing a new discriminating statistics. Given a time series data, we have identified an admissible class of the regime switching candidate models for the statistical inference. By performing several systematic experiments, we have successfully shown that the sampling distribution of the test statistics differs drastically, if the model assumption changes from GBM to Markov modulated GBM, or to semi-Markov modulated GBM. Furthermore, we have implemented this statistics for testing the regime switching hypothesis with Indian sectoral indices.