Robust portfolio choice for a defined contribution pension plan with stochastic income and interest rate

This paper considers a robust portfolio choice problem for a defined contribution pension plan with stochastic income and stochastic interest rate. The investment objective of the pension plan is to maximize the expected utility of the wealth at the retirement time. We assume that the financial market consists of a stock, a zero-coupon bond and a risk-free asset. And the member of defined contribution pension plan is ambiguity-averse, which means that the member is uncertain about the expected return rate of the bond and stock. Meanwhile, the member's ambiguity-aversion level toward these two financial assets is quite different. The closed-form expressions of the robust optimal investment strategy and the corresponding value function are derived by adopting the stochastic dynamic programming approach. Furthermore, the sensitive analysis of model parameters on the optimal investment strategy are presented. We find that the member's aversion on model ambiguity increases her hedging demand and has remarkable impact on the optimal investment strategy. Moreover, we demonstrate that ignoring model uncertainty will lead to significant utility loss for the ambiguity-averse member, and the model uncertainty about the stock dynamics implies greater effect on the outcome of the investment than the bond.     

Optimal investment and life insurance strategies in a mixed jump-diffusion framework

Abstract

This article investigates an optimal investment and life insurance strategies in a mixed jump-diffusion framework. The individual life insurance policyholder who has CRRA preferences. The market consists of riskless asset, a zero-coupon bond, a stock and life insurance. The instantaneous interest rate is modeled as the O-U model, while a zero-coupon bond with credit risk follows a BSDE and a risky asset be driven by MJD-fBm model. The problem is solved by the mixed jump diffusion fractional HJB SDE which satisfied the admissible strategy, then the closed form solution and optimal strategies are derived and the simulation of the various parameters are also given.     

基于状态空间模型的货币流动性与股价关系的实证研究

货币流动性与股价波动之间相互影响的关系一直是学术界争论的热点。利用变参数状态空间模型,研究货币流动性与股价之间的动态关系。研究结果表明：M2增长率／GDP增长率对股价的影响不大;M2与股价的波动从长期和短期来看均存在单向因果关系,而利率与股价波动只是短期存在单向因果关系,但相对于货币供应量而言,利率对股价的波动更为敏感。     

Lattice-oriented percolation system applied to volatility behavior of stock market

In this paper, a discrete time series of stock price process is modeled by the two-dimensional lattice-oriented bond percolation system. Percolation theory, as one of statistical physics systems, has brought new understanding and techniques to a broad range of topics in nature and society. According to this financial model, we studied the statistical behaviors of the stock price from the model and the real stock prices by comparison. We also investigated the probability distributions, the long memory and the long-range correlations of price returns for the actual data and the simulative data. The empirical research exhibits that for proper parameters, the simulative data of the financial model can fit the real markets to a certain extent.     

宏观经济因素对房地产价格的长短期影响

本文将购房者、房产商与中央银行纳入统一的模型当中,对房价的影响因素进行理论建模,然后拓展了传统的向量自回归模型长短期分解技术,研究了利率、通货膨胀率、汇率、土地价格以及经济增长率对房价的长期和短期影响。研究表明,利率上升对房价具有长期的负向效应,短期内会加剧房价的波动;通胀预期在长期内促使资金流入房地产行业,对房价会产生推动作用,短期内,通胀促使房地产企业加快投资速度,房屋供给增加预期引起房价下降的冲击;人民币升值在长短期内均与房价上涨存在正向作用;在当前的经济和房地产发展阶段,经济增速提高时,对房地产业的投资和投机具有一定的抑制作用;土地价格长期来看推动房价上涨,短期内对房价影响的传导较慢。     

Pricing American put option on zero-coupon bond under fractional CIR model with transaction cost

The motivation of this study is to evaluate the American put option on zero-coupon bond, when the interest rate model is governed by a fractional CIR (FCIR) interest rate model. Since the existence of fractional Brownian motion, leading to create the arbitrage, we employ the transaction cost for eliminating the arbitrage. We first of all apply the Leland's hedging strategy for a self-financing portfolio that contains an American option and zero-coupon bond and derive a formula for the transaction cost. We perform the least-square Monte Carlo (LSM) method for pricing American option under the proposed interest rate model.     

利率期限结构预测、国债定价及国债组合管理

国债利率期限结构是固定收益产品定价和投资组合管理的核心问题。本文利用NARX（Nonlinear AutoRegressive network with eXogenous inputs）神经网络模型研究利率曲线的运动机制,拟合并预测利率期限结构,在此基础上利用Hermite插值方法构造平滑的利率曲线并计算得到国债理论价格及其预测值。实证分析发现我国国债定价效率不足,交易价格显著偏离理论价格,但国债的理论价格的实际值和预测值均对交易价格具有显著的预测能力。基于上述发现本文提出了主动国债组合管理策略,通过预测的期限结构得到国债理论价格的预测值构建的多空对冲组合和单边多头组合均能获得显著的收益。本文的研究丰富了利率期限结构的研究方法,提出的主动国债组合管理策略对通过交易提高国债定价有效性具有参考价值。     

收益率曲面预测及其在信用债投资组合管理中的应用

收益率曲线是信用债投资者的重要参考。在市场套利活动的作用下,跨期限和信用等级的债券收益率具有很强的内生联系,其变动规律具有整体性和连续性。以往研究将信用债收益率曲线拆分成无风险利率曲线和信用利差两个部分,前者关注期限的影响,后者关注信用等级的影响,但很少同时考虑两者的共同影响。本文在收益率曲线的基础上增加信用等级维度,将AAA+级到AA级收益率曲线视为一个相互关联的整体,定义为信用债的收益率曲面(Yield Surface)。相比收益率曲线,收益率曲面包含了跨等级的系统性预测信息,通过预测收益率曲面能够构建具有较高收益的投资管理策略。本文以中期票据市场为例,采用卷积神经网络模型预测1周后的收益率曲面,在此基础上计算债券的预测理论价格,发现该价格对未来交易价格的预测有显著作用。基于预测价差,本文提出了信用债投资管 理策略,应用该策略的投资组合能够获得显著的正收益。业绩归因分析发现,该策略取得的收益同时来 自投资组合在信用风险和久期风险上的暴露,预测价差可以刻画债券市场的“风险前沿”。本文采用了中债估值价格进行稳健性检验,主要结论均保持一致,具有较强的稳健性。     

货币政策冲击对股票市场价格泡沫影响的时变分析

一直以来,资产价格波动与货币政策的关系都是经济金融领域关注的重点问题。本文以1997-2014年我国月度数据为样本,运用新近发展的时变参数的因子扩展向量自回归模型（TVP-FAVAR）分析了我国货币政策冲击对股票市场价格泡沫影响的时变特征。研究结果表明,我国货币政策的变动对股票市场价格的影响有着显著的时变性特点。当股票市场存在较大程度的资产价格泡沫时,股票市场的价格可能会随着利率提高而上升,采用加息的紧缩货币政策未必能够起到相应的调控资产价格波动的作用。这一结果和理性资产价格泡沫理论一致,意味着有必要重新审视传统观念中货币政策与资产价格波动之间是逆周期关系的认知。因此,政策制定者在采用“逆风而动”的货币政策时需要更加审慎,应密切关注货币政策对资产价格影响的时变特征,针对金融市场波动状况相机抉择。     

Minimizing Upper Bound of Ruin Probability Under Discrete Risk Model with Markov Chain Interest Rate

This article focuses on minimal upper bound of ruin probability for a discrete time risk model with Markov chain interest rate and stochastic investment return. The interest rate of bond market is assumed to be a stationary Markov chain, and the return process of a stock market can be negative. This article presents two kinds of methods for minimizing the upper bound of ruin probability. One method relies on recursive equations for finite time ruin probabilities and inductive approach, the other one depends on martingale approach. Numerical examples show that the martingale approach is better than the inductive one.     

基于可信性理论的Mean-CVaR投资组合优化

选取CVaR作为风险度量指标,在可信性理论的基础上构建Mean-CVaR投资组合模型,采用Markov过程预测作为模糊变量的预期投资收益率,并设计基于模糊模拟和遗传算法的混合智能算法以求解;选取上证50成份股2013—2014年的日度历史交易数据,将该模型应用到中国证券市场,结果发现该投资组合模型与中国证券市场的环境具有一定的适应性,能够为投资者的投资决策提供依据。     

新闻对上海股票市场股价影响的实证分析

文章在界定新闻含义的基础上,借鉴国外学者在外汇领域建立新闻模型的研究思路和方法,建立股票市场狭义线性新闻模型,试图从新闻角度来解释股价变动的原因。以沪市为例进行实证研究表明,在所取样本区间,股票市场狭义线性新闻模型成立,新闻对沪市股价变动有显著影响:"人民币/美元汇率指数"新闻项、"消费价格指数"新闻项和"燃料动力价格指数"新闻项对股价变动存在显著性影响。     

中国股指期货与股票市场波动性关系的实证分析

使用修正的EGARCH模型与VaR方法检验股指期货的推出对中国股票市场波动性所产生的影响。采用的数据为沪深300指数,样本数据分为股指期货推出前,股指期货推出后的短期、中期和长期与样本数据全体五个时间段。研究表明,从股指期货推出的短期与中期来看,市场对信息的反应比较混乱。从长期来看,股指期货的推出加速了信息的传递速度并且弱化了非对称效应,并没有加大股市的波动性。VaR方法检验表明,股指期货的推出有效降低了股市风险,使A股市场更加成熟和完善。     

Efficient Monte Carlo option pricing under CEV model

One of the financial model with nonconstant volatiltiy is the constant elasticity of varinace model, or CEV model for short. The CEV model is an altrnative to the Black–Scholes model of stock price movements. In this diffusion process, unlike the Black–Scholes model, the volatility is a function of the stock price and involves two parameters. In this article, we propose an efficient Monte-Carlo algorithm for pricing arithmetic Asian option under CEV model. In an earlier work by Mehrdoust, an efficient Monte Carlo simulation algorithm for pricing arithmetic Asian options under Black–Scholes model is proposed. The proposed algorithm has proved extremely successful in decreasing the standard deviation and the error of simulation in pricing of the arithmetic Asian options. In this article, we find that the proposed algorithm under the geometric Brownian motion assumption in the Black–Scholes model can effectively apply for pricing arithmetic Asian options when the stock price process follows the CEV model. Numerical experiments show that our algorithm gives very accurate results.     

Optimal investment,consumption, and life insurance in an incomplete market

ABSTRACT

In this article, we solve an optimal insurance-consumption-investment problem for a wage earner in an incomplete market, where the stock price has a mean-reverting drift. By using the martingale method, we analyze this problem and derive the optimal strategies. Explicit solutions are found for both power and logarithmic utilities.     

遗传算法在投资组合模型中的应用

均值-VaR模型是比较复杂的非线性规划问题，传统的算法不能保证得到全局最优值。鉴于此，引入遗传算法求解资产配置比例。对基于均值-VaR的单目标优化问题，设计了限定搜索空间和惩罚函数的遗传算法；而对多目标优化问题，应用并行选择遗传算法，并以沪深300行业分类指数构建投资组合，分析了行业资产配置的投资组合问题。结果表明，算法取得了良好的效果，解的结果既满足了投资的目标和约束条件，又反映了投资者之间不同的收益风险需求，且具有较好的实践性。     

基于可变参数的FCI构建与实证研究

通过构建包含利率、汇率、房价、股价、货币供应量和第二产业增加值比重共六个指标的FCI,以具有可变参数特征的状态空间模型为基础,拟合具有可变权重的FCI。实证结果显示:FCI在样本期内的拟合效果基本符合经济现实,用于指示金融松紧程度和经济运行方向的功能明显,2005年第3季度、2008年第3季度和2011年第3季度等时段的金融形势最为紧张,2004年第2季度和2011年第1季度的金融形势最为宽松。     

A comparison of risk transfer strategies for a portfolio of life annuities based on RORAC

This paper aims to compare different reinsurance arrangements in order to reduce the longevity and financial risk originated by a life insurer while managing a portfolio of annuities policies. Linear and nonlinear reinsurance strategies as well as swap like agreements are evaluated via a discrete-time actuarial risk model. Specifically, longevity dynamics are represented by Lee–Carter type models, while interest rate is modeled by Cox–Ingersoll–Ross model. The reinsurance strategies effectiveness is evaluated according to the Return on Risk Adjusted Capital under a ruin probability constrain.     

An online estimation scheme for a Hull–White model with HMM-driven parameters

This paper considers the implementation of a mean-reverting interest rate model with Markov-modulated parameters. Hidden Markov model filtering techniques in Elliott (1994, Automatica, 30:1399–1408) and Elliott et al. (1995, Hidden Markov Models: Estimation and Control. Springer, New York) are employed to obtain optimal estimates of the model parameters via recursive filters of auxiliary quantities of the observation process. Algorithms are developed and implemented on a financial dataset of 30-day Canadian Treasury bill yields. We also provide standard errors for the model parameter estimates. Our analysis shows that within the dataset and period studied, a model with two regimes is sufficient to describe the interest rate dynamics on the basis of very small prediction errors and the Akaike information criterion.     

基于高维波动率网络模型的股票市场风险特征研究

波动率是金融风险管理研究的重要内容之一。本文基于复杂网络理论和数据挖掘技术提出股票市场的高维波动率网络模型。首先运用互信息度量不同股票价格波动之间的相关关系,其次对股票市场不同周期下的波动情况建立度的中心势、平均距离、幂律分布等网络拓扑指标,再次根据这些指标利用Prim算法构建出高维波动率网络模型,最后运用Newman-Girvan算法对股票价格波动率的相关性进行分层研究。高维波动率网络模型突破了传统波动率模型关于变量维数的限制,能够在依赖少量假设的基础上,挖掘出多个金融市场主体间的相互关系,反映金融市场的风险特征及网络拓扑性质。实证结果发现：与常用的Pearson相关系数法相比,在互信息框架下,股价波动的非线性相关关系得到了更好的度量;股票市场的整体波动性与个股波动率相关性变化趋势相反,市场处在高波动时期资产组合分散化效果较好;网络中存在少量度数大的关键节点和中心节点,风险通过这些节点可以迅速传递到整个市场;股票市场的运行具有明显的行业聚集现象;网络分层研究进一步直观的展现了风险在层与层之间的传递规律和与之对应的行业特征。高维波动率网络模型为挖掘股票市场的风险特征与管理金融风险提供了一个新的工具。