Bank Testing Linear Factor Pricing Models with Large Cross-Sections: A Distribution-Free Approach

We develop a finite-sample procedure to test the beta-pricing representation of linear factor pricing models that is applicable even if the number of test assets is greater than the length of the time series. Our distribution-free framework leaves open the possibility of unknown forms of non-normalities, heteroskedasticity, time-varying correlations, and even outliers in the asset returns. The power of the proposed test procedure increases as the time-series lengthens and/or the cross-section becomes larger. This stands in sharp contrast to the usual tests that lose power or may not even be computable if the cross-section is too large. Finally, we revisit the CAPM and the Fama-French three factor model. Our results strongly support the mean-variance efficiency of the market portfolio.Econometric and statistical methods; Financial markets

Multivariate tests of mean-variance efficiency and spanning with a large number of assets and time-varying covariances

We develop a finite-sample procedure to test the mean-variance efficiency and spanning hypotheses, without imposing any parametric assumptions on the distribution of model disturbances. In so doing, we provide an exact distribution-free method to test uniform linear restrictions in multivariate linear regression models. The framework allows for unknown forms of nonnormalities as well as time-varying conditional variances and covariances among the model disturbances. We derive exact bounds on the null distribution of joint F statistics to deal with the presence of nuisance parameters, and we show how to implement the resulting generalized nonparametric bounds tests with Monte Carlo resampling techniques. In sharp contrast to the usual tests that are not even computable when the number of test assets is too large, the power of the proposed test procedure potentially increases along both the time and cross-sectional dimensions

Announcing the Bankers\u27 Acceptance Purchase Facility: a COVID”‘19 event study

Research paper studying the announcement effect of the BAPF using different estimation effects and treatment variable

The impact of the Bank of Canada\u27s Government Bond Purchase Program

article estimating the announcement and purchasing effects of the GBP

Bootstrap Tests of Mean-Variance Efficiency with Multiple Portfolio Groupings

We propose double bootstrap methods to test the mean-variance efficiency hypothesis when multiple portfolio groupings of the test assets are considered jointly rather than individually. A direct test of the joint null hypothesis may not be possible with standard methods when the total number of test assets grows large relative to the number of available time-series observations, since the estimate of the disturbance covariance matrix eventually becomes singular. The suggested residual bootstrap procedures based on combining the individual group p-values avoid this problem while controlling the overall significance level. Simulation and empirical results illustrate the usefulness of the joint mean-variance efficiency tests

Exact distribution-free tests of mean-variance efficiency

This paper develops exact distribution-free tests of unconditional mean-variance efficiency. These new tests allow for unknown forms of non-normalities, conditional heteroskedasticity, and other non-linear temporal dependencies among the absolute values of the error terms in the asset pricing model. Exactness here rests on the assumption that the joint temporal error density is symmetric around zero. This still leaves open the possibility of return distribution asymmetry via coskewness with the benchmark portfolio. A simulation study shows that the new tests have very good power relative to that of many commonly used tests. The inference procedures developed are further illustrated by tests of the mean-variance efficiency of a market index using a 42-year sample of monthly returns on ten U.S. equity portfolios.CAPM Conditional heteroskedasticity Non-parametric tests Robust inference

Comparison between the "one-step" and "two-step" catalytic pyrolysis of pine bark

In this study, one-step and two-step pyrolysis systems were compared in the pyrolysis of pine bark. One-step pyrolysis was performed in a fixed bed reactor with and without catalyst. Two-step pyrolysis was carried out in a dual reactor system over catalyst; the first reactor containing no catalyst whereas the second reactor containing catalyst to upgrade the thermally cracked products. The catalysts used in the pyrolysis systems were ReUS-Y, red mud and ZSM-5. In thermal pyrolysis, the pyrolysis system mainly affected the relative amount of bio-oil. The bio-oil yields obtained from two-step thermal pyrolysis were higher than the yields from one-step thermal pyrolysis. In the catalytic runs, ReUS-Y catalyst slightly decreased the char formation with a consequent increase in aqueous phase yield in the case of one-step pyrolysis. However, the catalysts decreased the bio-oil yield with a consequent increase in the gas yield in the case of two-step pyrolysis. The general compositions of bio-oils obtained from both two pyrolysis systems were affected by using catalysts. In the case of one-step pyrolysis, the formation of water and water soluble compounds were reduced by using ReUS-Y catalyst. In the case of two-step pyrolysis, both ZSM-5 and red mud increased the formation of water soluble compounds while they decreased water formation. In contrast, ReUS-Y decreased the formation of water soluble compounds and increased the amount of pyrolytic lignin compounds in bio-oil. Fuel characteristics of pyrolysis products (gas, bio-oil and char) for both two pyrolysis systems were also investigated comparatively. (c) 2012 Elsevier B.V. All rights reserved