Wing Hong Chan

Conditional Jump Dynamics in Stock Market Returns

This article develops a new conditional jump model to study jump dynamics in stock market returns. We propose a simple filter to infer ex post the distribution of jumps. This permits construction of the shock affecting the time t conditional jump intensity and is the main input into an autoregressive conditional jump intensity model. The model allows the conditional jump intensity to be time-varying and follows an approximate autoregressive moving average (ARMA) form. The time series characteristics of 72 years of daily stock returns are analyzed using the jump model coupled with a generalized autoregressive conditional heteroscedasticity (GARCH) specification of volatility. We find significant time variation in the conditional jump intensity and evidence of time variation in the jump size distribution. The conditional jump dynamics contribute to good in-sample and out-of-sample fits to stock market volatility and capture the rally often observed in equity markets following a significant downturn.

University Efficiency: A Comparison and Consolidation of Results from Stochastic and Non-stochastic Methods

Abstract Efficiency scores are determined for Canadian universities using both data envelopment analysis and stochastic frontier methods for selected specifications. The outcomes are compared. There is considerable divergence in the efficiency scores and their rankings among methods and specifications. An analysis of rankings, however, reveals that the relative positions of individual universities across sets of several efficiency rankings (e.g., all the data envelopment analysis and stochastic frontier outcomes) demonstrate an underlying consistency. High‐efficiency and low‐efficiency groups are evidenced but the rank for most universities is not significantly different from that of many others. The results emphasize the need for caution when employing efficiency scores for management and policy purposes, and they recommend looking for confirmation across viable alternatives.

The Economic Value of Using Realized Volatility in Forecasting Future Implied Volatility

We examine the economic benefits of using realized volatility to forecast future implied volatility for pricing, trading, and hedging in the S&P 500 index options market. We propose an encompassing regression approach to forecast future implied volatility and hence future option prices by combining historical realized volatility and current implied volatility. An analysis of delta-neutral straddles and naked and delta-hedged option positions shows that the statistical superiority of historical realized volatility demonstrated in the encompassing regressions and option pricing errors does not translate into economic gains, when trading and hedging in the options markets, after considering trading costs.

Dynamic Hedging with Foreign Currency Futures in the Presence of Jumps

A dynamic hedging strategy based on a bivariate GARCH-jump model augmented with autoregressive jump intensity is proposed to manage currency risk. The GARCH-jump model, capable of capturing volatility clustering and leptokurtosis, provides a comprehensive description of the joint dynamics of the currency spot rate and the futures basis. We find significant common jump components in the currency spot rate and futures basis with jump sizes response asymmetrical to futures basis changes. Our out-of-sample hedging exercises show optimal hedge ratios incorporating information from common jump dynamics substantially reduce the portfolio risk of foreign currencies.

Weather, Inventory and Common Jump Dynamics in Natural Gas Futures and Spot Markets

This paper studies the common jump dynamics in natural gas futures and spot markets within a bivariate autoregressive jump intensity-GARCH framework (BARJI-GARCH). We particularly examine the role of weather as a short-run demand factor and inventory as a short-run supply factor in explaining price spikes and time-varying volatility in spot and futures returns. Using daily time series data from 1994 to 2004, we obtain several interesting empirical results: (1) conditional jump intensity is persistent and the likelihood of common jumps in the future depends on the past history of jump dynamics; (2) the magnitude of average jump size varies over time as a function of unanticipated low temperature and inventory surprise and (3) both means of jump intensity and jump size are higher in the winter months and lower in the summer months. These results are consistent with theory suggesting that price jumps (spikes) often occur in the situation where there is sudden shift in short-run demand when short-run supply is inelastic.

The Economic Value of Using Realized Volatility in the Index Options Market

We examine the economic benefits of using high frequency volatility measures for pricing, trading and hedging in the S&P 500 index options market. Using the encompassing regression framework, we generate volatility forecasts combining information from long memory high-frequency volatility specifications and option-based implied volatilities. We conduct out-of-sample tests of the volatility forecasts by examining option pricing performance, trading performance based on volatility timing strategies, and the performance of covered options positions for index option writers. Our results support combining forecasts of implied volatility and realized volatility and illustrate that the realized volatility approach has economic value in the context of option pricing and risk management.

Occupational Labour Demand and the Sources of Non-neutral Technical Change*

This article introduces a two-step empirical approach for examining both the nature and sources of non-neutral technical change across multiple occupations. First, conventional labour-demand parameter estimates and unbiased tests for neutrality are obtained in the context of a flexible cost system. The resulting input-specific indices of technical change, unconstrained with respect to time path, facilitate subsequent evaluation of proposed sources. In our application to employment decisions of airline firms, we find labour-saving technical change that is non-neutral across occupations. We also document occupation-specific responses to aircraft technology adoption, route system developments and an unprecedented range of technical change elements.

LONG RANGE DEPENDENCE AND STRUCTURAL BREAKS IN THE GOLD MARKETS

The price of gold and its determining factors have been studied extensively in the literature. However, there is a lack of research on structural break in the long memory of the gold markets. This paper examines the long memory properties of gold prices. In particular, it attempts to test the stability of the long range dependence of gold returns and volatility. The results suggest that long memory exists in gold returns and volatility, and that the volatility of daily gold futures returns can be characterized by a hyperbolic decaying long memory process. Three episodes of structural breaks are found.

Factor pricing in commodity futures and the role of liquidity

This paper empirically investigates the pricing factors and their associated risk premiums of commodity futures. Existing pricing factors in equity and bond markets, including market premium and term structure, are tested in commodity futures markets. Hedging pressure in commodity futures markets and momentum effects is also considered. This study combines these factors to discuss their importance in explaining commodity future returns, while the literature has studied these factors separately. One of the important pricing factors in equity and bond markets is liquidity, but its role as a pricing factor in commodity futures markets has not yet been studied. To our knowledge, this research is the first to study liquidity as a pricing factor in commodity futures. The risk premiums of two momentum factors and speculators’ hedging pressure range from 2% to 3% per month and are greater than the risk premiums of roll yield (0.8%) and liquidity (0.5%). The result of a significant liquidity premium suggests that liquidity is priced in commodity futures.

Do Derivative Markets Contain Useful Information for Signaling 'Hot Money' Flows?

This study examines whether information from derivative markets is useful for signaling “hot money�? and other large capital flows in an economy where the monetary authority pursues a policy of exchange rate stability. Specifically, this study examines the information content of various Hong Kong traded derivative securities for signaling changes in the aggregate balance of the Hong Kong banking system during a period of intense IPO activity and speculation on the revaluation of the renminbi. The impact of the introduction of the Hong Kong Monetary Authority’s (HKMA) Convertibility Undertakings on the dynamic relationships among capital flows, stock market volatility and stock market turnover is also examined. Finally, the implications for monetary policymakers in potentially using information from derivative markets are assessed. The results show that derivative markets contain useful information for signaling “hot money�? flows. Granger causality tests from a VAR model show that Hong Kong dollar forward and RMB non-deliverable forward (NDF) prices predict future variation in the aggregate balance. Moreover, changes in aggregate balance has a significant impact on Hong Kong’s interbank rates. The findings also suggest that the introduction of the May 18, 2005 Convertibility Undertakings may have increased the credibility of the Linked Exchange Rate System by discouraging the use of the Hong Kong dollar and Hong Kong dollar denominated assets as speculative vehicles on RMB denominated assets.