Time diversification and security preferences: A stochastic dominance analysis

We use stochastic dominance to test whether investor should prefer riskier securities as the investment horizon lengthens. Return distributions for stocks, bonds, and U.S. Treasury bills are generated for holding periods of one to 25 years by simulation. For each holding period, stochastic dominance tests are run to establish preferences between the alternative security classes. Contrary to previous mean-variance based studies, we find no evidence that high-risk securities (stocks) dominate low-risk securities (bonds, Treasury bills) as the investment horizon lengthens. However, we do find that corporate bonds systematically dominate government bonds.     

Optimal portfolio choice in the bond market

We consider the Merton problem of optimal portfolio choice when the traded instruments are the set of zero-coupon bonds. Working within a Markovian Heath–Jarrow–Morton model of the interest rate term structure driven by an infinite-dimensional Wiener process, we give sufficient conditions for the existence and uniqueness of an optimal trading strategy. When there is uniqueness, we provide a characterization of the optimal portfolio as a sum of mutual funds. Furthermore, we show that a Gauss–Markov random field model proposed by Kennedy [Math. Financ. 4, 247–258(1994)] can be treated in this framework, and explicitly calculate the optimal portfolio. We show that the optimal portfolio in this case can be identified with the discontinuities of a certain function of the market parameters.     

Measuring Inflation Expectations Uncertainty Using High‐Frequency Data

Inflation expectations play a key role in determining future economic outcomes. The associated uncertainty provides a direct gauge of how well‐anchored the inflation expectations are. We construct a model‐based measure of inflation expectations uncertainty by augmenting a standard unobserved components model of inflation with information from noisy and possibly biased measures of inflation expectations obtained from financial markets. This new model‐based measure of inflation expectations uncertainty is more accurately estimated and can provide valuable information for policymakers. Using U.S. data, we find significant changes in inflation expectations uncertainty during the Great Recession.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

Automatic balancing mechanisms for notional defined contribution accounts in the presence of uncertainty

The notional defined contribution model combines pay-as-you-go financing and a defined contribution pension formula. This paper aims to demonstrate the extent to which liquidity and solvency indicators are affected by fluctuations in economic and demographic conditions and to explore the introduction of an automatic balancing mechanism (ABM) into the pension scheme. We demonstrate that the introduction of an ABM reduces the volatility of the buffer fund and that, in most cases, the automatic mechanism that re-establishes solvency produces the highest value of the risk-adjusted notional factor.     

Expected exponential utility maximization of insurers with a Linear Gaussian stochastic factor model

In this paper, we consider the problem of optimal investment by an insurer. The wealth of the insurer is described by a Cramér–Lundberg process. The insurer invests in a market consisting of a bank account and m risky assets. The mean returns and volatilities of the risky assets depend linearly on economic factors that are formulated as the solutions of linear stochastic differential equations. Moreover, the insurer preferences are exponential. With this setting, a Hamilton–Jacobi–Bellman equation that is derived via a dynamic programming approach has an explicit solution found by solving the matrix Riccati equation. Hence, the optimal strategy can be constructed explicitly. Finally, we present some numerical results related to the value function and the ruin probability using the optimal strategy.     

供应链金融利益主体的多阶段博弈研究

现代经济中,金融在资源配置方面发挥的作用明显不足,出现严重的资源浪费和供需错配现象,加剧了中小企业面临的融资难问题.本文从供应链金融的内涵切入,建立以利益相关者为主体的融资量化模型,利用多阶段博弈理论对企业不同模式下的成本收益进行检验,结果表明:供应链金融在实现上、下游各利益相关者个体的最优化的同时,也可有效促使供应链整体的利益最大化、实现高效运作和价值增值,全面实现链上企业间的资源共享、互利共赢.     

保险公司市场风险经济资本度量研究

经济资本(EC)是在既定期间和置信水平下,公司根据实际承担的风险计算的用以吸收非预期损失的资本额度,目前市场风险是整体经济资本测算体系中最为突出的风险.根据当前保险运营与资产投资的比例特征,同时对资产端与负债端建立市场风险投资模型,采用嵌套随机模拟方法进行两阶段情景生成,度量未来一年内不同风险测度下的市场风险经济资本需求,并对比不同情景数量下的测算稳定性.结果证明:随着内部或外部情景模拟次数的增加,市场风险经济资本测算结果对于极端风险的预测趋于稳定,在内外部情景数量乘积相同时运算时间基本一致.当内外部两阶段情景生成参数差异较大的情形下,应适当增加情景生成数量,以确保对于极端风险预测的准确性.     

Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

非封闭的SIS确定模型与随机模型的研究

文章通过对具常数输入人口,死亡率和出生率的的SIS模型的确定形式与随机形式的研究,得到文中定义的基本再生数R0燮1时,传染病最终会消失,而当基本再生数大于等1时,传染病人数将稳定,传染病最终会形成地方病,最后验证了两种模型的结果是一致的。