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The purpose of this paper is to examine financial integration across North American stock markets from January 1984 to December 2003.

The paper uses an arbitrage pricing theory framework. The risk factors considered are the three Fama and French factors augmented with momentum for both countries as well as their international counterparts. Both the domestic and international four factor models in cross section and test for partial, mild, and strong financial integration are estimated. The domestic and international model are estimated on domestic portfolios and on a subset of Canadian cross listings matched with American stocks.

Results can be summarized as follows: first, results show stronger evidence of mild rather than partial or strong integration in both domestic portfolios and interlisted stocks. Second, interlisted stocks appear at first glance to be more integrated than the domestic portfolios, but this result can be attributed to the poor explanatory power of the models applied to interlisted stocks. Once the authors rule out the case where the model does not generate statistically important risk premiums for both countries, the evidence of integration is similar in both domestic and interlisted stocks. Third, the domestic and international models have similar explanatory power, although the domestic model performs better with the Canadian interlisted stocks are found.

The results suggest that, in an international context, a portfolio manager is better off using the four factor model as a benchmark in cross sections rather than the single market. Furthermore, if the agency problem described in Karolyi is ignored, Canadian interlisted stocks and Canadian domestic portfolios have the same diversification potential.

Statistical inference (estimation and testing) for the stochastic volatility (SV) model Taylor (1982, 1986) is challenging, especially likelihood-based methods which are difficult to apply due to the presence of latent variables. The existing methods are either computationally costly and/or inefficient. In this paper, we propose computationally simple estimators for the SV model, which are at the same time highly efficient. The proposed class of estimators uses a small number of moment equations derived from an ARMA representation associated with the SV model, along with the possibility of using “winsorization” to improve stability and efficiency. We call these ARMA-SV estimators. Closed-form expressions for ARMA-SV estimators are obtained, and no numerical optimization procedure or choice of initial parameter values is required. The asymptotic distributional theory of the proposed estimators is studied. Due to their computational simplicity, the ARMA-SV estimators allow one to make reliable – even exact – simulation-based inference, through the application of Monte Carlo (MC) test or bootstrap methods. We compare them in a simulation experiment with a wide array of alternative estimation methods, in terms of bias, root mean square error and computation time. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that ARMA-SV estimators match (or exceed) alternative estimators in terms of precision, including the widely used Bayesian estimator. The proposed methods are applied to daily observations on the returns for three major stock prices (Coca-Cola, Walmart, Ford) and the S&P Composite Price Index (2000–2017). The results confirm the presence of stochastic volatility with strong persistence.

The purpose of this paper is to show that multivariate t-distribution assumption provides a better description of stock return data than multivariate normality assumption.

The EM algorithm is applied to solve the statistical estimation problem almost analytically, and the asymptotic theory is provided for inference.

The authors find that the multivariate normality assumption is almost always rejected by real stock return data, while the multivariate t-distribution assumption can often be adequate. Conclusions under normality vs under t can be drastically different for estimating expected returns and Jensen’s αs, and for testing asset pricing models.

The results provide improved estimates of cost of capital and asset moment parameters that are useful for corporate project evaluation and portfolio management.

The authors proposed new procedures that makes it easy to use a multivariate t-distribution, which models well the data, as a simple and viable alternative in practice to examine the robustness of many existing results.