Home-field advantage or a matter of ambiguity aversion? Local bias among German individual investors

This paper investigates whether familiarity induced by ambiguity aversion can help explaining the local bias phenomenon among individual investors. Using geographic closeness as a proxy for investor familiarity, we find that investors pull out of (unfamiliar) remote stocks and pour into (familiar) local stocks during times of increased market uncertainty. Moreover, the magnitude of this ‘flight to familiarity’ increases in the spread of an investor's ambiguity (about expected returns) between local and remote stocks. Our results prove robust to a number of alternative explanations of local bias. Specifically, we rule out a ‘home-field advantage’, where investors are able to translate information advantages about nearby companies into excess returns on their local stockholdings. We conclude that individual investors’ local bias is induced by ambiguity aversion in the portfolio selection process rather than a trading strategy based on superior information about local companies.     

Price-raising drug enforcement and property crime: a dynamic model

Price-raising drug enforcement suppresses drug use, but it is expensive and may increase property crime. This has led to contradictory recommendations concerning how drug enforcement should or should not be used. We reconcile these recommendations by incorporating the enforcement's effects on both drug use and on property crime within an optimal-control model that recognizes whether convicted drug-involved property offenders are merely incarcerated or whether they receive some form of drug treatment.     

Forecast of realized covariance matrix based on asymptotic distribution of the LU decomposition with an application for balancing minimum variance portfolio

We derive the asymptotic distribution for the LU decomposition, that is, the Cholesky decomposition, of realized covariance matrix. Distributional properties are combined with an existing generalized heterogeneous autoregressive (GHAR) method for forecasting realized covariance matrix, which will be referred to as a generalized HARQ (GHARQ) method. An out-of-sample forecast comparison of a real data set shows that the proposed GHARQ method outperforms other existing methods in terms of optimizing the variances of portfolios.     

基于实物期权的房地产最优开发时机

本文主要利用实物期权定价理论,结合房地产实际投资状况,通过处理房地产开发项目的单位价格和单位建设成本之间的相互影响,建立了关于价格与成本之间的房地产最优投资决策时机模型,探讨了房地产开发的最适时机的选择问题。