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The financialization of commodities documented in [Tang and Xiong (2012) Financial Analyst Journal, 68:54-74]has led commodity prices to exhibit not only time-varying volatility, but also price and volatility jumps. Using the class of stochastic volatility (SV) models, we incorporate such extreme price movements to generate out-of-sample hedge ratios. In-sample estimation on China's copper (CU) and aluminum (AL) spot and futures markets confirms the presence of price jumps and price-volatility jump correlations. Out-of-sample hedge ratios from the [Bates (1996) Review of Financial Studies, 9:69-107] SV with price jumps model deliver the greatest risk reduction on the unhedged positions at 59.55% for CU and 49.85% for AL. But it is the [Duffie, Pan, and Singleton (2000) Econometrica, 68:1343-1376] SV model with correlated price and volatility jumps that produces hedge ratios which yield the largest Sharpe Ratios of 0.644 for CU and 0.886 for AL.

Price discovery is an essential function performed by derivative markets. For a derivative exchange, its markets' ability to incorporate information into prices to "derive" the underlying asset's value is a key objective of market design. The J. Hasbrouck (1991a) model is applied to examine the design and price discovery of a futures market. First, the model is extended to consider a comprehensive dynamic interaction between the pricesize coordinates of orders and trades. Second, floor and screen tick data from LIFFE's FTSE 100 index futures market is used to estimate the two models. The significance of order size variables in the extended model suggests that order flow transparency, which is supported by an electronic trading platform, improves price discovery. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1107-1146, 2004 "The microstructure literature analyses how specific trading mechanisms affect the price formation process . . . and the process by which markets become efficient." O'Hara (1995, Chapt. 1, p. 1) This paper combines a theoretical and an empirical chapter from my Ph.D. thesis. Data funding support from the Department of Finance, Melbourne University is gratefully acknowledged. I thank Kim Sawyer and Christine Brown for their continuous assistance and support. I retain full property rights to all errors. For correspondence, Department of Accounting and Finance, Monash University, Clayton, VIC 3800, Australia; e-mail: michael.chng@buseco.monash.edu.au Received February 2004; Accepted June 2004 I Michael T. Chng is in the Department of Accounting and Finance at MonashUniversity in Clayton, VIC, Australia. The Appendix contains a list of all acronyms used in this paper. 2 For example, competition of order flow in cross-listed securities among stock exchanges, and contract proliferation among futures exchanges. INTRODUCTIONIn a general sense, market microstructure, which focuses on the analysis and comprehension of the data generating process of capital markets, matters to all empirical finance research. It matters to market participants by providing invaluable insights into the operation of capital markets, the intra-day price behavior of security prices and the welfare distribution to distinct investor clienteles. The latter point is demonstrated in the investigation by Christie and Schultz (1994) on NASDAQ 1 dealers. Market microstructure is also relevant to a financial exchange that hosts and regulates its markets' trading procedures, the quality and integrity of which contain direct competitive implications. This is relevant in the context of multiple trading venues.2 Such competitive forces are particularly acute when the trading hours of alternate venues overlap and when the geographical proximity of traders ceases to pose a barrier to trade. Both are increasingly true with technology facilitating access to trading platforms from across vast distances and time zones.From an exchange's perspective, issues in market microstructure are basically questions in market design. Policy makers ar...

We examine whether systematic higher moments capture beta asymmetry in an asset pricing model whereby the conditional beta of a risky asset increases (decreases) during a bear (bull) market state. We first provide a simple conceptual outline from the microeconomic literature to show that beta asymmetry is driven by time-varying higher-order risk preferences (prudence and temperance) across different market states. We then empirically relate these higher-order risk preferences to systematic skewness and systematic kurtosis. We find that beta asymmetry in Australian stock returns cannot be explained by Carhart (1997) 4-factor model but is subsumed by systematic higher moments. Accounting and Finance 54 (2014) 779-807 1 Fabozzi and Francis (1977) first develop a dual-beta model based on the Sharpe's oneperiod CAPM. Their model, however, does not allow the intercept term to vary with beta across bull and bear market environments.