基于随机久期缺口控制的全资产负债优化模型

资产负债管理是银行等金融机构在负债结构和总量一定的前提下,通过对资产进行优化配置,达到资产流动性、盈利性和安全性“三性”之间的平衡。本文基于CIR动态利率期限结构求解随机久期,对包括增量和存量在内的全部资产负债组合的久期缺口进行预留和约束,构建资产负债优化模型控制利率风险。本文的创新与特色有三:一是以控制CIR利率期限结构的随机久期缺口为约束条件建立非线性规划模型、对资产配置进行利率风险免疫,反映了利率随时间的动态变化,突破了Macaulay久期、FW久期等现有研究的利率随时间的变化是固定不变或平行移动的限定条件,使资产配置的利率风险免疫更加符合现实情况。二是建立了包括增量资产负债与存量资产负债的全资产负债优化配置模型,改变了现有资产负债模型大多只考虑增量资产负债、而忽略存量资产负债的弊端。三是以市场利率朝着最不利方向变动时、预留缺口损失后的资本充足率仍满足监管要求为约束条件,保证了在利率不利变动情况下损失仍在可控范围内,在利率有利变动时银行净值增加。     

考虑随机利率和通货膨胀的连续时间资产组合选择

通过随机控制技术、Bellman最优性原理和HJB方程研究了通货膨胀、随机利率和交易成本等因素影响下的连续时间投资组合选择的最优化问题，将利率假定为服从Vasicek利率模型的随机过程，应用连续时间的动态均值-方差方法得到符合实际意义的HJB方程，通过多重网格的数值逼近方法求解相应的HJB方程得到双目标优化问题的最优投资策略。用实证方法与国内证券市场上代表性指数基金进行对比研究，发现通货膨胀和利率变动以及经济环境和投资者的异质信念等因素均会对最优策略产生影响，有效前沿会随之发生变化，债券与股票之间的投资比例并不是简单维持固定比例就可以保证总资产最优，拓展了基金分离定理。考虑通货膨胀和交易成本等因素的资产组合选择模型可以实实在在为机构投资者提供客观的实践指导和科学的理论依据。     

无风险利率变化时的实物期权定价方法研究

在现有的实物期权定价模型中,作为折现率的无风险利率被视为常数不发生变化,但实际上由于实物期权的到期时间较长而且不一定固定不变,作为折现率的无风险利率也并不总是固定的,而有可能发生变化.因此,研究无风险利率变化时的实物期权定价方法具有重要的理论意义和现实意义.本文正是考虑到这一点,首先假设无风险利率的变化服从Ornstein-Uhlenbeck随机过程,得到无风险利率变化时的实物期权定价公式,然后放松假设条件用Cox的利率均衡模型重新描述无风险利率的运动形式后,又对上述定价公式进行了修正,在此基础上本文给出一个数字实例对传统实物期权定价方法和考虑无风险利率变化时的实物期权定价方法进行比较.     

Stochasticity in Physiologically Based Kinetics Models: Implications for Cancer Risk Assessment

In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs. We adapted the dynamics equations of a physiologically based pharmacokinetic (PBPK) model and used a stochastic simulator for the system of equations that we derived. We then coupled kinetics simulations with a deterministic hockey stick model of carcinogenicity. Stochasticity induced substantial modifications relative to dose-response curve, compared with the deterministic situation. In particular, there was nonlinearity in the response and the stochastic apparent threshold was lower than the deterministic one. The approach that we developed could easily be extended to other biological studies to assess the influence of stochasticity at macroscopic scale for compound dynamics at the cell level.     

基于样本外分布回测的随机过程统计推断方法

采用不同的随机过程模型描述标的资产的价格动态，会极大的影响衍生品定价和风险管理活动。在文献中，同一资产采用的随机过程往往是不一致甚至是矛盾的。本文以GBM过程与OU过程为例，提出了一种统计推断方法，旨在从多个备选模型中选出能更好的描述标的资产价格动态的随机过程。该方法应用事后检验原理，将数据分成估计窗和检验窗，估计窗用来估计随机过程的参数，然后在模型参数不变的假定下，推导了原假设成立时检验窗各个时点的资产价格的样本外分布，看实际数据落在接受域或拒绝域的频数来判断是否接受原假设。本文以大宗商品、汇率、利率、股票作为标的资产，对随机过程选择进行了实证分析。实证结果表明，一些经常使用的随机过程模型并不一定是最优的模型。     

A two-stage stochastic programming approach for identifying optimal postponement strategies in supply chains with uncertain demand

In the course of globalization, applying mass-customization strategies has led to a high diversity of variants in many economic sectors. Thus, customer demands are often less predictable, and handling increasing inventory stocks as well as avoiding shortfalls have become particularly important. All these complexity drivers result in higher supply chain risks. Postponement strategies have been proposed as a suitable approach to address these problems. Although the concept of postponement and its impact on the supply chain are theoretically well discussed, optimally configuring the entire production and distribution activities is still challenging. We present a two-stage stochastic mixed-integer linear programming model, which comprises an integrated production and distribution planning approach, and considers postponement concepts. In comparison to earlier approaches that examine postponement strategies, our model supports the decision maker under demand uncertainty and considers lead times, penalty costs for shortfalls, as well as inventory-keeping decisions over a tactical planning horizon. This allows an integrated investigation of both form and logistics postponement concepts. Moreover, we consider the decision maker’s risk attitude identifying non-dominated profitable and risk-averse strategies. We illustrate the benefits of the model by using a case study from the apparel industry, and present the results of a sensitivity analysis with respect to varying demand uncertainty and demand correlations as well as different preferences regarding risk aversion. Furthermore, we carry out performance and quality benchmarks and compare the results of a standard mixed-integer linear programming solver, a parallel nested Benders approach and a sample average approximation technique.     

基于跳跃-扩散利率模型的浮动利率抵押贷款支持证券定价研究

已有文献在研究抵押贷款支持证券的定价时,所假设的利率随机过程大多是连续的,没有考虑到利率受人为或突发事件的干扰而产生跳跃不连续的情形.本文利用跳跃-扩散模型模拟利率随机过程,结合我国借款人行为特点建立提前偿还比例危险模型,运用Monte Carlo模拟方法,研究了浮动利率抵押贷款支持证券的定价,讨论了利率模型各参数的变化对定价的影响.经模拟发现:利率跳跃的频率、跳跃幅度的波动越大,证券价格越大,而利率跳跃幅度的均值越大,证券价格却越小.     

随机利率条件下可转换债券定价模型的经验检验

本文运用市场的实际数据对随机利率条件下可转换债券的定价模型作了经验检验,发现在可卖空的市场条件下,对处于实值状态的可转换债券可直接获得满意的定价,对处于虚值状态的可转换债券需要考虑债券的恶意违约风险,在加入风险补偿后也可获得满意的定价。在中国不可卖空的市场条件下,这一定价模型仅对进入转换期的可转换债券的价格具有一定的预测作用。     

Ho-Lee利率模型下带有零息票债券的投资-消费模型

在随机利率环境下研究一类带有零息票债券的投资-消费问题,其中假设无风险利率是服从Ho-Lee利率模型的随机过程,且金融市场由一种无风险资产、一种风险资产和一种零息票债券所构成。投资人希望选择一种最优投资-消费策略来最大化其有限时间段内终端财富和累积消费的期望效用。文章应用动态规划原理和变量替换方法得到了幂效用和对数效用下最优投资-消费策略的显示解。算例分析演示了最优投资-消费策略随市场参数的变化而变化的动态行为,并给出了一些经济学涵义。     

基于时变跳跃次数的基准利率风险测算研究

各国央行包括美联储的利率调整和变动就是基准利率风险.基准利率的变化势必要导致金融资产定价的变动和风险溢价.本文通过自回归模型AR测算随时间变化的利率跳跃次数,确定基准利率跳跃的概率,利用伽马分布和正态分布分别测算基准利率跳跃的时间与幅度,根据利率跳跃的概率、时间和幅度确定基准利率跳跃风险溢价,建立基于时变跳跃次数的基准利率跳跃风险溢价测算模型,并利用中国上海证券交易所国债7天回购利率数据进行实证研究.本文创新与特色:1)通过自回归模型AR测算时变的利率跳跃次数,测算利率发生跳跃的概率,确定基准利率跳跃风险溢价,揭示跳跃次数的动态变化规律,反映历史利率跳跃行为对未来利率跳跃行为的影响,改变现有研究以常数跳跃次数测算利率跳跃概率、无法真实反映利率跳跃的频繁程度,导致利率跳跃概率及利率跳跃风险溢价测算不准的弊端.2)研究表明,现有研究的常数跳跃次数仅仅是本文跳跃次数测算模型在参数ρ、γ等于0时的特例.3)通过利率跳跃的概率、时间和幅度确定基准利率的跳跃风险溢价,解决基准利率跳跃风险补偿的测算问题.     

Consistent Tests for Stochastic Dominance

Methods are proposed for testing stochastic dominance of any pre–specified order, with primary interest in the distributions of income. We consider consistent tests, that are similar to Kolmogorov–Smirnov tests, of the complete set of restrictions that relate to the various forms of stochastic dominance. For such tests, in the case of tests for stochastic dominance beyond first order, we propose and justify a variety of approaches to inference based on simulation and the bootstrap. We compare these approaches to one another and to alternative approaches based on multiple comparisons in the context of a Monte Carlo experiment and an empirical example.     

Diversification-consistent data envelopment analysis based on directional-distance measures

We propose new diversification-consistent DEA models suitable for assessing efficiency of investment opportunities available on financial markets. The formulations based on directional distance measures enable to use several risk measures as inputs and return measures as outputs, which can take both positive and negative values. We show that various models with different strength can be obtained and strongly, semi-strongly or weakly Pareto–Koopmans efficient investment opportunities can be identified. Moreover, the optimal solutions correspond to efficient investment opportunities and can be used by investors to revise the inefficient ones. If we consider discretely distributed returns, we can prove that under proper choice of the inputs (CVaRs) and outputs (expected return), the strongest model is able to identify efficient investment opportunities with respect to the second-order stochastic dominance. Moreover, the model can be formulated as a linear programming problem. In the numerical study, the proposed DEA models are applied to 48 representative industry portfolios from US stock markets.     

一类网络扩张的机会约束模型与算法

在不确定环境中的机会约束下,怎样去增加一组边的容量到一个指定的瓶颈容量,而使网络瓶颈扩张的费用最小.带有随机单位扩张费用的网络瓶颈容量扩张问题,可以根据一些概率机会约束规则,列出它的机会约束规划模型的通用表达式.将网络瓶颈容量算法、随机模拟方法和遗传算法合成在一起,设计出该问题的混合智能通用算法.最后,给出数值案例.     

银行股东权益的利率弹性对银行利率风险的测度

我们在银行股东权益价值模型的基础上,推导了测度银行利率风险暴露水平的一个指标———银行股东权益的利率弹性的表达式,并总结了它与相关经济变量之间的相互关系。这一研究对于银行管理与控制其利率风险的意义在于,我们能够量化银行在任意时刻的利率风险暴露水平,并隐含了对远期利率的预测,更重要的是,我们从中能够发现降低银行股东权益的利率弹性,从而降低银行利率风险暴露水平的途径。     

低碳港口存货质押贷款利率定价理论和模型

在港口低碳转型大趋势下,港口存货类质押贷款业务在实践中得到高速发展,如何在港口物流存货质押贷款理论决策中融入低碳港口约束因素、间接推动港口转型升级显得十分迫切。本研究深入探讨低碳转型下的港口质押贷款利率决策理论,提出了无风险套利原理、期权调整利差原理、低碳控制原理和质押风险控制原理。在此基础上,借助看跌期权反映客户违约风险调整价差、构建质押货物的碳排风险调整价差、采用VaR方法界定港口存货类质押率,建立了低碳港口流动性较强存货类物流质押贷款利率决策模型。重要参数敏感性分析表明:质押贷款利率与质押率呈现初始平稳、后上升、最后显著下滑,与回收率、质押物初始价格和质押物价格增长率三参数呈现初始显著下滑,最后平稳,与碳排治理成本率、融资成本率、经营成本率、无风险利率四参数呈现同方向类线性增长变化,与质押物价格波动率、质押物价值变动率两参数呈现反方向变化。最后,采用某港口实际数据,验证了模型可行性、可用性。     

MODELING MODEL UNCERTAINTY

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different “robust” policy recommendations. Therefore, we develop new methods to analyze uncertainty about the parameters of a model, the lag specification, the serial correlation of shocks, and the effects of real‐time data in one coherent structure. We consider both parametric and nonparametric specifications of this structure and use them to estimate the uncertainty in a small model of the U.S. economy. We then use our estimates to compute robust Bayesian and minimax monetary policy rules, which are designed to perform well in the face of uncertainty. Our results suggest that the aggressiveness recently found in robust policy rules is likely to be caused by overemphasizing uncertainty about economic dynamics at low frequencies. (JEL: E52, C32, D81)     

短期利率模型在上交所债券市场上的实证分析

以中国上海证券交易所从债券价格导出的0.5年期利率的周度数据为分析对象,使用SNP法,估计出短期利率的条件密度函数,发现其条件分布具有明显的异方差性和非正态性.然后利用EMM法实证分析了常用的连续时间的单因子和两因子短期利率模型.单因子短期利率模型包括Vasicek模型,CIR模型,CKLS模型等,两因子利率模型包括Gallant,Tanchen给出的随机波动率模型和Balduzzi等人的随机均值回复模型.实证结果表明所有的单因子短期利率模型都不能很好地描述中国上海证券交易所债券市场上的短期利率变化,CKLS模型是它们中表现最好的单因子利率模型.随机均值回复模型也不能描述短期利率的变化,只有随机波动率模型可以描述上海证券交易所的短期利率的变化.     

Evaluating Critical Uncertainty Thresholds in a Spatial Model of Forest Pest Invasion Risk

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model's numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for entry, establishment, and spread, to estimate the risks of invasion and their variation across a two-dimensional landscape for Sirex noctilio , a nonnative woodwasp recently detected in the United States and Canada. Here, we present a sensitivity analysis of the mapped risk estimates to variation in key model parameters. The tested parameter values were sampled from symmetric uniform distributions defined by a series of nested bounds (±5%, … , ±40%) around the parameters' initial values. The results suggest that the maximum annual spread distance, which governs long-distance dispersal, was by far the most sensitive parameter. At ±15% or larger variability bound increments for this parameter, there were noteworthy shifts in map risk values, but no other parameter had a major effect, even at wider bounds of variation. The methodology presented here is generic and can be used to assess the impact of uncertainties on the stability of pest risk maps as well as to identify geographic areas for which management decisions can be made confidently, regardless of uncertainty.     

Supply Performance in Multi‐Echelon Inventory Systems with Intermediate Product Demand: A Perspective on Allocation

In this article, we study the performance of multi‐echelon inventory systems with intermediate, external product demand in one or more upper echelons. This type of problem is of general interest in inventory theory and of particular importance in supply chain systems with both end‐product demand and spare parts (subassemblies) demand. The multi‐echelon inventory system considered here is a combination of assembly and serial stages with direct demand from more than one node. The aspect of multiple sources of demands leads to interesting inventory allocation problems. The demand and capacity at each node are considered stochastic in nature. A fixed supply and manufacturing lead time is used between the stages. We develop mathematical models for these multi‐echelon systems, which describe the inventory dynamics and allow simulation of the system. A simulation‐based inventory optimization approach is developed to search for the best base‐stock levels for these systems. The gradient estimation technique of perturbation analysis is used to derive sample‐path estimators. We consider four allocation schemes: lexicographic with priority to intermediate demand, lexiographic with priority to downstream demand, predetermined proportional allocation, and proportional allocation. Based on the numerical results we find that no single allocation policy is appropriate under all conditions. Depending on the combinations of variability and utilization we identify conditions under which use of certain allocation polices across the supply chain result in lower costs. Further, we determine how selection of an inappropriate allocation policy in the presence of scarce on‐hand inventory could result in downstream nodes facing acute shortages. Consequently we provide insight on why good allocation policies work well under differing sets of operating conditions.     

Heston模型下保险公司和再保险公司的投资与再保险博弈

在同时考虑保险公司和再保险公司利益的前提下,研究了保险公司和再保险公司之间的投资与再保险博弈问题。假设保险公司面临的赔付过程由带漂移的布朗运动描述。保险公司可以向再保险公司购买比例再保险,两公司均可以投资于一种无风险资产和一种价格过程服从Heston模型的风险资产,并以加权终端财富的期望效用最大化为目标,利用动态规划原理建立相应的HJB方程并求解,分别得到了保险公司与再保险公司的均衡投资与再保险策略的解析表达,并利用均衡保险市场上再保险合同的供需关系分析了保险产品的定价问题。最后通过数值实例分析了各模型参数对均衡策略的影响。