We propose computing HAC covariance matrix estimators based on one-stepahead forecasting errors. It is shown that this estimator is consistent and has smaller bias than other HAC estimators. Moreover, the tests that rely on this estimator have more accurate sizes without sacrificing its power.

This paper investigates the causal relations between stock return and volume based on quantile regressions. We first define Granger non-causality in all quantiles and propose testing non-causality by a sup-Wald test. Such a test is consistent against any deviation from non-causality in...

In this article we reexamine the profitability of technical analysis using White`s reality check and Hansen`s SPA test that correct the data snooping bias. Compared to previous studies, we study a more complete quot;universequot; of trading techniques, including not only simple rules but also...

In this paper we propose a downside risk measure, the expectile-based Value at Risk (EVaR), which is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). The index $\theta$ of an EVaR is the relative cost of the expected margin shortfall and hence...

In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the quot;superior predictive abilityquot; (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify predictive models without...

This paper applies a neural network model to intraday data of exchange rate futures between January 1990 and July 1992. We use the neural network model and the "lag selection method" to explore the intraday patterns of futures market exchange rates. The approach is particularly useful in...