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International audienceWe investigate whether liquidity introduces or helps resolve uncertainty in Phase I and the first year of Phase II of the European carbon futures market. We propose a distinction between ‘absolute’ or overall liquidity and that which is ‘relative’ to a benchmark. For this purpose, we suggest volume-weighted duration as a natural measure of trading intensity as a proxy for liquidity, and we model it as a rescaled temporal point process. The new model is called Autoregressive Conditional Weighted Duration (ACWD) and is shown to outperform its discrete modelling counterparts. Liquidity is found to play a dual role, with higher relative liquidity introducing uncertainty and higher absolute liquidity accelerating uncertainty resolution, thus, enhancing market efficiency

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This paper investigates whether a particular magnitude and direction of interregional return signal transmission dominates the performance of domestic trading in American, European and Australasian stock markets. A trading system design, based on fuzzy logic rules, combines direct and indirect channels of foreign information transmission, modelled by stochastic parameter regressions, with domestic momentum information to generate stock market trading signals. Filters that control for magnitude and direction of trading signals are then used to investigate incremental impact on economic performance of the proposed investment system. The results indicate that at reasonable levels of transaction costs very profitable trades that are fewer in number do not increase investment performance as much as trades based on foreign information of a specific low-to-medium daily return magnitude of 0.5% to 0.75%. These information-based strategies are profitable on risk-adjusted bases and relative to a market, but performance declines considerably when traded instruments are used.

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Carry returns have been widely observed in the FX market. This study exploits the common information embedded in several factors previously identified as relevant to carry trade returns. We find that the extracted common factor successfully models the time series and cross-sectional characteristics of carry returns. Empirical evidence is presented that the common factor produces smaller pricing errors than other well known factors, such as innovations of exchange rate volatility and the downside stock market excess return. Our results also suggest that stock market risk is somewhat segmented from FX market risk. JEL Classification Codes: C38, F31, G12, G15