Search

This article proposes a general framework of a Markov Switching GARCH model with a mixture of truncated Gaussian to model asset returns with price limits in China. Theoretically, while retaining many convenient statistical properties of the Gaussian distribution, the proposed model also assumes a flexible time-varying volatility structure to accommodate the feature of the return data under price restrictions in China, such as the clusters near the bounds (due to the 'bound effect'). Empirically, we apply the model to eight representative stocks from Shanghai and Shenzhen stock markets in China. Lastly, we find that our proposed model dominates the conventional volatility models in terms of Value-at-Risk measures. [ABSTRACT FROM AUTHOR]

This study investigates the development and sustainability of a firm's information technology (IT) capability reputation from an IT executive's standpoint. Building on institutional theory, we argue that IT executives will try to achieve external legitimacy (i.e., project an image of superior IT capability to external stakeholders) in the hope that the top management team and board members will reciprocate by elevating the internal legitimacy of IT executives. Firms that develop such a culture of reciprocity with their IT executives are more likely to sustain their IT capability reputation. Econometric results based on panel data for 1,326 large U.S. firms from a wide spectrum of industries over a 13-year period (1997-2009) validate these predictions. More specifically, we find that IT executives with greater structural power (e.g., higher job titles) or IT-related expert power (e.g., IT-related education or experience) are more likely to attract public recognition for their firm's IT capability. Firms that build such an IT capability reputation are more likely to promote their IT executives, and IT executives who are promoted are more likely to stay longer with their firms. This continuity in IT strategic leadership is positively associated with the firm's ability to sustain its IT capability reputation. Our findings have important practical implications related to a firm's IT reputation strategy as well as the motivation and career of IT executives. Firms wanting to develop and sustain their IT capability reputation would do well to foster the creation of a cycle of positive reciprocity with their IT executives. IT executives hoping to increase their power within their firm's top management team and improve the legitimacy of the firm's IT organization need to project an image of IT superiority to external stakeholders. [ABSTRACT FROM AUTHOR]

Organizations seek to differentiate themselves in the marketplace by deploying information technology (IT) to develop dynamic IT capabilities and resist competitors' attempts to imitate or improve these capabilities. While this strategy has been justified on the grounds that dynamic IT capabilities are durably heterogeneous, there does not seem to be empirical evidence supporting or refuting this assumption. This study empirically validates the assumption of durable heterogeneity of dynamic organizational IT capability (ITC) due to path dependence. We capture ITC heterogeneity by introducing a framework in which firms try to achieve ITC leadership in their industry and we propose that durable ITC heterogeneity can be attributed to path dependence, and hence, it can be tested using Heckman's true state dependence of ITC leadership status. Using random and fixed effect dynamic logit models, we investigate true state dependence of ITC leadership on a sample of large U.S. firms. The results, which are robust to alternative sample, dependent, and control variable specifications, show that achieving ITC leadership is a true state-dependent process, suggesting durable heterogeneity of ITC due to path dependence. The study contributes to the dynamic capabilities literature and has important managerial implications. The proposed framework for conceptualizing durable resource heterogeneity due to path dependence is general and versatile, thus providing a foundation for future research on dynamic capabilities. The findings provide empirical evidence to confirm that ITC is durably heterogeneous and should be managed as a potential source of competitive advantage. [ABSTRACT FROM AUTHOR]

This 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 FROM AUTHOR]

This paper conducts simulation-based comparison of several stochastic volatility models with leverage effects. Two new variants of asymmetric stochastic volatility models, which are subject to a logarithmic transformation on the squared asset returns, are proposed. The leverage effect is introduced into the model through correlation either between the innovations of the observation equation and the latent process, or between the logarithm of squared asset returns and the latent process. Suitable Markov Chain Monte Carlo algorithms are developed for parameter estimation and model comparison. Simulation results show that our proposed formulation of the leverage effect and the accompanying inference methods give rise to reasonable parameter estimates. Applications to two data sets uncover a negative correlation (which can be interpreted as a leverage effect) between the observed returns and volatilities, and a negative correlation between the logarithm of squared returns and volatilities. [ABSTRACT FROM AUTHOR]

This article focuses on simulation-based inference for the time-deformation models directed by a duration process. In order to better capture the heavy tail property of the time series of financial asset returns, the innovation of the observation equation is subsequently assumed to have a Student-tdistribution. Suitable Markov chain Monte Carlo (MCMC) algorithms, which are hybrids of Gibbs and slice samplers, are proposed for estimation of the parameters of these models. In the algorithms, the parameters of the models can be sampled either directly from known distributions or through an efficient slice sampler. The states are simulated one at a time by using a Metropolis-Hastings method, where the proposal distributions are sampled through a slice sampler. Simulation studies conducted in this article suggest that our extended models and accompanying MCMC algorithms work well in terms of parameter estimation and volatility forecast. [ABSTRACT FROM AUTHOR]

This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes and innovations of observed duration process and latent log duration process. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allow comparison of our models and methods to those existing in the literature. [ABSTRACT FROM PUBLISHER]

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 presence of common stochastic trends in the forcing processes. This method yields more reasonable estimates of the preference parameters than the previous studies did, and the asset-pricing equation is not rejected by the data. Moreover, the preference specification adopted in this paper allows a number of useful economic information to be obtained. The additive separability assumption and the Cobb-Douglas functional form of the utility function are ruled complements in the sense of Edgeworth and Pareto. [ABSTRACT FROM AUTHOR]

This paper proposes a simple, partial equilibrium model for studying an individual's migration decisions. It shows that an individual may choose to delay migration when the condition appears to be favorable, giving rise to the “waiting” behavior observed in the data. Using a closed-form solution, it also examines how the duration of the waiting is affected by a number of economic factors such as the risks associated with the wages in regions of origin and destination, the individual's attitude toward risk, etc. [ABSTRACT FROM AUTHOR]

This paper estimates and tests several versions of the consumption-based asset pricing model extended to allow for time-nonseparable preferences and/or liquidity constraint proxies, using Canadian aggregate data. It is found that a habit-persistence effect uncovered in the time-nonseparable preference model is due to the model's misspecification and that liquidity constraints have significant effects on an individual's intertemporal consumption behaviour. [ABSTRACT FROM AUTHOR]

The article presents an empirical investigation into the permanent income hypothesis taking evidence from the Canadian data. The objective of the paper is to investigate whether aggregate consumption in postwar Canada shares similar features to the findings for the U.S. Recently, researchers J.Y. Campbell and G.N. Mankiw (CM) have demonstrated that most empirical findings in the aggregate consumption literature can be explained by a simple model in which some fraction of income accrues to individuals who consume their current income each period and the remainder accrues to individuals whose consumption behaviour is predicted by the consumption-based asset pricing model. For the U.S. data, researchers report that the fraction of income going to current income consumers is in the range of 35 to 50%. The paper departs on several points from CM study in which the first-order condition is linearized by essentially assuming that the joint distribution of asset returns and consumption is conditionally lognormal and homoscedastic.