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Continuous Time Processes for Finance - 9783031063633
Continuous Time Processes for Finance - 9783031063633
Continuous Time Processes for Finance - 9783031063633
Continuous Time Processes for Finance - 9783031063633
Continuous Time Processes for Finance - 9783031063633

Continuous Time Processes for Finance - 9783031063633

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Continuous Time Processes for FinanceSwitching, Self-exciting, Fractional and other Recent Dynamics\nAuthor(s): Donatien Hainaut\nFormat: Paperback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031063633, 978-3031063633\nSynopsis\nThis book explores recent topics in quantitative finance with an emphasis on applications and calibration to time-series. This last aspect is often neglected in the existing mathematical finance literature while it is crucial for risk management. The first part of this book focuses on switching regime processes that allow to model economic cycles in financial markets. After a presentation of their mathematical features and applications to stocks and interest rates, the estimation with the Hamilton filter and Markov Chain Monte-Carlo algorithm (MCMC) is detailed. A second part focuses on self-excited processes for modeling the clustering of shocks in financial markets. These process.

Continuous Time Processes for Finance - 9783031063633

Continuous Time Processes for FinanceSwitching, Self-exciting, Fractional and other Recent Dynamics\nAuthor(s): Donatien Hainaut\nFormat: Paperback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031063633, 978-3031063633\nSynopsis\nThis book explores recent topics in quantitative finance with an emphasis on applications and calibration to time-series. This last aspect is often neglected in the existing mathematical finance literature while it is crucial for risk management. The first part of this book focuses on switching regime processes that allow to model economic cycles in financial markets. After a presentation of their mathematical features and applications to stocks and interest rates, the estimation with the Hamilton filter and Markov Chain Monte-Carlo algorithm (MCMC) is detailed. A second part focuses on self-excited processes for modeling the clustering of shocks in financial markets. These process.

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