Wai Keung Li
Biographic Data
| ID | 8920447 |
|---|---|
| NAME | Wai Keung Li |
| GIVEN NAMES | Wai Keung |
| FAMILY NAME | Li |
| SIGNATURE | LI W K |
| AFFILIATIONS | University of Hong Kong |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1999 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 0 |
Buffered Autoregressive Models With Conditional Heteroscedasticity
This article introduces a new model called the buffered autoregressive model with generalized autoregressive conditional heteroscedasticity (BAR-GARCH). The proposed model, as an extension of the BAR model in Li et al. (2015 Li, G.D., Guan, B., Li, W.K., and Yu, P. L.H. (2015), “Hysteretic Autoregressive Time Series Models,” Biometrika, 102, 717–723.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), can capture the buffering phenomena …
On Mixture Double Autoregressive Time Series Models
This article proposes a mixture double autoregressive model by introducing the flexibility of mixture models to the double autoregressive model, a novel conditional heteroscedastic model recently proposed in the literature. To make it more flexible, the mixing proportions are further assumed to be time varying, and probabilistic properties including strict stationarity and higher order moments are derived. Inference tools including the maximum li…
Time Series
A New Pearson-Type QMLE for Conditionally Heteroscedastic Models
This article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not just the heavy-tailed but also the skewed innovations. Under strict stationarity and some weak moment conditions, the strong consistency and asymptotic normality of the PQMLE are obtained. With no furt…
Score Tests for Hyperbolic GARCH Models
Davidson (2004) recently proposed the hyperbolic GARCH model to capture the phenomenon of longrange dependence in volatility, with the extent of such dependence measured by the geometric or hyperbolic decay of the coefficients in an ARCH(∞) model. In this article, we reinterpret the hyperbolic GARCH model by building a link with the common GARCH model, and construct a simplified score test to check the presence of the hyperbolic decay. We derive …
Bayesian Unit-Root Testing in Stochastic Volatility Models
This article uses a Bayesian unit-root test in stochastic volatility models. The time series of interest is the volatility that is unobservable. The unit-root testing is based on the posterior odds ratio, which is approximated by Markov-chain Monte Carlo methods. Simulations show that the testing procedure is efficient for moderate sample size. The unit-root hypothesis is rejected in seven market indexes, and some evidence of nonstationarity is o…
No prominent works on this page.
Bayesian Unit-Root Testing in Stochastic Volatility Models
This article uses a Bayesian unit-root test in stochastic volatility models. The time series of interest is the volatility that is unobservable. The unit-root testing is based on the posterior odds ratio, which is approximated by Markov-chain Monte Carlo methods. Simulations show that the testing procedure is efficient for moderate sample size. The unit-root hypothesis is rejected in seven market indexes, and some evidence of nonstationarity is o…
Score Tests for Hyperbolic GARCH Models
Davidson (2004) recently proposed the hyperbolic GARCH model to capture the phenomenon of longrange dependence in volatility, with the extent of such dependence measured by the geometric or hyperbolic decay of the coefficients in an ARCH(∞) model. In this article, we reinterpret the hyperbolic GARCH model by building a link with the common GARCH model, and construct a simplified score test to check the presence of the hyperbolic decay. We derive …
Time Series
A New Pearson-Type QMLE for Conditionally Heteroscedastic Models
This article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not just the heavy-tailed but also the skewed innovations. Under strict stationarity and some weak moment conditions, the strong consistency and asymptotic normality of the PQMLE are obtained. With no furt…
Buffered Autoregressive Models With Conditional Heteroscedasticity
This article introduces a new model called the buffered autoregressive model with generalized autoregressive conditional heteroscedasticity (BAR-GARCH). The proposed model, as an extension of the BAR model in Li et al. (2015 Li, G.D., Guan, B., Li, W.K., and Yu, P. L.H. (2015), “Hysteretic Autoregressive Time Series Models,” Biometrika, 102, 717–723.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), can capture the buffering phenomena …
On Mixture Double Autoregressive Time Series Models
This article proposes a mixture double autoregressive model by introducing the flexibility of mixture models to the double autoregressive model, a novel conditional heteroscedastic model recently proposed in the literature. To make it more flexible, the mixing proportions are further assumed to be time varying, and probabilistic properties including strict stationarity and higher order moments are derived. Inference tools including the maximum li…
Econometrics (6 works) · Financial Risk and Volatility Modeling (6 works) · Mathematics (6 works) · Statistics (6 works) · Autoregressive conditional heteroskedasticity (4 works) · Heteroscedasticity (4 works) · Autoregressive integrated moving average (3 works) · Autoregressive model (3 works) · Market Dynamics and Volatility (3 works) · STAR model (3 works)