Inference of Binary Regime Models with Jump Discontinuities

Identifying the instances of jumps in a discrete-time-series sample jump diffusion model is challenging task. We have developed novel statistical technique for detection and volatility estimation return time series data using threshold method. The consistency estimator has been obtained. Since we derived simultaneously by solving an implicit equation, obtained unprecedented accuracy across wide range parameter values. Using this method, increments attributed to removed from large collection historical Indian sectorial indices. Subsequently, tested presence regime-switching dynamics coefficient new discriminating statistic. statistic shown be sensitive transition kernel model. perform testing Bootstrap method find clear indication multiple regimes data. A link all Python codes given conclusion. methodology suitable analyzing high frequency may applied algorithmic trading.