Gregory Connor

Performance measurement with the arbitrage pricing theory : A new framework for analysis

This paper develops a theory and econometric method of portfolio performance measurement using a competitive equilibrium version of the Arbitrage Pricing Theory. We show that the Jensen coefficient and the appraisal ratio of Treynor and Black are theoretically compatible with the Arbitrage Pricing Theory. We construct estimators for the two performance measures using a new principal components technique, and describe their asymptotic distributions. The estimators are computationally feasible using a large number of securities. We also suggest a new approach to testing for the correct number of factors.

Risk and Return in an Equilibrium Apt: Application of a New Test Methodology

We use an asymptotic principal Components technique to estimate pervasive factors influencing asset returns and to test the restrictions imposed by static and intertemporal equilibrium versions of the arbitrage pricing theory (APT) on a multivariate regression model. The empirical techniques allow for fairly arbitrary time variation in risk premiums. We find that the APT provides a better description of the expected returns on assets than the capital asset pricing model (CAPM). However, some statistically reliable mipricing of assets by the APT remains.

A unified beta pricing theory

Abstract This paper derives Rosss mutual fund separation theory and a new, equilibrium version of Rosss arbitrage pricing theory as special cases of a general theory. The paper also reveals that the two theories are identical in their predictions of asset prices and portfolio returns. The capital asset pricing model (a restricted case of the mutual fund separation theory) receives special treatment.

The Attributes, Behavior, and Performance of U.S. Mutual Funds

This article examines the risk and return characteristics of U.S. mutual funds. We employ an equilibrium version of the Arbitrage Pricing Theory (APT) and a principal-components-based statistical technique to identify performance benchmarks. We also consider the Capital Asset Pricing Model (CAPM) as an alternative. We implement a procedure for overcoming the rotational indeterminacy of factor models. This procedure is a hybrid of statistical factor estimation and prespecification of factors. We estimate measures of timing ability for the CAPM and extend it to the APT. We find that this timing test is misspecified due to noninformation-based changes in mutual fund betas. We develop a modification of the timing measure that, under certain conditions, distinguishes true timing ability from noninformation-based beta changes.

The U.S. and Irish credit crises: Their distinctive differences and common features

Although the 2007–2008 US credit crisis precipitated it, the subsequent Irish credit crisis is an identifiably separate one, which might have occurred in the absence of the U.S. crash. The distinctive differences between them are notable. Many of the apparent causal factors of the U.S. crisis are missing in the Irish case; and the same applies vice versa. At a deeper level, we identify four common features of the two credit crises: capital bonanzas, asset price bubbles, regulatory imprudence, and moral hazard. The particular manifestations of these four “deep” common features are quite different in the two cases.

Semiparametric estimation of a characteristic-based factor model of common stock returns

We introduce an alternative version of the Fama-French three-factor model of stock returns together with a new estimation methodology. We assume that the factor betas in the model are smooth nonlinear functions of observed security characteristics. We develop an estimation procedure that combines nonparametric kernel methods for constructing mimicking portfolios with parametric nonlinear regression to estimate factor returns and factor betas simultaneously. The methodology is applied to US common stocks and the empirical findings compared to those of Fama and French.

EFFICIENT SEMIPARAMETRIC ESTIMATION OF THE FAMA-FRENCH MODEL AND EXTENSIONS

This paper develops a new estimation procedure for characteristic-based factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time-varying weights and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a time-series and cross-sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic-beta functions. By avoiding the curse of dimensionality, our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the three-factor Fama–French model, Carhart’s four-factor extension of it that adds a momentum factor, and a five-factor extension that adds an own-volatility factor. We find that momentum and own-volatility factors are at least as important, if not more important, than size and value in explaining equity return comovements. We test the multifactor beta pricing theory against a general alternative using a new nonparametric test

How much structure is best? A comparison of market model, factor model and unstructured equity covariance matrices

This paper compares three approaches to estimating equity covariance matrices: a factor model, a market model and an unstructured asset-by-asset model. These approaches make different trade-offs between estimation variance and model specification error. We explore this trade-off with a simulation experiment and with an empirical analysis of UK equity portfolios. The factor model is found to perform best for large investment universes and typical sample lengths. The market model underperforms due to excessive specification error while an asset-by-asset model with a short half-life of 22 days underperforms due to high estimation variance. The importance of properly accounting for serial correlation is highlighted.

Factor Models in Portfolio and Asset Pricing Theory

The foundation of modern portfolio theory is the mean–variance portfolio selection approach of Markowitz (Journal of Finance 7:77–91, 1952; Portfolio Selection: Efficient Diversification of Investments, Wiley, New York, 1959). We discuss the role of factor models in implementing portfolio selection, defining the nature of systematic risk, and estimating the premium for risk bearing.

Dynamic Stock Market Covariances in the Eurozone

This paper examines the short-term dynamics, macroeconomic sensitivities, and longer-term trends in the variances and covariances of national equity market index daily returns for eleven countries in the Euro currency zone. We modify Colacito, Engle and Ghysels Mixed Data Sampling Dynamic Conditional Correlation Garch model to include a new scalar measure for the degree of correlatedness in time-varying correlation matrices. We also explore the robustness of the findings with a less model-dependent realized covariance estimator. We find a secular trend toward higher correlation during our sample period, and significant linkages between macroeconomic and market-wide variables and dynamic correlation. One notable finding is that average correlation between these markets is lower when their average GDP growth rate is lower or when more of them have negative GDP growth.