Forecasting turning points in tourism growth

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.     

Forecasting dynamic return distributions based on ordered binary choice

We present a simple approach to the forecasting of conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regressions that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and a past volatility proxy as predictors. The direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications in which conditional distribution forecasts are desired.     

Ruin probabilities in classical risk models with gamma claims

In this paper, we provide three equivalent expressions for ruin probabilities in a Cramér–Lundberg model with gamma distributed claims. The results are solutions of integro-differential equations, derived by means of (inverse) Laplace transforms. All the three formulas have infinite series forms, two involving Mittag–Leffler functions and the third one involving moments of the claims distribution. This last result applies to any other claim size distributions that exhibits finite moments.     

Behavioral equilibrium and evolutionary dynamics in asset markets

This paper analyzes a dynamic stochastic equilibrium model of an asset market based on behavioral and evolutionary principles. The core of the model is a non-traditional game-theoretic framework combining elements of stochastic dynamic games and evolutionary game theory. Its key characteristic feature is that it relies only on objectively observable market data and does not use hidden individual agents’ characteristics (such as their utilities and beliefs). A central goal of the study is to identify an investment strategy that allows an investor to survive in the market selection process, i.e., to keep with probability one a strictly positive, bounded away from zero share of market wealth over an infinite time horizon, irrespective of the strategies used by the other players. The main results show that under very general assumptions, such a strategy exists, is asymptotically unique and easily computable.     

基于Moran过程的PPP项目承包商机会主义行为演化动态

在PPP项目中，由于政府部门和承包商的目标不一致以及信息不对称等原因的存在，使得在其全寿命周期过程中，承包商的机会主义行为屡见不鲜。本文基于随机演化博弈理论，建构基于Moran过程的博弈模型，旨在探讨推动PPP项目承包商不采取机会主义行为策略的有利条件。研究表明，承包商数目不断扩大，通过降低承包商之间的举报成本，增大惩处力度，可使降低机会主义行为发生概率的效果显著。进一步，借助数值算例，对研究结果进行验证并给出相应的管理对策。     

Survival of full versus partial acquisitions: The moderating role of firm’s internationalization experience,cultural distance,and host country context characteristics

The high divestment rates of acquired foreign units indicate challenges connected to planning and management of foreign acquisitions. In this paper we analyze the moderating effect of internal and external variables on the relationship between acquirers’ ownership strategy and survival of acquired foreign units. We test our hypotheses on a sample of 1275 acquisitions conducted by Finnish firms in various countries during the period 1980–2005. The results indicate that the probability of survival does not differ significantly between full and partial acquisitions. We further find that the likelihood of survival in full, relative to partial acquisitions, is positively associated with the acquisition-specific experience, but inversely related to general international and target country experience. The results also reveal that the positive impact of full acquisitions is stronger if the acquisitions are made in culturally similar countries, in less developed economies, and in markets where the country risk has increased after entry.     

Self-employment of Chinese rural labor force: Subsistence or opportunity?—An empirical study based on nationally representative micro-survey data

Self-employment is an essential form of non-agricultural employment, and its nature has been rarely identified in recent studies. From the perspectives of the human capital, social capital, and family assets of rural laborers, this study focuses on determining self-employment by using the nationally representative data on the rural labor force in China. Through the static comparative analysis between three groups of laborers in self-employment, wage employment, farming, and the dynamic comparative analysis of laborers entering into and exiting from self-employment, the study shows that self-employment of rural laborers in China is almost opportunity-driven or moving toward opportunistic self-employment. Human capital, social capital, and family assets promote rural laborers shifting from wage employment to self-employment and stimulate the establishment of high-value enterprises. The study suggests that local governments should increase the investments in rural education and vocational skills training and strengthen the availability of rural credit to lay a good foundation for self-employment activities in rural areas.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.