A new Bayesian dynamic model integrates realized volatility proxies with price series via dynamic gamma processes and DLMs to enhance financial forecasting.
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Drift-diffusion analysis of Chiangmai pollutant data indicates that the dynamical models for PM, ozone, and NO2 have time-dependent parameters varying periodically to explain annual peaks.
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Bayesian Dynamic Modeling of Realized Volatility in Financial Asset Price Forecasting
A new Bayesian dynamic model integrates realized volatility proxies with price series via dynamic gamma processes and DLMs to enhance financial forecasting.
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Taking drift-diffusion analysis from the study of turbulent flows to the study of particulate matter smog and air pollutants dynamics
Drift-diffusion analysis of Chiangmai pollutant data indicates that the dynamical models for PM, ozone, and NO2 have time-dependent parameters varying periodically to explain annual peaks.
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