Yaw-Huei Wang

The Euro and European Financial Market Dependence

A time-varying copula model is used to investigate the impact of the introduction of the Euro on the dependence between 17 European stock markets during the period 1994–2003. The model is implemented with a GJR-GARCH-MA-t model for the marginal distributions and the Gaussian copula for the joint distribution, which allows capturing time-varying, non-linear relationships. The results show that, within the Euro area, market dependence increased after the introduction of the common currency only for large equity markets, such as in France, Germany, Italy, the Netherlands and Spain. Structural break tests indicate that the increase in financial market dependence started around the beginning of 1998 when Euro membership was determined and the relevant information was announced. The UK and Sweden, but not other European countries outside the Euro area, are found to exhibit an increase in equity market co-movement, which is consistent with the interpretation that these countries may be expected to join the Euro in the future.

The relationships between sentiment, returns and volatility

Previous papers that test whether sentiment is useful for predicting volatility ignore whether lagged returns information might also be useful for this purpose. By doing so, these papers potentially overestimate the role of sentiment in predicting volatility. In this paper we test whether sentiment is useful for volatility forecasting purposes. We find that most of our sentiment measures are caused by returns and volatility rather than vice versa. In addition, we find that lagged returns cause volatility. All sentiment variables have extremely limited forecasting power once returns are included as a forecasting variable.

The Information Content of the S&P 500 Index and VIX Options on the Dynamics of the S&P 500 Index

Given that both S&P 500 index and VIX options essentially contain information about the future dynamics of the S&P 500 index, in this study, we set out to empirically investigate the informational roles played by these two option markets with regard to the prediction of returns, volatility, and density in the S&P 500 index. Our results reveal that the information content implied from these two option markets is not identical. In addition to the information extracted from the S&P 500 index options, all of the predictions for the S&P 500 index are significantly improved by the information recovered from the VIX options. Our findings are robust to various measures of realized volatility and methods of density evaluation.

Bounds and Prices of Currency Cross-Rate Options

This paper derives the pricing bounds of a currency cross-rate option using the option prices of two related dollar rates via a copula theory and presents the analytical properties of the bounds under the Gaussian framework. Our option pricing bounds are useful, because (1) they are general in the sense that they do not rely on the distribution assumptions of the state vari-ables or on the selection of the copula function; (2) they are portfolios of the dollar-rate op-tions and hence are potential hedging instruments for cross-rate options; and (3) they can be applied to generate bounds on deltas. The empirical tests suggest that there are persistent and stable relationships between the market prices and the estimated bounds of the cross-rate op-tions and that our option pricing bounds (obtained from the market prices of options on two dollar rates) and the historical correlation of two dollar rates are highly informative for ex-plaining the prices of the cross-rate options. Moreover, the empirical results are consistent with the predictions of the analytical properties under the Gaussian framework and are robust in various aspects.

The Impact of Jump Dynamics on the Predictive Power of Option-Implied Densities

This study examines whether incorporating jumps with stochastic volatility can improve the predictive power of option-implied densities of the FTSE 100 index. A general double-jump model, as proposed by Duffie et al. (2000), is used to fit the market prices of options and to estimate ‘risk-neutral’ densities. ‘Real-world’ densities are then converted from their risk-neutral form by means of alternative statistical calibrations. Both the risk-neutral and real-world densities are evaluated, over five forecast horizons, using two different tests. Our empirical results indicate that adding jumps into the price and/or volatility processes not only substantially lowers the fitting errors of option prices, but also improves the predictive power of risk-neutral densities. Furthermore, satisfactory density prediction was consistently provided by the real-world densities, which were not dependent on the addition of jumps, the approach used to construct the densities, or the prediction horizon.

Sophistication, Sentiment, and Misreaction

This study investigates whether the existence or strength of any misreaction in the options market is affected by investor sophistication and investor sentiment. Based on a unique data set of the complete history of all transactions in the Taiwan options market, we find that individual investors exhibit significant misreaction to information and that this misreaction becomes stronger during periods of high investor sentiment. In addition, more active or aggressive individual investors always exhibit misreaction and do not learn from their past mistakes. Our empirical results are robust to alternative measures of investor sentiment and definitions of long- and short-term horizons.

The Volatility and Density Prediction Performance of Alternative GARCH Models

This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewed-t or compound Poisson (jump) distribution based upon a non-linear and asymmetric GARCH (NGARCH) model framework. The empirical results for the S&P 500 and FTSE 100 index returns suggest that the jump model outperforms all other models in terms of both volatility forecasting and density prediction. Nevertheless, the superiority of the non-normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change.

The Information Content of the Implied Volatility Term Structure on Future Returns

We derive the theoretical relation between the term structure of implied variance and the expected excess returns of the underlying asset. Adopting three alternative approaches to compile the variables representing the information on the implied volatility index level and term structure, we show the important role of the term structure in determining future excess returns of the S&P 500 index. Both the in-sample and out-of-sample analyses suggest that the information content of the term structure variable is significant and a strong complement to that of the level variable, especially for shorter-term excess returns.

The Information Content of Option-Implied Tail Risk on the Future Returns of the Underlying Asset

We compile option-implied tail loss and gain measures based on a deep out-of-the- money option pricing formula derived by applying ‘extreme value theory’, and then use these measures to investigate the information content of option-implied tail risk on the future returns of the underlying assets. Our empirical analysis shows that both tail measures implied by S&P 500 and VIX options can predict future changes in the corresponding underlying assets and are informative on the future returns of the S&P 500 index. The relationships are particularly strong during periods of economic recession and driven by the tail-risk premium.