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Using Canadian data, the consumption-based asset pricing model is studied, defined in terms of nondurable and durable goods consumption. A two-stage estimation procedure is used, which takes account of the [...]

I. INTRODUCTION Recently, Campbell and Mankiw (1989, 1990) have demonstrated that most empirical findings in the aggregate consumption literature can be explained by a simple model in which some fraction [...]

ABSTRACTThis paper discusses the management of climate change risks for equity investments and presents a scenario-based framework for building sustainable portfolios under the climate change scheme. An empirical analysis is first performed using historical price data to show the inferior risk-adjusted performance of the carbon-intensive industries in the North American stock market, which supplements evidence from existing literature in the market's gradual pricing of the climate change risk. Risk management modules are devised with subjective top-level constraints to achieve comprehensive coverage of the key aspects of climate change: risk exposures are measured by carbon intensities, while the risk impacts are quantified through equity return impact scenarios derived from climate change paths under Integrated Assessment Models. A model for quantifying stranded asset risk is also presented. Results from these modules formulate the joint posterior return distribution of the stocks that are used to construct the mean-variance optimal portfolio.

Abstract: Selecting an estimator for the covariance matrix of a regression’s parameter estimates is an important step in hypothesis testing. From less to more robust estimators, the choices available to researchers include Eicker/White heteroskedasticity-robust estimator, cluster-robust estimator, and multi-way cluster-robust estimator. The rationale for choosing a less robust covariance matrix estimator is that tests conducted using this estimator can have better power properties. This motivates tests that examine the appropriate level of robustness in covariance matrix estimation. In this paper, we propose a new robustness testing strategy, and show that it can dramatically improve inference about the proper level of robustness in covariance matrix estimation. In an empirically relevant example, namely the placebo treatment application of , the power of the proposed robustness testing strategy against the null hypothesis “no clustering” is 0.82 while the power of the existing robustness testing approach against the same null is only 0.04. We also show why the existing clustering test and other applications of the  robustness testing approach often perform poorly, which to our knowledge has not been well understood. The insight into why this existing testing approach performs poorly is also the basis for the proposed robustness testing strategy. [Copyright &y& Elsevier]

There is a substantial empirical literature which examines the relationship between private and public consumption. The conclusions from this literature, however, are generally mixed. In this paper, we attempt to provide some additional evidence on this relationship. We consider a two-good permanent-income model which allows us to estimate both the intraperiod and intertemporal elasticities of substitution. The estimation strategy proceeds in two steps. In the first step we use cointegration methods to estimate the intraperiod preference parameter, while in the second step we estimate the intertemporal parameter via a generalized method of moments. A useful implication of this approach is that it allows us to use the estimated preference parameters to shed some light on whether private and public consumptions are best described as complements, substitutes, or unrelated occurrences (in a Edgeworth–Pareto sense).Journal of Economic LiteratureClassification Numbers: C22, E21.

This paper studies regression models with a lagged dependent variable when both the dependent and independent variables are nonstationary, and the regression model is misspecified in some dimension. In particular, we discuss the limiting properties of leastsquares estimates of the parameters in such regression models, and the limiting distributions of their test statistics. We show that the estimate of the lagged dependent variable tends to unity asymptotically independent of its true value, while the estimates of the independent variables tend to zero. The limiting distributions of their test statistics are shown to diverge with sample size.

This paper studies a stochastic conditional duration model running on multiple time scales with the aim of better capturing the dynamics of a duration process of financial transaction data. New Markov chain Monte Carlo (MCMC) algorithms are developed for the model under three distributional assumptions about the innovation of the measurement equation for a two-component model. Simulation results suggest that the proposed model and MCMC method improve in-sample fits and duration forecasts. Most importantly applications to FIAT and IBM duration datasets indicate the existence of at least two factors (or components) governing the dynamics of the financial duration process.

In this paper, we investigate the nature of sovereign credit risk for selected Asian and European countries based on a set of sovereign CDS data over an eight-year period that includes the episode of the 2007–2008 global financial crisis. Our results indicate that there exists strong commonality in sovereign credit risk among the countries studied in this paper following the crisis. In addition, our results also show that commonality is importantly associated with both local and global financial and economic variables. However, there are markedly different impacts of the sovereign of credit risk in Asian and European countries. Specifically, we find that foreign reserve, global stock market, and volatility risk premium, affect Asian and European sovereign credit risks in the opposite direction. Lastly, we model the arrival rates of credit events as a square-root diffusion process from which a pricing model is constructed and estimated over pre- and post-crisis periods. Then the resulting model is used to decompose credit spreads into risk premium and credit-event components. For most countries in our study, credit-event components appear to weight more than risk-premiums.