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This paper develops a two-step estimation methodology that allows us to apply catastrophe theory to stock market … returns with time-varying volatility and to model stock market crashes. In the first step, we utilize high-frequency data to … estimate daily realized volatility from returns. Then, we use stochastic cusp catastrophe on data normalized by the estimated …
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This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market … returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily … realized volatility from the returns in the first step and use stochastic cusp catastrophe on data normalized by the estimated …
Persistent link: https://www.econbiz.de/10010206135
Persistent link: https://www.econbiz.de/10013193348
This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market … returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily … realized volatility from the returns in the first step and use stochastic cusp catastrophe on data normalized by the estimated …
Persistent link: https://www.econbiz.de/10012938546
Persistent link: https://www.econbiz.de/10009722625
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. The Smolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the...
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