Fenghua Wen

SKEWNESS OF RETURN DISTRIBUTION AND COEFFICIENT OF RISK PREMIUM

The skewness of the return distribution is one of the important features of the security price. In this paper, the authors try to explore the relationship between the skewness and the coefficient of risk premium. The coefficient of the risk premium is estimated by a GARCH-M model, and the robust measurement of skewness is calculated by Groeneveld-Meeden method. The empirical evidences for the composite indexes from 33 securities markets in the world indicate that the risk compensation requirement in the market where the return distribution is positively skewed is virtually zero, and the risk compensation requirement is positive in a significant level in the market where the return distribution is negative skewed. Moreover, the skewness is negatively correlated with the coefficient of the risk premium.

Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model

Basing on the Heterogeneous Autoregressive with Continuous volatility and Jumps model (HAR-CJ), converting the realized Volatility (RV) into the adjusted realized volatility (ARV), and making use of the influence of momentum effect on the volatility, a new model called HAR-CJ-M is developed in this paper. At the same time, we also address, in great detail, another two models (HAR-ARV, HAR-CJ). The applications of these models to Chinese stock market show that each of the continuous sample path variation, momentum effect, and ARV has a good forecasting performance on the future ARV, while the discontinuous jump variation has a poor forecasting performance. Moreover, the HAR-CJ-M model shows obviously better forecasting performance than the other two models in forecasting the future volatility in Chinese stock market.

A COPULA-BASED CORRELATION MEASURE AND ITS APPLICATION IN CHINESE STOCK MARKET

In this paper, a copula-based correlation measure is proposed to test the interdependence among stochastic variables in terms of copula function. Based on a geometric analysis of copula function, a new derivation method is introduced to derive the Gini correlation coefficient. Meantime theoretical analysis finds that the Gini correlation coefficient tends to overestimate the tail interdependence in the case of stochastic variables clustering at the tails. For this overestimation issue, a fully new correlation coefficient called Co is developed and extended to measure the tail interdependence. Empirical study shows that the new correlation coefficient Co can effectively solve the overestimation issue, which implies that the proposed new correlation coefficient is more suitable to describe the interdependence among stochastic variables than the Gini correlation coefficient.

A multiscale neural network learning paradigm for financial crisis forecasting

A financial crisis is typically a rare kind of an event, but it hurts sustainable economic development when it occurs. This study proposes a multiscale neural network learning paradigm to predict financial crisis events for early-warning purposes. In the proposed multiscale neural network learning paradigm, currency exchange rate, a typical financial indicator that usually reflects economic fluctuations, is first chosen. Then a Hilbert-EMD algorithm is applied to the currency exchange rate series. Using the Hilbert-EMD procedure, some intrinsic mode components (IMCs) of the currency exchange rate series, with different scales, can be obtained. Subsequently, the internal correlation structures of different IMCs are explored by a neural network model. Using the neural network weights, some important IMCs are selected as the final neural network inputs and some unimportant IMCs that are of little use in mapping from inputs to output are discarded. Using these selected IMCs, a neural network learning paradigm is used to predict future financial crisis events, based upon some historical data. For illustration purpose, the proposed multiscale neural network learning paradigm is applied to exchange rate data of two Asian countries to evaluate the state of financial crisis. Experimental results reveal that the proposed multiscale neural network learning paradigm can significantly improve the generalization performance relative to conventional neural networks.

Genetic algorithm-based multi-criteria project portfolio selection

Project portfolio selection is one of the most important decision-making problems for most organizations in project management and engineering management. Usually project portfolio decisions are very complicated when project interactions in terms of multiple selection criteria and preference information of decision makers (DMs) in terms of the criteria importance are taken into consideration simultaneously. In order to solve this complex decision-making problem, a multi-criteria project portfolio selection problem considering project interactions in terms of multiple selection criteria and DMs’ preferences is first formulated. Then a genetic algorithm (GA)-based nonlinear integer programming (NIP) approach is used to solve the multi-criteria project portfolio selection problem. Finally, two illustrative examples are presented for demonstration and verification purposes. Experimental results obtained indicate that the GA-based NIP approach can be used as a feasible and effective solution to multi-criteria project portfolio selection problems.

Dynamics Analysis of a Class of Delayed Economic Model

This investigation aims at developing a methodology to establish stability and bifurcation dynamics generated by a class of delayed economic model, whose state variable is described by the scalar delay differential equation of the form . At appropriate parameter values, linear stability and Hopf bifurcation including its direction and stability of the economic model are obtained. The main tools to obtain our results are the normal form method and the center manifold theory introduced by Hassard. Simulations show that the theoretically predicted values are in excellent agreement with the numerically observed behavior. Our results extend and complement some earlier publications.

STUDY ON THE FRACTAL AND CHAOTIC FEATURES OF THE SHANGHAI COMPOSITE INDEX

The Hurst exponent derived by the R/S analysis method of Shanghai stock markets logarithmic return series is about 0.6298. This shows that the Shanghai stock market exhibits fractal features, and a long memory cycle of about one-and-a-half years. With the reconstruction of phase space, the Shanghai Stock attractor dimension converges to 1.335, which means that the Shanghai stock market has chaotic features, and constructing a dynamic system of the Shanghai stock market needs at least two variables. The findings from the principal component analysis support the conclusion of the existence of chaotic features of the Shanghai stock market. The fractal and chaotic features of the Shanghai stock market reveal the nonlinear properties of the Chinese stock market, and the nonlinearity perspective will be more conducive to the formulation of countermeasures for the development of the Chinese stock market.

Investors’ Risk Preference Characteristics and Conditional Skewness

Perspective on behavioral finance, we take a new look at the characteristics of investors’ risk preference, building the D-GARCH-M model, DR-GARCH-M model, and GARCHC-M model to investigate their changes with states of gain and loss and values of return together with other time-varying characteristics of investors’ risk preference. Based on a full description of risk preference characteristic, we develop a GARCHCS-M model to study its effect on the return skewness. The top ten market value stock composite indexes from Global Stock Exchange in 2012 are adopted to make the empirical analysis. The results show that investors are risk aversion when they gain and risk seeking when they lose, which effectively explains the inconsistent risk-return relationship. Moreover, the degree of risk aversion rises with the increasing gain and that of risk seeking improves with the increasing losses. Meanwhile, we find that investors’ inherent risk preference in most countries displays risk seeking, and their current risk preference is influenced by last period’s risk preference and disturbances. At last, investors’ risk preferences affect the conditional skewness; specifically, their risk aversion makes return skewness reduce, while risk seeking makes the skewness increase.

A modified Perry's conjugate gradient method-based derivative-free method for solving large-scale nonlinear monotone equations

In this paper, we propose a derivative-free method for solving large-scale nonlinear monotone equations. It combines the modified Perrys conjugate gradient method (I.E. Livieris, P. Pintelas, Globally convergent modified Perrys conjugate gradient method, Appl. Math. Comput., 218 (2012) 9197-9207) for unconstrained optimization problems and the hyperplane projection method (M.V. Solodov, B.F. Svaiter, A globally convergent inexact Newton method for systems of monotone equations, in: M. Fukushima, L. Qi (Eds.), Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods, Kluwer Academic Publishers, 1998, pp. 355-369). We prove that the proposed method converges globally if the equations are monotone and Lipschitz continuous without differentiability requirement on the equations, which makes it possible to solve some nonsmooth equations. Another good property of the proposed method is that it is suitable to solve large-scale nonlinear monotone equations due to its lower storage requirement. Preliminary numerical results show that the proposed method is promising.

Another improved Wei–Yao–Liu nonlinear conjugate gradient method with sufficient descent property

Abstract Recently, Zhang [13] take a little modification to the Wei–Yao–Liu nonlinear conjugate gradient method proposed by Wei et al. [10] such that the modified method (called NPRP method in Zhang [13] ) satisfies sufficient descent condition with greater parameter σ ∈ 0 , 1 2 in the strong Wolfe line search and converges globally for nonconvex minimization with the strong Wolfe line search. In this paper, we take a little modification to the NPRP method such that the modified NPRP method possesses better properties than the NPRP method in Zhang [13] . Firstly, the modified NPRP method possesses the sufficient descent property without any line searches. Secondly, the modified NPRP method converges globally for nonconvex minimization with Wolfe line search or Armijo line search. Moreover, we extend these results to the Hestenes–Stiefel (HS) method and prove that the modified HS method also possesses sufficient descent property and global convergence with the standard Wolfe conditions. Numerical results are reported by utilizing some test problems in the CUTE library.