Dongming Zhu

French Automobiles and the Chinese Boycotts of 2008: Politics Really Does Affect Commerce

Abstract We explore the economic impact of boycotts of French automobiles in China during the time of the 2008 Beijing Olympics. Conditions were favorable for a boycott, enabling Chinese consumers to overcome the collective action problems that can prevent boycott success and other voluntary contributions to public goods. We use brand and model level data in a difference-in-difference specification to investigate the boycotts’ effects on sales. A robust pattern of large impacts emerges: sales of French automobile brands fell 25-33 percent or more. Consumers substituted mostly toward Chinese and other Asian cars. The sales of the French models did not experience similar relative sales declines in countries other than China—triple-difference estimates point toward even larger relative loss of market share in China. Our results provide evidence that commerce can be used as an effective political weapon.

Forecasting Expected Shortfall with a Generalized Asymmetric Student-T Distribution

Financial returns typically display heavy tails and some skewness, and conditional variance models with these features often outperform more limited models. The difference in performance may be especially important in estimating quantities that depend on tail features, including risk measures such as the expected shortfall. Here, using a recent generalization of the asymmetric Student-t distribution to allow separate parameters to control skewness and the thickness of each tail, we fit daily financial returns and forecast expected shortfall for the SP the generalized distribution is used for the standardized innovations in a nonlinear, asymmetric GARCH-type model. The results provide empirical evidence for the usefulness of the generalized distribution in improving prediction of downside market risk of financial assets. De facon generale, les rendements financiers sont caracterises par des queues epaisses et une certaine asymetrie. Ainsi, les modeles a variance conditionnelle dotes de ces caracteristiques donnent de meilleurs resultats que les modeles plus limites. La difference dans les resultats obtenus peut etre particulierement importante lorsquil sagit devaluer des quantites qui dependent des caracteristiques des queues, y compris les mesures du risque, tel que le manque a gagner prevu. Dans le cas actuel, en recourant a une generalisation recente de la distribution asymetrique suivant la loi t de Student, de sorte que des parametres distincts limitent lasymetrie et lepaisseur de chaque queue, nous integrons les rendements financiers quotidiens et estimons le manque a gagner prevu dans le cas de lindice S&P 500 et de certaines actions de compagnies individuelles. La distribution generalisee est utilisee pour les innovations normalisees contenues dans un modele asymetrique non lineaire de type GARCH. Les resultats demontrent de facon empirique lutilite de la distribution generalisee pour ameliorer les previsions au sujet du risque de perte en cas de baisse du marche des actifs financiers.

HUMAN CAPITAL AND INNOVATION IN A MONETARY SCHUMPETERIAN GROWTH MODEL

This study explores the growth and welfare effects of monetary policy in a scale-invariant Schumpeterian growth model with endogenous human capital accumulation. We model money demand via a cash-in-advance (CIA) constraint on R&D investment. Our results can be summarized as follows. We find that an increase in the nominal interest rate leads to a decrease in R&D and human capital investment, which in turn reduces the long-run growth rates of technology and output. This result stands in stark contrast to the case of exogenous human capital accumulation in which the long-run growth rates of technology and output are independent of the nominal interest rate. Simulating the transitional dynamics, we find that the additional long-run growth effect under endogenous human capital accumulation amplifies the welfare effect of monetary policy. Decreasing the nominal interest rate from 10% to 0% leads to a welfare gain that is equivalent to a permanent increase in consumption of 2.82% (2.38%) under endogenous (exogenous) human capital accumulation.

Goodness-of-Fit Test In Multivariate Jump Diffusion Models

In this article, we develop a new goodness-of-fit test for multivariate jump diffusion models. The test statistic is constructed by a contrast between an “in-sample” likelihood (or a likelihood of observed data) and an“out-of-sample” likelihood (or a likelihood of predicted data). We show that under the null hypothesis of a jump diffusion process being correctly specified, the proposed test statistic converges in probability to a constant that equals to the number of model parameters in the null model. We also establish the asymptotic normality for the proposed test statistic. To implement this method, we invoke a closed-form approximation to transition density functions, which results in a computationally efficient algorithm to evaluate the test. Using Monte Carlo simulation experiments, we illustrate that both exact and approximate versions of the proposed test perform satisfactorily. In addition, we demonstrate the proposed testing method in several popular stochastic volatility models for time series of weekly S&P 500 index during the period of January 1990 and December 2014, in which we invoke a linear affine relationship between latent stochastic volatility and the implied volatility index.