LIQUIDITY AND ASSET PRICING UNDER THE THREE‐MOMENT CAPM PARADIGM

We examine whether the use of the three‐moment capital asset pricing model can account for liquidity risk. We also make a comparative analysis of a four‐factor model based on Fama–French and Pástor–Stambaugh factors versus a model based solely on stock characteristics. Our findings suggest that neither of the models captures the liquidity premium nor do stock characteristics serve as proxies for liquidity. We also find that sensitivities of stock return to fluctuations in market liquidity do not subsume the effect of characteristic liquidity. Furthermore, our empirical findings are robust to differences in market microstructure or trading protocols between NYSE/AMEX and NASDAQ.     

Capital structure around the world: The roles of firm- and country-specific determinants

We analyze the importance of firm-specific and country-specific factors in the leverage choice of firms from 42 countries around the world. Our analysis yields two new results. First, we find that firm-specific determinants of leverage differ across countries, while prior studies implicitly assume equal impact of these determinants. Second, although we concur with the conventional direct impact of country-specific factors on the capital structure of firms, we show that there is an indirect impact because country-specific factors also influence the roles of firm-specific determinants of leverage.     

Some Tests of APT Mispricing Using Mimicking Portfolios

Cross-sectional and time-series tests using mimicking portfolios are used to assess the exactness of the APT with(out) a residual market factor. The first factor seems to be sufficient to span the efficient set, whether the model is estimated using (un)conditional variance-covariance matrices that are (un)adjusted for nonsynchronous trading. Although the conditional standard deviations of the mimicking portfolios significantly explain the time-variability of security volatilities, the residuals of the mean equation still exhibit heteroskedasticity. Similar results are obtained for portfolios of CAPM-betaranked securities, and for randomly selected individual securities.     

Characterizations of negative multinomial distributions based on conditional distributions

In this paper characterizations of negative multinomial distributions based on conditional distributions have been studied.     

Nonlinearities and heterogeneity in environmental quality: An empirical analysis of deforestation

We use a panel data set of 59 developing countries over the 1972–1994 period to study the deforestation process. Relying on both parametric and semiparametric models, we examine nonlinearities and heterogeneity in the deforestation process. We first study the existence of an Environmental Kuznets Curve (EKC) and then analyze determinants of deforestation. Our data sample provides no evidence of an EKC. We also find that political institution failures may worsen the deforestation process in developing countries.     

Concessional Amnesty and the Politics of Immigration Reforms

This paper establishes a political support model of immigration reforms. The analysis highlights the distinction between border enforcement and employer sanction measures in that the former can be more aptly characterized as an income transfer from employer to native labor interests, whereas the latter generates deadweight losses that are borne entirely by producers. We show that while amnesty may appear to run contrary to the original intent of the immigration reform, it may nevertheless facilitate rent capture by the politician by "wiping the slate clean" and reducing the deadweight loss of employer sanction measures.     

Distribution Dynamics of CO<Subscript>2</Subscript> Emissions

This paper uses nonparametric methods to examine the convergence in CO₂ emissions per capita on a sample of 100 countries for the period 1966–1996. Industrial countries show a convergence pattern. However, there is little evidence of convergence for the whole sample.     

Do the Forward Sales of Real Estate Stabilize Spot Prices?

We examine the effect of forward sale (pre-sale) activities on the volatility of spot prices in the real estate market. The abundance of pre-sales data and major changes in regulatory control on the pre-sale market during the 90's in Hong Kong allow us to undertake empirical tests using Hong Kong's real estate data. Our results show that the volatility of spot prices increased significantly after forward sales were severely dampened by regulatory control measures introduced in 1994, but decreased again when the measures were partly relaxed in 1998. The results contribute to the long lasting debate on whether the introduction of a futures market reduces the volatility of spot prices. Previous studies were mainly conducted in markets with low transaction costs, notably financial markets. By utilizing the unique regulatory changes in the pre-sale market of Hong Kong, we are able to conduct an experiment on the conditional volatility of spot prices in a high information-cost environment, thereby shedding light on the important role of forward housing contracts in providing price expectation information for spot trading.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.