Cathy W. S. Chen

A giant planet orbiting the 'extreme horizontal branch' star V 391 Pegasi

After the initial discoveries fifteen years ago, over 200 extrasolar planets have now been detected. Most of them orbit main-sequence stars similar to our Sun, although a few planets orbiting red giant stars have been recently found. When the hydrogen in their cores runs out, main-sequence stars undergo an expansion into red-giant stars. This expansion can modify the orbits of planets and can easily reach and engulf the inner planets. The same will happen to the planets of our Solar System in about five billion years and the fate of the Earth is matter of debate. Here we report the discovery of a planetary-mass body (Msini = 3.2MJupiter) orbiting the star V 391 Pegasi at a distance of about 1.7 astronomical units (au), with a period of 3.2 years. This star is on the extreme horizontal branch of the Hertzsprung–Russell diagram, burning helium in its core and pulsating. The maximum radius of the red-giant precursor of V 391 Pegasi may have reached 0.7 au, while the orbital distance of the planet during the stellar main-sequence phase is estimated to be about 1 au. This detection of a planet orbiting a post-red-giant star demonstrates that planets with orbital distances of less than 2 au can survive the red-giant expansion of their parent stars.

Asymmetrical reaction to US stock-return news: evidence from major stock markets based on a double-threshold model

Abstract This paper examines the hypothesis that both stock returns and volatility are asymmetrical functions of past information from the US market. By employing a double-threshold GARCH model to investigate six major index-return series, we find strong evidence supporting the asymmetrical hypothesis of stock returns. Specifically, negative news from the US market will cause a larger decline in a national stock return than an equal magnitude of good news. This holds true for the volatility series. The variance appears to be more volatile when bad news impacts the market than when good news does.

Turning points, reproduction number, and impact of climatological events for multi-wave dengue outbreaks

Objectives  To study climatological and public health events which might have affected the 2007 two‐wave dengue outbreak in Taiwan, an island with both tropical and subtropical regions, where the 2007 dengue incidence exceeded the combined total of the previous four years.

Bayesian time-varying quantile forecasting for Value-at-Risk in financial markets

Recently, advances in time-varying quantile modeling have proven effective in financial Value-at-Risk forecasting. Some well-known dynamic conditional autoregressive quantile models are generalized to a fully nonlinear family. The Bayesian solution to the general quantile regression problem, via the Skewed-Laplace distribution, is adapted and designed for parameter estimation in this model family via an adaptive Markov chain Monte Carlo sampling scheme. A simulation study illustrates favorable precision in estimation, compared to the standard numerical optimization method. The proposed model family is clearly favored in an empirical study of 10 major stock markets. The results that show the proposed model is more accurate at Value-at-Risk forecasting over a two-year period, when compared to a range of existing alternative models and methods.

Optimal dynamic hedging via copula-threshold-GARCH models

The contribution of this paper is twofold. First, we exploit copula methodology, with two threshold GARCH models as marginals, to construct a bivariate copula-threshold-GARCH model, simultaneously capturing asymmetric nonlinear behaviour in univariate stock returns of spot and futures markets and bivariate dependency, in a flexible manner. Two elliptical copulas (Gaussian and Students-t) and three Archimedean copulas (Clayton, Gumbel and the Mixture of Clayton and Gumbel) are utilized. Second, we employ the presenting models to investigate the hedging performance for five East Asian spot and futures stock markets: Hong Kong, Japan, Korea, Singapore and Taiwan. Compared with conventional hedging strategies, including Engles dynamic conditional correlation GARCH model, the results show that hedge ratios constructed by a Gaussian or Mixture copula are the best-performed in variance reduction for all markets except Japan and Singapore, and provide close to the best returns on a hedging portfolio over the sample period.

Quarantine for SARS, Taiwan.

Quarantine for SARS during the 2003 Taiwan outbreak expedited case detection, thereby indirectly preventing infections.

A Bayesian analysis of generalized threshold autoregressive models

The threshold autoregressive (TAR) model is generalized which results in more flexibility in applications. We construct a Bayesian framework to show that Markov chain Monte Carlo method can be applied to estimating parameters with success.

Comparison of nonnested asymmetric heteroskedastic models

The GJR-GARCH model is a popular choice among nonlinear models of the well-known asymmetric volatility phenomenon in financial market data. However, recent work employs double threshold nonlinear models to capture both mean and volatility asymmetry. A Bayesian model comparison procedure is proposed to compare the GJR-GARCH with various double threshold GARCH specifications, by designing a reversible jump Markov chain Monte Carlo algorithm. A simulation experiment illustrates good performance in estimation and model selection over reasonable sample sizes. In a study of seven markets strong evidence is found that the DTGARCH, with US market news as threshold variable, outperforms the GJR-GARCH and traditional self-exciting DTGARCH models. This result was consistent across six markets, excluding Canada.

Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisis

Volatility forecasting using threshold heteroskedastic models of the intra-day range

An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The range is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous variable such as US market information. The model captures aspects such as sign or size asymmetry and heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility forecasting in a Bayesian framework. An MCMC sampling scheme is employed for estimation and shown to work well in simulation experiments. Finally, competing range-based and return-based heteroskedastic models are compared via out-of-sample forecast performance. Applied to six international financial market indices, the range-based threshold heteroskedastic models are well supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast performance under various volatility proxies.