CPPI的优化策略及实证分析

CPPI策略作为一种重要的投资组合保险策略,在保本基金,保险等领域得到广泛应用,许多关于CPPI策略的研究都是假设市场在连续时间条件下.通过研究基于离散时间条件下的CPPI策略,并引入股指期货作为风险资产,对传统CPPI策略进行修正;同时讨论修正CPPI策略模型和传统CPPI策略模型在不同市场状况下的差异.采用Monte Carlo模拟方法对不同CPPI策略进行仿真,结果表明:在离散时间条件下,当放大乘数m较小时,不同CPPI策略都能实现保本,但不同CPPI策略期末价值差别明显.     

PI(Portfolio Insurance)投资者特性研究

根据期权定价理论,分析了投资组合保险策略与期权的关系及投资组合保险策略与凸收益函数的关系,通过建立投资组合保险模型,得出不同条件下购买投资组合保险投资者的特点如下:1)随着财富的增加他们的风险承受能力比市场一般投资者增加的快;2)他们的市场预期比一般市场投资者更乐观,并且受益于投资组合保险.     

离散时间投资组合保险策略CPPI及其实证分析

本文重点讨论了在离散时刻对投资组合进行调整的CPPI策略.给出了组合价值的过程表达式,并对其进行风险分析;引入二次期望效用函数,给出了确定CPPI策略中最优乘数的方法;讨论了借贷限制对CPPI策略的影响并将其与买入持有策略进行比较分析。最后,文章对CPPI策略的投资效果进行了实证分析.     

常数比例投资组合保险(CPPI)策略 --捐赠型基金投资策略的最优选择

本文采用Merton提出的处理捐赠型基金的连续时间模型的一般框架，分析了在风险资产为几何布朗运动，效用函数为CRRA效用函数，且捐赠型基金有动态最低支出时的最优支出策略和最优投资策略，结果表明存在一条策略基准线，当基金的总资产在策略基准线之上时，基金管理人关于基金支出与投资策略的选择与不存在最低支出的要求时所作出的决策是一样的，但是一旦基金的总资产低于这条策略基准线时，基金管理人便需要考虑到基金将来必要的支出，并实际影响到他对投资策略的选择，此时基金管理人可作的最优选择是：最低的支出和一种为复制幂收益函数期权的CPPI投资策略。     

分期付款期权在基于教育基金保险的期权中的应用

文献[1]提出了一种基于教育基金保险的欧式看涨期权,它赋予合约持有人在约定时间以约定价格购买连续支付固定年限的教育年金保险的权利,本文在[1]的基础上进一步提出基于教育基金保险的分期付款期权,该期权进一步改进了基于教育基金保险的欧式看涨期权,它赋予期权持有人分期支付期权费的权利,而不是一次性支付期权费,经过首期期权费的支付,期权持有人可以在继续支付期权费以持有期权和中断期权费的支付让期权作废之间选择,这样就可以使投资者在必要的时候取消期权,从而避免无效成本支出.该期权更加方便于低收入家庭和欲将资本用于其它高回报的投资的家庭进行教育投资.本文用后向递推和二叉树方法的方法给出期权定价公式,并确定分期支付的期权费的范围.     

基于随机模糊投资乘数的离散CPPI资产配置研究

以传统CPPI投资策略的分析框架为基础,在风险资产为连续价格波动的条件下,构建离散投资决策时点的CPPI投资策略。引入模糊决策的分析方法度量投资决策者的心理预期,将传统CPPI投资策略中的投资乘数修正为随机模糊投资乘数,采用马尔科夫链蒙特卡洛模拟风险资产未来市场价格,利用模糊隶属函数描述投资决策者对未来市场运行状况预期的不确定性,保证即使投资决策者预期不精确的条件下,也能保证离散CPPI投资策略获得相对稳定的投资效果。利用中国证券市场上的真实数据进行实证检验,认为:随机模糊投资乘数最大限度地涵盖了投资决策者主观预测的不确定性;基于随机模糊投资乘数的离散CPPI投资策略在不同的市场运行状况中,较传统的CPPI投资策略更具投资的灵活性,可以在保证投资保险的前提下,追求较高的投资收益。     

基于CvaR的融入期权的投资组合模型

把期权作为一种投资对象融入到投资组合中,而不仅仅是作为风险对冲工具.用条件风险价值(CVaR)刻画组合风险,并求出最小化风险下的最优鲁棒投资组合策略.最后通过数值算例证明了模型的有效性,并得到融入期权后有效地提高了组合的收益,特别是当标的资产出现大的波动时,期权在组合中的表现更突出.     

基于VaR的权变投资组合保险策略及在中国股市中的实证研究

传统的投资组合保险策略在投资期内全程进行保险操作,在熊市期间确能起到保险作用,但在牛市期间又会丧失部分收益.应用滤嘴法则设计了基于V aR的权变型投资组合保险策略,实证结果表明,该策略很好地起到了投资与保险的功能,能有效地进行市场风险的实时监控,为保险资金或保本型基金投资股市提供了有效的手段.     

基于生存概率的保险基金最优投资策略研究

利用破产理论和随机控制理论研究保险基金最优投资策略,建立生存概率最大化的目标函数,得到最优投资策略满足的随机微分方程;在初始金逼近0时得到保险基金的最优投资策略的显示解;采用递推算法,得到初始准备金为任意值时的最优投资策略.     

基于VaR的权变投资组合保险策略及实证分析

传统的投资组合保险策略在投资期内全程进行保险操作,在熊市期间确能起到保险作用,但在牛市期间又会夹失部分收益。应用综合了VaR技术和滤嘴法则的VaR套补的权变投资组合保险策略,则能弥补上述缺憾,为保险资金或保本型基金投资股市提供了有效的投资手段。     

Optimal investment strategies for participating contracts

Participating contracts are popular insurance policies, in which the payoff to a policyholder is linked to the performance of a portfolio managed by the insurer. We consider the portfolio selection problem of an insurer that offers participating contracts and has an S-shaped utility function. Applying the martingale approach, closed-form solutions are obtained. The resulting optimal strategies are compared with portfolio insurance hedging strategies (CPPI and OBPI). We also study numerical solutions of the portfolio selection problem with constraints on the portfolio weights.     

Portfolio insurance: Gap risk under conditional multiples

The research on financial portfolio optimization has been originally developed by Markowitz (1952). It has been further extended in many directions, among them the portfolio insurance theory introduced by Leland and Rubinstein (1976) for the “Option Based Portfolio Insurance” (OBPI) and Perold (1986) for the “Constant Proportion Portfolio Insurance” method (CPPI). The recent financial crisis has dramatically emphasized the interest of such portfolio strategies. This paper examines the CPPI method when the multiple is allowed to vary over time. To control the risk of such portfolio management, a quantile approach is introduced together with expected shortfall criteria. In this framework, we provide explicit upper bounds on the multiple as function of past asset returns and volatilities. These values can be statistically estimated from financial data, using for example ARCH type models. We show how the multiple can be chosen in order to satisfy the guarantee condition, at a given level of probability and for various financial market conditions.     

Stochastic dominance of portfolio insurance strategies

The purpose of this article is to analyze and compare two standard portfolio insurance methods: Option-based Portfolio Insurance (OBPI) and Constant Proportion Portfolio Insurance (CPPI). Various stochastic dominance criteria up to third order are considered. We derive parameter conditions implying the second- and third-order stochastic dominance of the CPPI strategy. In particular, restrictions on the CPPI multiplier resulting from the spread between the implied volatility and the empirical volatility are analyzed.     

Options on constant proportion portfolio insurance with guaranteed minimum equity exposure

In the present paper we study a new exotic option offering participation in a dynamic asset allocation strategy, which is an extension of the well‐known Constant Proportion Portfolio Insurance (CPPI) strategy. Our novel approach consists in assuming that the percentage of wealth invested in stocks cannot go under a fixed level, called guaranteed minimum equity exposure (GMEE). In particular, our proposal ensures to overcome the so‐called cash‐in risk, typically related to a standard CPPI technique, simultaneously guaranteeing the equity market participation. We look deeper into the valuation of call and put options linked to this new CPPI‐GMEE strategy. A particular attention is devoted to the analysis of key parameters' value as to gain a better understanding of the sensitivities of the option prices, when changing, for example, the embedded guarantee level. To show the effectiveness of our proposal we provide a detailed computational analysis within the Heston‐Vasicek framework, numerically comparing the evaluation of the price of European plain vanilla options when the underlying is either a purely risky asset, a standard CPPI portfolio and a CPPI with GMEE.     

For what trading strategies is the tax payment stream of infinite variation?

For an Itô asset price process and under quite mild structural assumptions, we show that the accumulated payments of a linear tax on trading gains are of infinite variation if the quadratic covariation of the trading strategy and the asset price is negative. By contrast, if the strategy is a smooth function of the asset price and some finite variation processes with positive partial derivative with respect to the price variable, then accumulated tax payments are of finite variation. An interesting example are constant proportion portfolio insurance (CPPI) strategies which we extend to models with capital gains taxes. The associated tax payment stream is of finite variation if the tax-adjusted constant multiple of the cushion which is invested in the risky asset is bigger or equal to one. Otherwise, it is of infinite variation.     

非平稳市场中适应性在线投资组合策略设计与分析

考虑到股票市场的表现往往是非平稳的, 过去较长时间的股票价格对当前的投资决策影响较小, 因此基于近期股票价格数据设计在线投资组合策略. 首先, 将上一期的策略与固定长度的股票价格近期数据对应的最优定常再调整策略加权平均, 设计了一个在线投资组合策略. 其次, 进一步采用在线学习的方法选择加权平均的权重, 设计了一个适应性的在线投资组合策略. 利用实际股票价格数据对构造的策略进行数值分析, 结果表明与基准策略和已有的在线投资组合策略相比, 设计的策略具有较好的性能.     

均值方差偏好和期望损失风险约束下的动态投资组合

本文在均值方差框架下,研究了期望损失风险约束下的连续时间动态投资组合问题。运用鞅理论和凸对偶方法,分别给出了最优财富和最优投资策略的解析式,而且两基金分离定理仍然成立。最后通过数值例子分析了风险约束对最优投资策略的影响。     

Filter‐based portfolio strategies in an HMM setting with varying correlation parametrizations

We consider portfolio optimization in a regime‐switching market. The assets of the portfolio are modeled through a hidden Markov model (HMM) in discrete time, where drift and volatility of the single assets are allowed to switch between different states. We consider different parametrizations of the involved asset covariances: statewise uncorrelated assets (though linked through the common Markov chain), assets correlated in a state‐independent way, and assets where the correlation varies from state to state. As a benchmark, we also consider a model without regime switches. We utilize a filter‐based expectation‐maximization (EM) algorithm to obtain optimal parameter estimates within this multivariate HMM and present parameter estimators in all three HMM settings. We discuss the impact of these different models on the performance of several portfolio strategies. Our findings show that for simulated returns, our strategies in many settings outperform naïve investment strategies, like the equal weights strategy. Information criteria can be used to detect the best model for estimation as well as for portfolio optimization. A second study using real data confirms these findings.