Optimal investment,consumption and proportional reinsurance under model uncertainty

This paper considers the optimal investment, consumption and proportional reinsurance strategies for an insurer under model uncertainty. The surplus process of the insurer before investment and consumption is assumed to be a general jump–diffusion process. The financial market consists of one risk-free asset and one risky asset whose price process is also a general jump–diffusion process. We transform the problem equivalently into a two-person zero-sum forward–backward stochastic differential game driven by two-dimensional Lévy noises. The maximum principles for a general form of this game are established to solve our problem. Some special interesting cases are studied by using Malliavin calculus so as to give explicit expressions of the optimal strategies.     

Arithmetic returns for investment performance measurement

This paper introduces new money-weighted metrics for investment performance analysis, based on arithmetic means of holding period rates weighted by the investment’s market values. This approach generates rates of return which measure a fund’s or portfolio’s performance and a fund manager’s performance. It also enables to show that the Internal Rate of Return (IRR) is a weighted mean of holding period rates associated with interim values which differ from market values, so that value additivity is violated. The manager’s Arithmetic Internal Rate of Return (AIRR) is shown to be the true period equivalent of the cumulative Time Weighted Rate of Return (TWRR), whereas the period TWRR (a geometric return) provides a different ranking. The method is easily generalized for coping with varying benchmark rates. We also cope with the practical problem of estimating interim values whenever they are not available.     

Can long-run dynamic optimal strategies outperform fixed-mix portfolios? Evidence from multiple data sets

Using five alternative data sets and a range of specifications concerning the underlying linear predictability models, we study whether long-run dynamic optimizing portfolio strategies may actually outperform simpler benchmarks in out-of-sample tests. The dynamic portfolio problems are solved using a combination of dynamic programming and Monte Carlo methods. The benchmarks are represented by two typical fixed mix strategies: the celebrated equally-weighted portfolio and a myopic, Markowitz-style strategy that fails to account for any predictability in asset returns. Within a framework in which the investor maximizes expected HARA (constant relative risk aversion) utility in a frictionless market, our key finding is that there are enormous difference in optimal long-horizon (in-sample) weights between the mean–variance benchmark and the optimal dynamic weights. In out-of-sample comparisons, there is however no clear-cut, systematic, evidence that long-horizon dynamic strategies outperform naively diversified portfolios.     

Developing real option game models

By mixing concepts from both game theoretic analysis and real options theory, an investment decision in a competitive market can be seen as a “game” between firms, as firms implicitly take into account other firms’ reactions to their own investment actions. We review two decades of real option game models, suggesting which critical problems have been “solved” by considering game theory, and which significant problems have not been yet adequately addressed. We provide some insights on the plausible empirical applications, or shortfalls in applications to date, and suggest some promising avenues for future research.     

Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method

Socially Responsible Investing (SRI) is broadly defined as an investment process that integrates not only financial but also social, environmental, and ethical (SEE) considerations into investment decision making. SRI has grown rapidly around the world in the last decades. In the last years, given the causes of the 2008 financial crisis, ethical, social, environmental and governance concerns have become even more relevant investment decision criteria. However, while a diverse set of models have been developed to support investment decision-making based on financial criteria, models including also social responsibility criteria are rather scarce.     

风险项目的投资期权分析

本文讨论了风险投资家向企业主融资时的投资期权,求出了投资期权的表达式,并对其中的一些参数进行了分析.     

A genetic algorithm for resource investment project scheduling problem,tardiness permitted with penalty

In this paper a genetic algorithm for solving a class of project scheduling problems, called Resource Investment Problem, is presented. Tardiness of project is permitted with defined penalty. Elements of algorithm such as chromosome structure, unfitness function, crossover, mutation, immigration and local search operations are explained.     

投资组合保险CPPI策略研究

随着期权理论应用的发展,投资组合保险在国外已成为一种盛行的资产配置策略, 常数比例投资组合保险策略(CPPI)以其模型简单、参数的设置又能充分反映投资人不同的风险偏好、而且易于实施,成为大型安全型基金的基金经理首选的投资策略.本文研究并推广了CPPI策略,找出CPPI与期权的关系,讨论了借贷限制对(CPPI策略的影响,最后对CPPI策略在中国市场的可投资性进行了评测.     

具有交易成本的证券组合投资决策研究

本文利用均值－方差模型，分析了有交易成本的证券投资组合的决策问题，给出了风险资产和无风险资产的最优投资比例与交易成本关系的一个有意义的结论。     

Capital budgeting with benders decomposition

Capital budgeting problems with different interest rates for borrowing and lending and with possible limits on borrowing are applied to dual and primal decomposition. While the former fails if a dual gap exists, the latter becomes attractive. The paper elaborates dual and primal decomposition to capital budgeting models and discusses variants of the Benders scheme. A computer implementation is described and results of extensive computer runs with different strategies are reported which give proof of the efficiency of the implemented decomposition procedure.