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.

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...

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 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 proposes a new class of estimators based on the interquantile range of intraday returns, referred to as interquantile range based volatility (IQRBV), to estimate the integrated daily volatility. More importantly and intuitively, it is shown that a properly chosen IQRBV is jump-free...

We propose a stepwise test, Step-SPA(k), for multiple inequalities testing. This test is analogous to the Step-SPA test of Hsu, Hsu, and Kuan (2010, Journal of Empirical Finance) but has asymptotic control of a generalized familywise error rate: the probability of at least k false rejections....