Similar to the cross-sectional momentum crashes, the time series momentum experiences deep and persistent drawdowns in the stressed time of slumps in the upward momentum, rebounds in the downward momentum, and long time sideways market. We measure the upside and downside risk using the upper and...

We utilize the mixed exponential power asymmetric GARCH model where each component exhibits asymmetric conditional heteroskedasticity to model Shanghai Stock Exchange Composite Index daily returns. Thanks to extra component-specific shape parameters, it can better capture the tail behavior and...

The paper develops a tail risk forecasting model that incorporates the wealth of economic and financial information available to risk managers. The approach can be viewed as a regularized extension of the two-stage GARCH-EVT model of McNeil and Frey (2000) where we permit a time-varying...

This article develops a direct filtration-based maximum likelihood methodology for estimating the parameters and realizations of latent affine processes. The equivalent of Bayes' rule is derived for recursively updating the joint characteristic function of latent variables and the data...

This article develops a direct filtration-based maximum likelihood methodology for estimating the parameters and realizations of latent affine processes. The equivalent of Bayes' rule is derived for recursively updating the joint characteristic function of latent variables and the data...

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of...