 We have developed a statistical technique to test the model assumption of binary regime switching extension of the geometric L\'{e a new discriminating statistics. The statistics is  sensitive to the transition kernel of the regime switching model. With this statistics, given a time series data, one can test the hypothesis on the nature of regime switching. Furthermore, we have implemented this statistics for testing the regime switching hypothesis with Indian sectoral indices and have reported the result here. The result shows a clear indication of presence of multiple regimes  in the data.
Identifying the instances of jumps in a discrete time series sample of a jump diffusion model is a challenging task. We have developed a novel statistical technique for jump detection and volatility estimation in a return time series data using a threshold method. Since we derive the threshold and the volatility estimator simultaneously by solving an implicit equation, we obtain unprecedented accuracy across a wide range of parameter values. Using this method, the increments attributed to jumps have been removed from a large collection of historical data of Indian sectoral indices. Subsequently, we test the presence of regime switching dynamics in the volatility coefficient using a new discriminating statistics. The statistics is shown to be sensitive to the transition kernel of the regime switching model. We perform the testing using bootstrap method and find a clear indication of presence of multiple regimes of volatility in the data.
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Identifying the instances of jumps in a discrete time series sample of a jump diffusion model is a challenging task.
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statistical technique to test the model assumption of binary regime switching extension of the geometric L\'{e
novel statistical technique for jump detection and volatility estimation in a return time series data using a threshold method. Since we derive the threshold and the volatility estimator simultaneously by solving an implicit equation, we obtain unprecedented accuracy across a wide range of parameter values. Using this method, the increments attributed to jumps have been removed from a large collection of historical data of Indian sectoral indices. Subsequently, we test the presence of regime switching dynamics in the volatility coefficient using
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shown to be
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With this statistics, given a time series data, one can test the hypothesis on the nature of regime switching. Furthermore, we have implemented this statistics for testing the regime switching hypothesis with Indian sectoral indices and have reported the result here. The result shows
We perform the testing using bootstrap method and find
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of volatility