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15 April 2020, Volume 37 Issue 2 Previous Issue    Next Issue

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Optimal Behavioral Portfolio Selection for an Individual under Inflation Risk GUO Wen-jing, JIANG Hai-wen 2020, 37 (2):  131-145.  doi: 10.3969/j.issn.1005-3085.2020.02.001 Abstract ( 127 )   PDF (789KB) ( 257 )   Save It is well known that inflation risk is an important factor that affects investors' making decisions. Also, the influence of investors' behavioral characteristics on portfolio selec-tions can not be ignored. This paper discusses the problem of optimal behavioral portfolio selection for an individual under inflation risk. At first, in the financial market, we introduce an inflation-linked index bond, which can be used to hedge the inflation risk. Meanwhile, investors are assumed to be loss averse. Thus, we get the optimal individual behavioral portfolio selection model under inflation risk. Then, maximizing the expected utility of the part investor's terminal wealth exceeds the reference level, the explicit solutions for the optimal strategies and terminal wealth are derived by martingale approach, and the properties of optimal strategies are discussed by property analysis and numerical simulation. Finally, the numerical results show that the inflation risk and loss aversion indeed have a significant effect on the optimal strategies. Related Articles | Metrics

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Sliding Mode Synchronization of Fractional-order Hyperchaotic Financial Systems with Uncertainty and Outer Disturbance MAO Bei-xing 2020, 37 (2):  146-154.  doi: 10.3969/j.issn.1005-3085.2020.02.002 Abstract ( 133 )   PDF (398KB) ( 398 )   Save In this paper, the synchronization problem of hyperchaotic fractional-order financial systems with uncertainty and outer disturbances are studied based on the sliding mode control and integral sliding mode control techniques. Two sufficient conditions are established to ensure the fractional-order uncertain hyperchaotic financial systems acquiring sliding mode and integral sliding mode synchronization by designing sliding mode functions and controllers through founding adaptive rules using fractional-order integral and calculus. The conclusions are verified by Matlab numerical simulations. The sliding mode functions, controllers and adaptive rules which are designed in the paper can be planted for studying sliding mode synchronization of integer-order chaotic systems. The approaches used in the paper supply the techniques to study the fractional-order systems and can be extended to the integer-order systems synchronization problems. Related Articles | Metrics

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Mean-variance Lower-semi-variance Portfolio Model with Transaction Costs WANG Xiao-qin, GAO Yue-lin 2020, 37 (2):  155-164.  doi: 10.3969/j.issn.1005-3085.2020.02.003 Abstract ( 236 )   PDF (322KB) ( 469 )   Save The research on portfolio selection provides a quantifiable way and scientific basis for investment decision and risk management. In this paper, we introduce a typical nonconcave and nonconvex transaction cost function, and establish a mean-variance lower-semi-variance portfolio model with transaction cost. Considering that different investors have different degrees of risk aversion, the risk aversion coefficient is introduced and the double objective portfolio optimization model is transformed into single objective portfolio optimization model. The model is solved by using Teaching and Learning algorithm, and the optimal portfolio under different returns is obtained. At the same time, the effective boundary of portfolio is given. Finally, the advantage of the algorithm is analyzed, and a good simulation result is presented. Related Articles | Metrics

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Option Pricing Under Mixed Exponential Jump Diffusion Model Based on the FST Method ZHANG Su-mei, ZHAO Jie-qiong 2020, 37 (2):  165-176.  doi: 10.3969/j.issn.1005-3085.2020.02.004 Abstract ( 165 )   PDF (444KB) ( 294 )   Save The mixed exponential jump-diffusion model that can approximate any distribution is widely used to describe the actual trend of stock price. Based on the Fourier Space Time-stepping (FST) method, this paper considers European option pricing under the mixed exponential jump-diffusion model. By the Fourier transform and the characteristic exponent, the partial integral-differential equation for pricing European options is transformed into an ordinary differential equations and solved to obtain European option prices. Numerical results indicate that the FST method is accurate and fast. Moreover, by collecting real market data and the nonlinear least squares method, we apply the obtained option price to model calibration to obtain the model parameters which match the real market. By examining the impact of jump parameters on the implied volatility, we find that the mixed exponential jump-diffusion model can well reflect the volatility ``smile" of asset returns. Related Articles | Metrics

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Transient and Equilibrium Solutions of Queue Length Distribution for $M/G/1$ Queueing System with Min($N,D,V$)-policy and Single Server Vacation WANG Min, TANG Ying-hui 2020, 37 (2):  177-202.  doi: 10.3969/j.issn.1005-3085.2020.02.005 Abstract ( 162 )   PDF (287KB) ( 218 )   Save This paper considers the $M/G/1$ queueing system with single server vacation which can be interrupted immediately according to the Min($N,D,V$)-policy. By applying the total probability decomposition technique and the Laplace transformation, the transient and steady-state properties of the queue length from any initial state are discussed, and the Laplace transformation expression of the transient solution of queue length distribution is obtained. Moreover, we derive the recursive expressions of the equilibrium solution of queue length distribution for convenient calculation. Furthermore, we propose the stochastic decomposition structures of the steady-state queue length, the explicit expressions for the probability distribution of the additional queue length and the corresponding results for some special cases. Finally, by numerical examples, we discuss the sensitivity of the steady state queue length distribution towards system parameters and analyze the influence of different parameters on system performance. Related Articles | Metrics

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Bayesian Modeling and Variational Inference for Logistic Group Sparse Regression Model SHEN Yuan-yuan, CAO Wen-fei, HAN Guo-dong 2020, 37 (2):  203-214.  doi: 10.3969/j.issn.1005-3085.2020.02.006 Abstract ( 231 )   PDF (226KB) ( 294 )   Save In engineering applications, such as data mining, cost prediction, risk prediction etc., Logistic regression is a class of very important prediction methods. Presently, most of Logistic regression methods are designed based on optimization criteria, and these methods have several shortcomings such as tedious parameter tuning, poor model interpretation, and with the estimator no confidence interval. Therefore, we study the modeling and inference problem of Logistic group sparse regression from the perspective of Bayesian probability in this paper. Specifically, a Bayesian probability model of Logistic group sparse regression is firstly proposed by using the Gaussian-variance mixture formula. Then, an efficient inference algorithm is designed through the variational Bayesian method. Numerical results on simulated data show that the proposed method has better prediction performance. Related Articles | Metrics

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Depiction Technology of Super Corona Distance Matrix Spectrum XU Xiao-jing, WANG Pei-wen, WANG Zhi-ping 2020, 37 (2):  215-230.  doi: 10.3969/j.issn.1005-3085.2020.02.007 Abstract ( 121 )   PDF (271KB) ( 335 )   Save The topological structure of the graph has important significance in chemical molecular structure, and the various matrices of the graph contain the topology information of the graph. In this paper, we depict the distance spectrum of four kinds of double corona, according to the definition of distance spectrum and the structure characteristics of four types of graphs, the corresponding distance matrix is obtained by mathematical induction. On the basic of them, the block matrix is constructed, which is a super corona distance matrix. By using the uniqueness of the matrix eigenvalues, the eigenvalues and eigenvectors of the super corona distance matrix are solved, and the accuracy and reliability of the conclusion are verified simultan-eously. Finally, we research the distance spectrum of $G^{(S)}\circ\{G_{1},G_{2}\}$, $G^{(Q)}\circ\{G_{1},G_{2}\}$, $G^{(R)}\circ\{G_{1},G_{2}\}$, $G^{(T)}\circ\{G_{1},G_{2}\}$, when $G$ is a complete graph and $G_{1}$, $G_{2}$ is a regular graph. Related Articles | Metrics

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The Modified Local Crank-Nicolson Schemes for Rosenau-Burgers Equation Muyassar Ahmat, Abdurishit Abduwal, Abdugeni Abduxkur 2020, 37 (2):  231-244.  doi: 10.3969/j.issn.1005-3085.2020.02.008 Abstract ( 194 )   PDF (179KB) ( 516 )   Save Two class of modified local Crank-Nicolson schemes for Rosenau-Burgers equation are proposed. Firstly, we obtain the exact solution of the ODE which reached from the original PDE by using central finite difference discretization in space direction. Next, the exponential coefficient matrix of this equation is approximated by using matrix splitting technique by line and element. Finally, two types of methods are achieved by using modified local Crank-Nicolson scheme. The stability, convergence and priori error estimation of two schemes are discussed. The accuracy of theoretical proof and efficiency of both schemes are demonstrated by numerical results. The proposed methods possess the advantages of simple structure and high accuracy. Related Articles | Metrics

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Link Prediction in Complex Networks Incorporating the Degree and Community Information DENG Ya-jing, ZHANG Hai, GUO Xiao, GOU Ming, WANG Yao 2020, 37 (2):  245-259.  doi: 10.3969/j.issn.1005-3085.2020.02.009 Abstract ( 139 )   PDF (343KB) ( 448 )   Save Recently the structural features of networks are widely used to the link prediction problem. Based on the information-theoretic model, we propose a more general information-theoretic model by encoding various network structural information. Specifically, for the scale-free networks, a set of Neighbor Set Information (NSI) based indices by suppressing the contribution of high-degree neighbors are proposed. Secondly, to incorporate the community information, this paper further presents a set of NSI based indices in which the prior probability of a node pair being connected is encodes the community information of networks. The experimental results on a series of real networks show that our methods outperform other classical link prediction indices. Related Articles | Metrics