基于风险网络结构的资产分配策略研究

股票市场是一个高风险市场,如何在频繁发生的极端波动环境下进行有效的资产分配是当前热点问题。本文首次应用VaR模型构建股市风险网络,并基于风险网络模型进行最优投资组合成分选择,分析不同市场波动行情下最优资产分配权重和股票中心性的时变关系,融合风险网络时变中心性和个股表现提出新的动态资产分配策略(φ投资策略)。结果表明:在股市上涨和震荡期,股票中心性和最优投资组合权重呈正相关关系;股市下跌期,股票中心性和最优投资组合权重呈负相关关系;当φ>0.05时,投资者的合理投资区域向高中心性节点移动,反之。φ投资策略的绩效表现证明了风险网络结构能提高投资组合选择过程。此研究对于优化资产配置、提高投资收益、多元化分散投资风险具有重要意义。     

产能配置的市场传导机制与政策效应——来自中国钢铁行业的证据

通过构建两区制的MSVAR模型,探讨不同市场状态下钢铁行业产能配置的市场传导机制及财政支出的作用效应,研究发现:产能过剩是行业快速发展过程中市场传导机制引发的客观结果,这是由市场传导机制的非对称性以及政策的差异性作用导致的。在市场扩张时期,行业需求与财政政策均能促进对投资与产能配置水平的提高,但市场需求的诱导作用要明显强于财政政策的刺激作用;在市场下滑时期,行业需求对投资与产能配置的作用明显减弱,财政政策对投资的调整作用显著增强,对产能配置的影响不显著。不同市场状态下市场传导机制的非对称性正是钢铁行业产能过剩形成且难治理,进而导致供需结构失衡的主要原因。上述研究表明,仅通过市场调节机制是无法有效改善经济运行状态的,加强财政政策与市场传导机制的结合才是政府如何改善供给结构和需求变化不匹配问题的关键。     

Models for capacity and electricity market design

The paper employs Operations Research methods for analysis of electricity and capacity markets. We provide two algorithms that determine the optimal capacity structure with account of fixed and variable costs. The first one relates to the case where there are several capacity types, and for each type the capacity constraint is not binding. The second algorithm is applicable when electricity is produced by standard small generators with the same capacity and different costs. Then we study two typical architectures of the market and examine their Nash equilibria. We consider a uniform price supply function auction in the electricity market. For pay-as-bid and uniform price versions of the capacity market design, we compare the equilibrium outcomes with the optimal capacity structure. The paper shows that the market equilibrium corresponds to the optimal capacity structure under conditions of pure competition, full rationality, and completely informed agents in the market. However, under more realistic assumptions, selection of the optimal structure is unlikely. Finally we provide the auction design that realizes such selection of capacities and does not require any additional information of each producer besides his own production costs. We establish sufficient conditions for perfect competition in the market.     

Discrete time market with serial correlations and optimal myopic strategies

The paper studies discrete time market models with serial correlations. We found a market structure that ensures that the optimal strategy is myopic for the case of both power or log utility function. In addition, discrete time approximation of optimal continuous time strategies for diffusion market is analyzed. It is found that the performance of optimal myopic diffusion strategies cannot be approximated by optimal strategies with discrete time transactions that are optimal for the related discrete time market model.     

Network structure,perception level,and participants' welfares

Based on a market consisting of one monopoly and several customers who are embedded in an economic network, we study how the different perception levels about the network structure affect the two kinds of participants' welfares, and then provide some good strategies for the monopoly to mine the information of the network structure. The above question is the embodiment of the “complex structure and its corresponding functions” question often mentioned in the field of complexity science. We apply a two‐stage game to solve for the optimal pricing and consumption at different perception levels of the monopoly and further utilize simulation analysis to explore the influence patterns. We also discuss how this theoretic model can be applied to a real world problem by introducing the statistical exponential random graph model and its estimation method. Further, the main findings have specific policy implications on uncovering network information and demonstrate that it is possible for the policy‐maker to design some win–win mechanisms for uplifting both the monopoly's profit and the whole customers' welfare at the same time. © 2014 Wiley Periodicals, Inc. Complexity 21: 349–362, 2015     

A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans

A data-driven Neural Network (NN) optimization framework is proposed to determine optimal asset allocation during the accumulation phase of a defined contribution pension scheme. In contrast to parametric model based solutions computed by a partial differential equation approach, the proposed computational framework can scale to high dimensional multi-asset problems. More importantly, the proposed approach can determine the optimal NN control directly from market returns, without assuming a particular parametric model for the return process. We validate the proposed NN learning solution by comparing the NN control to the optimal control determined by solution of the Hamilton–Jacobi–Bellman (HJB) equation. The HJB equation solution is based on a double exponential jump model calibrated to the historical market data. The NN control achieves nearly optimal performance. An alternative data-driven approach (without the need of a parametric model) is based on using the historic bootstrap resampling data sets. Robustness is checked by training with a blocksize different from the test data. In both two and three asset cases, we compare performance of the NN controls directly learned from the market return sample paths and demonstrate that they always significantly outperform constant proportion strategies.     

Optimality of myopic strategies for multi-stock discrete time market with management costs

The paper studies multi-stock discrete time market models with serial correlations and with some management costs. We found a market structure that ensures that the optimal strategy is myopic for the case of either power or log utility function.     

Modelling a property's time to sale by inflating its previous selling price

The modelling of the time to sale for residential property is complex because of complicating factors such as properties changing their price or being withdrawn from sale, changing market conditions, the presence of submarkets, whether the property represents ‘good value’, and seller motivation. This paper analyses a data set of modern properties that contains information about their previous selling price. This information is used to value the properties instead of the standard method of regressing on the physical characteristics of the properties. A survival analysis approach is then used to model the time to sale with this time being adjusted to take into account changing market circumstances and alterations to the list price during this period. It is found that the hazard rate for selling a property is approximately constant for the first 9 months a property is marketed.     

Optimal payoff under the generalized dual theory of choice

We consider portfolio optimization under a preference model in a single-period, complete market. This preference model includes Yaari’s dual theory of choice and quantile maximization as special cases. We characterize when the optimal solution exists and derive the optimal solution in closed form when it exists. The optimal portfolio yields an in-the-money payoff when the market is good and zero payoff otherwise. Finally, we extend our portfolio optimization problem by imposing a dependence structure with a given benchmark payoff.     

HARA frontiers of optimal portfolios in stochastic markets

In this paper, we consider the optimal portfolio selection problem in continuous-time settings where the investor maximizes the expected utility of the terminal wealth in a stochastic market. The utility function has the structure of the HARA family and the market states change according to a Markov process. The states of the market describe the prevailing economic, financial, social and other conditions that affect the deterministic and probabilistic parameters of the model. This includes the distributions of the random asset returns as well as the utility function. We analyzed Black–Scholes type continuous-time models where the market parameters are driven by Markov processes. The Markov process that affects the state of the market is independent of the underlying Brownian motion that drives the stock prices. The problem of maximizing the expected utility of the terminal wealth is investigated and solved by stochastic optimal control methods for exponential, logarithmic and power utility functions. We found explicit solutions for optimal policy and the associated value functions. We also constructed the optimal wealth process explicitly and discussed some of its properties. In particular, it is shown that the optimal policy provides linear frontiers.     

Portfolio selection in stochastic markets with exponential utility functions

We consider the optimal portfolio selection problem in a multiple period setting where the investor maximizes the expected utility of the terminal wealth in a stochastic market. The utility function has an exponential structure and the market states change according to a Markov chain. The states of the market describe the prevailing economic, financial, social and other conditions that affect the deterministic and probabilistic parameters of the model. This includes the distributions of the random asset returns as well as the utility function. The problem is solved using the dynamic programming approach to obtain the optimal solution and an explicit characterization of the optimal policy. We also discuss the stochastic structure of the wealth process under the optimal policy and determine various quantities of interest including its Fourier transform. The exponential return-risk frontier of the terminal wealth is shown to have a linear form. Special cases of multivariate normal and exponential returns are disussed together with a numerical illustration.     

Locating Multiple Competitive Facilities: Spatial Interaction Models with Variable Expenditures

We develop a new framework for location of competitive facilities by introducing non-constant expenditure functions into spatial interaction location models. This framework allows us to capture two key effects – market expansion and cannibalization – within the same model.We develop algorithmic approaches for finding optimal or near-optimal solutions for several models that arise from choosing a specific form of the expenditure functions.     

可再生能源配额制下异质权力发电商的绿色生产决策研究

基于可再生能源配额的政策背景,本文构造了发电商三种不同市场权力结构下的绿色生产决策模型。讨论了敏感参数对异质权力发电商最优决策结果的影响,基于参数范围分析了不同权力结构下的最优决策效果。通过消费者效用、发电成本、环境保护的社会福利函数,识别出了参数最优取值范围内的最优决策效果的市场结构。结果显示:当绿证交易价格、最低配额比例超过最低临界值时,绿色发电商能够实现最优决策效果,但对传统发电商的最大利润产生了显著的“挤出”效应。若绿证交易价格和电力需求价格弹性系数在最佳区间内,则传统和绿色发电商既能够达到最优决策效果,又可以最大限度促进社会福利效用的单调增加,即存在绿色发电商主导的最优市场决策结构,但不存在最优纳什决策结构。     

An Asymptotic Expansion Scheme for Optimal Investment Problems

We shall propose a new computational scheme for the evaluation of the optimal portfolio for investment. Our method is based on an extension of the asymptotic expansion approach which has been recently developed for pricing problems of the contingent claims’ analysis by Kunitomo and Takahashi (1992, 1995, 2001, 2003), Yoshida (1992), Takahashi (1995, 1999), Takahashi and Yoshida (2001). In particular, we will explicitly derive a formula of the optimal portfolio associated with maximizing utility from terminal wealth in a financial market with Markovian coefficients, and give a numerical example for a power utility function. This revised version was published online in June 2006 with corrections to the Cover Date.     

股指极端波动下中国股市复杂网络结构及中心性分析

基于复杂网络理论,以2015年、2008年国内两次股灾为背景,分别构建股灾发生前、中、后的中国股市网络,通过度、度分布、平均路径长度等基本拓扑指标,分析中国股市网络特性及网络结构的变化,综合利用度中心性、介数中心性及接近度中心性,筛选出各时期网络中的核心股票、核心行业并分析其变化情况,基于网络特征向量中心性分析股市的系统性风险及变化情况,通过仿真实验分析股市网络的鲁棒性。研究表明:两次股灾背景下的中国股市复杂网络均具有小世界性和无标度性;与2008年国际金融危机相比,2015年国内股灾对中国股市的影响强度更大,且2015年中国股市对金融风险的弹性更大;股灾期间各行业版块具有明显的风险传染性,指出各行业板块对稳定股市、修复股指的作用;股指极端波动时,股灾的外生冲击会使股市的系统性风险加大,与2015年国内股灾时期相比,2008年国际金融危机时期的股市系统性风险更大;中国股市网络对随机攻击具有一定鲁棒性,但对蓄意攻击具有脆弱性,股灾的外生冲击会降低中国股市网络的鲁棒性。研究为把握股市极端波动风险下的市场结构特征、股市风险管理提供了参考。     

Mean–variance optimal portfolios in the presence of a benchmark with applications to fraud detection

We first study mean–variance efficient portfolios when there are no trading constraints and show that optimal strategies perform poorly in bear markets. We then assume that investors use a stochastic benchmark (linked to the market) as a reference portfolio. We derive mean–variance efficient portfolios when investors aim to achieve a given correlation (or a given dependence structure) with this benchmark. We also provide upper bounds on Sharpe ratios and show how these bounds can be useful for fraud detection. For example, it is shown that under some conditions it is not possible for investment funds to display a negative correlation with the financial market and to have a positive Sharpe ratio. All the results are illustrated in a Black–Scholes market.     

Product damage and free sampling: a newsvendor model with passive and proactive self-consumption

A retailer sells a single product for a single period. During transportation and storage, some of these products are consumed by the retailer either (1) due to unavoidable damages (passive self-consumption), or (2) distributed for free to the customers (proactive self-consumption). This creates a mismatch between the amount purchased by the retailer and the amount available for sale. We study passive self-consumption with (i) fixed and (ii) proportional consumption, and proactive self-consumption with (iii) additive and (iv) multiplicative demand. Under proactive self-consumption, the retailer holds more inventory and receives a higher profit; the reverse is true under passive self-consumption. Yet, (i), (iii) and (iv) result in a higher order quantity and same fill rate compared to no self-consumption, (ii) may result in a higher or lower order quantity with a lower fill rate. When both types of self-consumption coexist, the optimal policy can be complicated. We characterize the optimal policy and show through numerical studies that the optimal policy can take at most three formats: sell to the market with positive proactive self-consumption, sell to the market with zero proactive self-consumption and do not sell to the market. Interestingly, the optimal order quantity is not smooth in the fraction of the proportional self-consumption. Further we find that when the market adoption rate is uncertain, the optimal strategy preserves a similar structure. The retailer benefits from expediting if the difference between the high and the low market adoption rates is high and the probability of a high market adoption rate is low.     

An equilibrium model for urban transit assignment based on game theory

The urban public transport system is portrayed as a special commodity market where passenger is consumer, transit operator is producer and the special goods is the service for passenger’s trip. The generalized Nash equilibrium game is applied to describe how passengers adjust their route choices and trip modes. We present a market equilibrium model for urban public transport system as a series of mathematical programmings and equations, which is to describe both the competitions among different transit operators and the interactive influences among passengers. The proposed model can simultaneously predict how passengers choose their optimal routes and trip modes. An algorithm is designed to obtain the equilibrium solution. Finally, a simple numerical example is given and some conclusions are drawn.     

Dynamic portfolio selection with market impact costs

This paper concerns optimal dynamic portfolio choice with quadratic utility when there are market impact costs. The optimal policy is difficult to characterize, so we look instead for sub-optimal policies. Our proposed suboptimal policy solves a tractable dynamic portfolio choice problem where the cost of trading is captured in the objective instead of the price dynamics. A multiple time scale asymptotic expansion shows that our proposed policy has sensible structural properties, while numerical experiments show promising performance and robustness properties.     

A leader-followers model of power transmission capacity expansion in a market driven environment

We introduce a model for analyzing the upgrade of the national transmission grid that explicitly accounts for responses given by the power producers in terms of generation unit expansion. The problem is modeled as a bilevel program with a mixed integer structure in both upper and lower level. The upper level is defined by the transmission company problem which has to decide on how to upgrade the network. The lower level models the reactions of both power producers, who take a decision on new facilities and power output, and Market Operator, which strikes a new balance between demand and supply, providing new Locational Marginal Prices. We illustrate our methodology by means of an example based on the Garver’s 6-bus Network.