Marie-Claude Beaulieu

Multivariate Tests of Mean–Variance Efficiency With Possibly Non-Gaussian Errors

We develop exact mean–variance efficiency tests of the market portfolio in the context of (conditional and unconditional) capital asset pricing models (CAPM), allowing for a wide class of possibly non-Gaussian error distributions. The proposed procedures are applicable in a general multivariate linear regression framework, and exactness is achieved through Monte Carlo test techniques. We also perform exact multivariate diagnostic checks. Empirical results show that the Gaussian assumption is rejected, temporal instabilities are apparent, and mean–variance efficiency is rejected over several subperiods, but finite-sample methods that allow for nonnormality and conditioning information substantially reduce the number of rejections.

Political Uncertainty and Stock Market Returns: Evidence from the 1995 Quebec Referendum

In this study, we investigate the short run effect of the 30 October 1995 Quebec referendum on the common stock returns of Quebec firms. Our results show that the uncertainty surrounding the referendum outcome had an impact on stock returns of Quebec firms. We also find that the effect of the referendum varied with the political risk exposure of Quebec firms, that is, the structure of assets and principally the degree of foreign involvement.

Identification-Robust Estimation and Testing of the Zero-Beta CAPM

We propose exact simulation-based procedures for: (i) testing mean-variance efficiency when the zero-beta rate is unknown, and (ii) building confidence intervals for the zero-beta rate. On observing that this parameter may be weakly identified, we propose LR-type statistics as well as heteroskedascity and autocorrelation corrected (HAC) Wald-type procedures, which are robust to weak identification and allow for non-Gaussian distributions including parametric GARCH structures. In particular, we propose confidence sets for the zero-beta rate based on inverting exact tests for this parameter; these sets provide a multivariate extension of Fiellers technique for inference on ratios. The exact distribution of LR-type statistics for testing efficiency is studied under both the null and the alternative hypotheses. The relevant nuisance parameter structure is established and finite-sample bound procedures are proposed, which extend and improve available Gaussianspecific bounds. Furthermore, we study the invariance to portfolio repacking property for tests and confidence sets proposed. The statistical properties of available and proposed methods are analyzed via aMonte Carlo study. Empirical results on NYSE returns show that exact confidence sets are very different from the asymptotic ones, and allowing for non-Gaussian distributions affects inference results. Simulation and empirical results suggest that LR-type statistics - with p-values corrected using the Maximized Monte Carlo test method - are generally preferable to their Wald-HAC counterparts from the viewpoints of size control and power.

Time to maturity in the basis of stock market indices: Evidence from the S&P 500 and the MMI

Abstract This paper focuses on the behaviour of the basis in stock market index futures contracts over the lifetime of futures contracts. The model in this paper relaxes the cost of carry model assumptions of constant interest rate and known dividend yield over the lifetime of futures contracts. This allows for a test of the presence of time to maturity in the conditional variance of the model using GARCH. The empirical evidence reveals that, consistent with Samuelsons (1995) analysis, time to maturity is a determinant of the conditional variance of the basis. Furthermore, it implies that time to maturity cannot be accounted for by transaction costs or cost of carry.

Finite sample multivariate tests of asset pricing models with coskewness

Exact inference methods are proposed for asset pricing models with unobservable risk-free rates and coskewness; specifically, the Quadratic Market Model (QMM) which incorporates the effect of asymmetry of return distribution on asset valuation. In this context, exact tests are appealing given (i) the increasing popularity of such models in finance, (ii) the fact that traditional market models (which assume that asset returns move proportionally to the market) have not fared well in empirical tests, (iii) finite sample QMM tests are unavailable even with Gaussian errors. Empirical models are considered where the procedure to assess the significance of coskewness preference is LR-based, and relates to the statistical and econometric literature on dimensionality tests which are interesting in their own right. Exact versions of these tests are obtained, allowing for non-normality of fundamentals. A simulation study documents the size and power properties of asymptotic and finite sample tests. Empirical results with well-known data sets reveal temporal instabilities over the full sampling period, namely 1961-2000, though tests fail to reject the QMM restrictions over 5-year sub-periods.

A cross‐section analysis of financial market integration in North America using a four factor model

Purpose - The purpose of this paper is to examine financial integration across North American stock markets from January 1984 to December 2003. Design/methodology/approach - 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. Findings - 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. Originality/value - 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.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Less is More: Testing Financial Integration Using Identification-Robust Asset Pricing Models

We revisit financial market integration and study the impact of multiple risk factors and model specification on inference. Our tests exploit a method correct in finite sample that jointly assesses coefficient significance and detects identification problems. Results on four countries show that multiple sources of risk in international asset pricing models lead to lack of identification and spurious inference. We find that domestic factor models are well identified which is not the case for global and international models. Nonetheless, domestic models do not provide a base for testing financial market integration. Given that constraint, the best-identified international model includes few factors and reveals that financial integration varies over time and across countries.

Firm-specific risk and IPO market cycles

This article characterizes the role of risk in the initial public offering (IPO) cycle. While most of the previous literature uses the volatility of IPO initial returns to measure risk, we focus on different risk measures, namely firm-level systematic and idiosyncratic volatilities and the market-wide implied volatility index (VIX), to assess their role in the IPO cycle. Our results shed new light on (1) which risk measure is important in the determination of IPO cycles, (2) the temporal pattern of each risk component across issuing firms and (3) the relationship between market-wide uncertainty and IPO risk. Our findings reveal a lead-lag relationship between IPO waves, VIX and the IPO systematic risk measure. We also highlight the fact that market-level uncertainty predicts IPO activity and the level of idiosyncratic risk of the next-period-issuing firms. Issuing firms’ systematic risk can only be predicted by the systematic risk of firms now proceeding to their offering. The main implication resulting from our study is that one can better anticipate ‘hot-issue’ markets, as well as the specific risk components of future new issues. This will help improve upon the regulatory environment, IPO investment decisions and IPO timing given market receptivity.

The impact of firm-specific information during the registration period on initial public offering pricing

In this article, we gather public information at the firm level during the initial public offering (IPO) registration period and examine its impact on IPO pricing. First, we show that the firm origin and the number of IPO amendments filed during the registration period are correlated with negative news, whereas positive news is essentially driven by previous IPO experience and underwriter prestige. Second, our main findings reveal that the type of firm-specific information (good or bad news) during the registration period has implications for initial returns, pricing and trading volume. These results support the idea that the pricing process of IPOs is not completely efficient.