Shuguang Zhang

• Ph. D
• University of Science and Technology of China

This paper studies the optimal investment problem with an option compensation scheme under rank-dependent expected utilities. Due to the presence of distortion functions and a nonconcave actual utility function, the conventional optimization tools like convex optimization and dynamic programming cannot be applied to this model. To address this chal...

This study delves into the complicated relationship between environmental performance and support for energy transition, emphasizing how ecological change influences this process. The analysis covers the period from 2005 to 2021, which includes a time of major worldwide changes in geopolitics, financial markets, and environmental concerns. In order...

Purpose The purpose of the research paper is to investigate the relationship between personality traits and investment decisions in the crypto market, including cryptocurrencies and NFTs. The study aims to explore the effect of dark personalities and the big five personalities on investment decisions in the crypto market. Design/methodology/approa...

In this paper, we consider the optimal investment problem with both probability distortion/weighting and general non-concave utility functions with possibly finite number of inflection points, and apply a Lagrange duality based relaxation approach for solving this problem. Existing literature has shown the equivalent relationships (strong duality)...

Important nodes can determine the internal structure of complex networks and reveal the internal relationships of real-world systems, and identifying key nodes in complex networks is one of the important research areas of complex network science. As the king of commodities, changes in the price of gold significantly impact the economic development...

We solve in closed-form the optimal investment strategies in equity and VIX derivatives in a stochastic volatility model with jumps. Our framework includes both complete market and incomplete market cases, when diffusive risk, volatility risk and jump risk are present. VIX derivatives allow for direct exposure to volatility risk compared to equity...

This article proposes return-on-equity (ROE) networks for portfolio optimization, which integrate the DuPont analysis and graph theory. Portfolio diversification is interpreted as follows: An intercluster relationship of the network structure diversifies business models, whereas an innercluster relationship variegates different industries. The prop...

In observational studies, identifying subgroups and exploring heterogeneity is of practical significance. However, causal inference at the individual level is a challenging problem due to the absence of counterfactual outcomes and the presence of selection bias. To address this issue, we propose a general framework called TRIMATCH for estimating he...

In this paper, we study the robust recovery of signals for general noise by ℓ 1 - 2 / ℓ p \ell_{1-2}/\ell_{p} ( 2 ≤ p < + ∞ 2\leq p<+\infty ) minimization with partially known support (PKS). A recovery condition for ℓ 1 - 2 / ℓ p \ell_{1-2}/\ell_{p} minimization by incorporating prior support information is established, and an error estimate is obt...

This paper introduces Price-to-Earnings Ratio Network (PEN) analysis as an alternative to mean–variance analysis for portfolio optimization. The equivalence among Price-to-Earnings (P/E) ratios, node degree distribution and capital allocation distribution is established with a Havel–Hakimi network structure. Such equivalences allow network entropy...

Based on functional relative errors, we develop a functional penalized smooth least absolute relative error (FPSLARE) criterion for locally sparse functional multiplicative regression models. Under some mild conditions, we establish the oracle properties of FPSLARE estimators, including the consistency of model selection and the asymptotic normalit...

This paper discusses the block sparse signal recovery when the partially block support information is available. A high order block RIP condition for our proposed weighted ℓ2/ℓ1−2 minimization is established, which in absence of support information is proved to improve the previous one. We demonstrate that the obtained recovery condition is weaker...

This paper provides a new theoretical support for block sparse recovery. By embedding prior support information into the block [Formula: see text] minimization with [Formula: see text], we establish a sufficient condition on block [Formula: see text]-restricted isometry property (RIP) to guarantee the stable and robust recovery of block sparse sign...

In this paper, we consider the optimal investment problem with both probability distor-tion/weighting and general non-concave utility functions with possibly finite number of inflection points, and propose a general approach for solving this problem. Existing literature has shown the equivalent relationships (strong duality) between the concavified...

By introducing the weighted versions of null space property (NSP) of l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> minimization, this paper studies the recovery scheme for l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> minimi...

This article proposes the use of a novel approach to portfolio optimization, referred to as “Fundamental Networks” (FN). FN is an effective and robust network-based fundamental-incorporated method, and can be served as an alternative to classical mean-variance framework models. As a proxy for a portfolio, a fundamental network is defined as a set o...

In this paper, we propose a nonconcave penalized M-estimation of the least absolute relative errors (penalized M-LARE) method for a sparse multiplicative regression model, where the dimension of model can increase with the sample size. Under certain appropriate conditions, the consistency and asymptotic normality for the penalized M-LARE estimator...

In this paper, we consider the optimal investment problem with both probability distor- tion/weighting and general non-concave utility functions with possibly finite number of inflection points. Our model contains the model under cumulative prospect theory (CPT) as a special case, which has inverse S-shaped probability weighting and S-shaped utilit...

In this paper, we consider the optimal investment problem with both probability distor- tion/weighting and general non-concave utility functions with possibly finite number of inflection points. Our model contains the model under cumulative prospect theory (CPT) as a special case, which has inverse S-shaped probability weighting and S-shaped utilit...

Using data from BRICS countries, we apply the TVP-VAR model to analyze the effects of exchange rate fluctuations on their stock markets and the mechanisms leading to those effects. We find that for BRICS countries, there are similarities as well as differences in the extent, direction, and duration of the effects of exchange rate changes on the sto...

In this paper, we solve in closed-form for the optimal investment strategies in both equity derivatives and VIX derivatives in a stochastic volatility model with jumps. This is motivated by the recent developments of the VIX derivatives market, and the increasing adoption of VIX derivatives in portfolio management practices. VIX derivatives allow f...

The regression dependence is a practical dependent structure which provides a good mechanism to describe non-life insurance businesses, and further allows applications in various areas. In this paper, we investigate large deviations for random sums of extended negatively dependent random variables in the general renewal risk model with the regressi...

This paper proposes a nonstandard renewal counting process based on web Markov skeleton process, called web renewal process, which allows applications in the field of natural science and social science. Parallel with the standard renewal counting process, some properties of the web renewal process and the corresponding web renewal reward processes,...

This paper investigates a nonstandard renewal counting process with dependent inter-arrival times-web renewal process. Several limit properties, including the tail of the exponential moment which is a crucial condition in many situations, are obtained. Then the results are applied in insurance to derive precise large deviations and moderate deviati...

In this paper, we study the Moderate Deviation Principle for a perturbed stochastic heat equation in the whole space (Formula presented.). This equation is driven by a Gaussian noise, white in time and correlated in space, and the differential operator is a fractional derivative operator. The weak convergence method plays an important role.

The current study has tried to explore the types of services provided by Pakistan. A total of 1,089 bank customers affiliated with eight major banks across four major cities were interviewed through specified questionnaire. The gender wise distribution is 87% males and 13% female of the interviewed, individuals; while account type-based distributio...

This paper investigates continuous-time optimal portfolio and consumption problems under loss aversion in an infinite horizon. The investor’s goal is to choose optimal portfolio and consumption policies to maximize total discounted S-shaped utility from consumption. The consumption rate process is subject to a downside constraint. The optimal consu...

Based on the reduced-form approach, this paper investigates the pricing problems of default-risk bonds and credit default swaps (CDSs) for a fractional stochastic interest rate model with jump under the framework of primary-secondary. Using properties of the quasi-martingale with respect to the fractional Brownian motion and the jump technique in P...

In this paper, we establish a moderate deviation principle for stochastic Volterra equation by using the weak convergence approach. A maximal inequality for stochastic integral plays an important role. As an application, we give an interesting example: a stochastic differential equation driven by fractional Brownian motion.

This paper investigates continuous-time optimal portfolio and consumption problems under loss aversion in an infinite time horizon. The investor's goal is to choose the optimal portfolio and consumption policies to maximize total discounted S-shaped utility from consumption. The problems are solved under two different situations respectively for th...

In this paper, we establish a central limit theorem and a moderate deviation principle for the positive diffusions, including the CEV and CIR models. The proof is based on the exponential approximations theorem and Burkholder-Davis-Gundy’s inequality. MSC: 60H10, 60F05, 60F10.

A continuous-time portfolio selection problem with consumption and terminal gains was considered in the framework of prospect theory. The Inada conditions for the utility functions were discarded by assuming a regularity condition on the terminal utility. First, the problem with the reference point depending on the wealth was considered, and the co...

In this paper, we establish a large deviation principle for stochastic differential delay equations driven by both Brownian motions and Poisson random measures. The weak convergence method plays an important role.

In this paper, we proved a central limit theorem and established a moderate deviation principle for a perturbed stochastic heat equations defined on [0,T]×[0,1]d . This equation is driven by a Gaussian noise, white in time and correlated in space.

This paper investigates a dynamic asset allocation problem for loss-averse investors in a jump-diffusion model where there are a riskless asset and N risky assets. Specifically, the prices of risky assets are governed by jump-diffusion processes driven by an m-dimensional Brownian motion and a (N − m)-dimensional Poisson process. After converting t...

Pricing variance swaps under stochastic volatility has been an important subject pursued recently. Various approaches have been proposed, mainly due to the substantially increased trading activities of volatility-related derivatives in the past few years. In this note, the authors develop analytical method for pricing variance swaps under stochasti...

In this study we examined the effect of structural break points in conditional volatility on variance persistency of asymmetric GARCH models. We used Bai and Perron methodology to detect structural break points in conditional variance of daily stock returns of 7 emerging markets (4-European and 3-Asian) from 1997 to 2014. We implied Exponential GAR...

This paper studies the optimization problem with both investment and proportional reinsurance control under the assumption that the surplus process of an insurance entity is represented by a pure diffusion process. The company can buy proportional reinsurance and invest its surplus into a Black-Scholes risky asset and a risk free asset without rest...

This paper is concerned with a linear quadratic stochastic two-person zero-sum differential game with constant coefficients in an infinite time horizon. Open-loop and closed-loop saddle points are introduced. The existence of closed-loop saddle points is characterized by the solvability of an algebraic Riccati equation with a certain stabilizing co...

The changes of numeraire can be used as a very powerful tool in pricing contingent claims in the context of a complete market. By using the method of numeraire changes to evaluate convertible bonds when the value of firm, and those of zero-coupon bonds follow general adapted stochastic processes in this paper, using It_o theorem and Gisanov theorem...

This paper investigates the pricing of options written on non-traded assets and trading strategies for the stock and option in an exponential utility maximization framework. Under the assumption that the option can be continuously traded without friction just as the stock, a dynamic relationship between their optimal positions is derived by using t...

We further extend the renewal risk model and introduce a more practical regression-type dependence structure between inter-arrival times and claim sizes, which is described in the framework of a web Markov skeleton process. We investigate large deviations of the aggregate claims, and, for a case of heavy-tailed claims, we obtain a precise large-dev...

In order to address the pricing problems of fixed income derivatives in the context of stochastic volatility, the Homotopy analysis method was applied to give the pricing formula of caplet and bond option in a LIBOR market model with stochastic volatility in which the volatility is an Ornstein-Uhlenbeck process under corresponding forward measure,...

Option pricing problem plays an extremely important role in quantitative finance. In complete market, Black-Scholes-Merton theory has been central to the development of financial engineering as both discipline and profession. However, in incomplete market, there are not any replicating portfolios for those options, and thus, the market traders cann...

This paper studies a general dynamic portfolio selection model for loss-averse investors with wealth constraints in a complete market. By applying the martingale approach, we derive the optimal terminal wealth of the investors with or without the benchmark floor constraints respectively. Simultaneously, the existence and uniqueness of the correspon...

The backward stochastic differential equations driven by both standard and fractional Brownian motions (or, in short, SFBSDE) are studied. A Wick-Itô stochastic integral for a fractional Brownian motion is adopted. The fractional Itô formula for the standard and fractional Brownian motions is provided. Introducing the concept of the quasi-condition...

In this paper we mainly investigated the resource allocation optimization problem in three population models: death process, PDE model and birth and density-independent growth model. Considering the influence on population growth from different factors, find the best proportion of population to obtain the biggest economic benefit. Furthermore, we cons...

In this paper, we consider the stochastic optimal control problems under G-expectation. Based on the theory of backward stochastic differential equations driven by G-Brownian motion, which was introduced in [10.11], we can investigate the more general stochastic optimal control problems under G-expectation than that were constructed in [28]. Then w...

Methods for the reconstruction of temperature fields in an intelligent building with temperature data of discrete observation positions is a current topic of research. To reconstruct temperature field with observation data, it is necessary to model the identification of temperature in each observation position. In this paper, models for temperature...

MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is appro...

In this study, we investigate a general continuous-time portfolio selection model with loss aversion in an incomplete market where the number of stocks is strictly less than the dimension of the underlying Brownian motion. The investor’s preference facing market risks is defined by an S-shaped value function. By transforming the market into a compl...

The identification of temperature filed of monitored region is one of the key steps for the energy efficiency management in intelligent building. In this paper, the identification of temperature field is treated as one optimization problem and the identification structure for that optimization problem is implemented with a RBF neural network. To in...

Methods for the reconstruction of temperature fields in an intelligent building with temperature data of discrete observation positions is a current topic of research. To reconstruct temperature field with observation data, it is necessary to model the identification of temperature in each observation position. In this paper, models for temperature...

The identification of temperature filed of monitored region is one of the key steps for the energy efficiency management in intelligent building. In this paper, the identification of temperature field in monitored region is formalized as one optimization problem. With the formalization, a feed forward neural network is used to identify the temperat...

This paper examines hedging strategy in Chinese stock index futures market. Focus on task of forming a portfolio which holds long position of limited numbers of stock that outperforms the underlying index, and meanwhile holds short position of related index future. In order to select proper stocks, the particle swarm optimization is implemented, wi...

Lose aversion refers to the psychological phenomenal that losses and disadvantages have greater impact on preferences than gains and advantages. While to a considerable extent, risk aversion as it is commonly observed is caused by loss aversion. This paper integrates Markowitz's efficient frontier theory into lose aversion function, and the portfol...

Option pricing problem plays an extremely important role in quantitative finance. In complete market, Black-Scholes-Merton theory has been central to the development of financial engineering as both discipline and profession. However, in an incomplete market, there isn't any replicating portfolios for those options, and thus, we cannot apply the la...

Methods for the identification of temperature in intelligent building and building equipments is one of hot topics focused by lots of researchers in that research area. To implement the process of inspecting and forecasting of energy efficiency in building and its accessory, a feed forward neural network is used as the identification structure for...

In the flnancial market including n risky assets with prices following the full-observed geometric-Brownian Motion model and a bank account with stochastic interest rate, we study the problem of maximizing the probability of an investor's wealth at terminal time T meeting or exceeding the value of a contingent claim C which expires at T. In this in...

Chance discovery is a new research topic on cognition psychology inspired deep data analysis. Scene is the contact point of human process and computer process in the double helical model of chance discovery. Event archipelago is the basic foundation for scene construction. Event archipelago and island are defined in this paper. Problem of event arc...

Chance discovery is a new research topic on cognition psychology inspired deep data analysis. Scene is the contact point of human process and computer process in the double helical model of chance discovery. Scene aggregation and binary scene aggregation problem are defined in this paper. Binary scene aggregation problem is a NP hard problem in cha...

Clustering aggregation problem is a kind of formal description for clustering ensemble problem and technologies for the solving of clustering aggregation problem can be used to construct clustering division with better clustering performance when the clustering performances of each original clustering division are fluctuant or weak. In this paper,...

Correlation clustering problem is a NP hard problem and an approach based on self organizing feature map (SOM) neural network for correlation clustering is presented in this paper. Clustering performance of SOM neural network based correlation clustering is test with 20 newsgroups data in UCI KDD archive. Experimental results show that the performa...

With transaction data in the stock market, prediction methods based on neural network for the highest price of one stock in every daily trading day and per minute price of one stock in a daily trading day are explored in this paper. BP neural network and RBF neural network are used in the implementation of predictors. To normalize input/out data wi...

Correlation clustering problem is a NP hard problem and technologies for the solving of correlation clustering problem can be used to cluster given data set with relation matrix for data in the given data set. In this paper, an approach based on genetic algorithm for correlation clustering problem, named as GeneticCC, is presented. To estimate the...

TextCC can classify real documents instantly by cosine similarity. In this paper, stereographic projection is defined from n dimensional real space to the surface of the unit sphere in (n+1) dimensional space. This paper also proposes the relation between the Euclidean distance in n dimensional space and the cosine similarity in (n+1) dimensional r...

Asian options are the popular second generation derivative products and embedded in many structured notes to enhance upside performance. The embedded options, as a result, usually have a long duration. The movement of interest rates becomes more important in pricing such long-dated options. In this paper, the pricing of Asian options under stochast...

A feed forward neural network (FFNN) for the corner classification 4 (CC4) can instantaneously classify text data. With the definition of the deriving vector for a binary vector, the deriving vector based analyses for constructions of weight matrixes of the CC4 hidden and output layer are given. The principle for outputs of the CC4 hidden layer, an...

To implement personalization recommendation of text information retrieval (TR) based in user-focus, TextCC, a feed forward neural network for instantly classification is adopted to classify query results according to user-focuses of a user in the paper. Experimental results show that the implementation of personalization recommendation with TextCC...

Corner classification (CC) network is a kind of feed forward neural network for instantly document classification. To classify text object instantly, new training algorithm, named as TextCC, for feed forward neural network is presented in this paper. To give a solution for multi-corner judging, new training algorithm for the construction of weight...

Corner classification (CC) is a kind of algorithms for instantly classification. The feed forward neural network trained by CC algorithm can be used validly by information retrieval, especially online information retrieval. CC4 is the fourth version of CC. In this paper, the generalized distance is defined according to the construction of weight ma...

Motivated by the reset option with n predetermined dates analyzed by S. Zhang and W.-Y. Cheng [J. Derivatives 2000, Fall, 59–71(2000)], we consider a kind of reset option with uncertain dates by introducing N pre-specified barrier levels. We claim this reset option consists of some standard knock-in and knock-out barrier options. The closed-form pr...

The authors study a new type of path-dependent option called a reset option. On each of a set of prespecified dates, the strike price of a reset option will be reset to the prevailing stock price if the option is out of the money. Its relationships with other exotic options are examined. A closed-form pricing formula in terms of the multivariate no...

It is shown that in a market modeled by a vector-valued semimartingale, when we choose the wealth process of an admissible self-financing strategy as a numeraire such that the historical probability measure becomes a martingale measure, then this numeraire must be the wealth process of a growth optimal portfolio. As applications of this result, the...

In this paper, we study the pricing problem of a European call option written on a corporate bond and obtain a relationship between the price of such an option and that of a class option written on a risk-free bond.

We study a new class of backward stochastic differential equations, which involves the integral with respect to a continuous increasing process. This allows us to give a probabilistic formula for solutions of semilinear partial differential equations with Neumann boundary condition, where the boundary condition itself is nonlinear. We consider both...

The problem of hedging contingent claims by portfolios constrained to take values in a given convex, closed subset of ℛ m in an incomplete market is studied. By extending the pair of portfolio and consumption to a triple of portfolio, extra-investment and consumption, an interval of arbitrage-free prices of a given contingent claim is obtained.

In this paper, we study the convergence rates of the projection residue of U-statistics. When the kernel function is bounded or is exponentially bounded, some exponential convergence rates are obtained which are better than Hoeffding’s and Berk’s. Finally, the problem of the change-point of scale-parameter is studied by the above results.