EZ模型中的有限尺寸效应

研究EZ模型中的有限尺寸效应.当经纪人数目N足够大及发生交易的概率a1/N，发现有限尺 寸效应是重要的.此时，系统几乎变成包含所有经纪人的单一集团.而对较小集团，尺寸分布 仍然服从幂函数律，但是指数因涨落效应而改变.但当a1/N时，可以论证涨落效应不重要 ，因而平均场理论是严格成立的. 关键词： EZ模型 有限尺寸效应 涨落 平均场理论     

一混沌模型的系综模拟研究

本文用随机模拟方法研究了一化学混沌模型的介观动力学。对该混沌模型的系综模拟发现 ,在这种不稳定运动中存在强烈的内部涨落 ,然而由于混沌运动整体上的稳定性 ,使得系统中的代表点被限制在混沌吸引子上 ,并且每个代表点形成的随机轨道很好地保持了确定性混沌吸引子的基本特性     

量子气体中的输运行为

输运测量是了解物质性质的一个重要手段.本文简单介绍最近在量子气体中实现的输运实验及其主要结论,包括在类似于介观物理器件中的Landauer输运和强相互作用费米气体中的自旋输运行为.我们着重讨论自旋动力学的特殊性以及其由于全同粒子相互作用所导致的特殊自旋扩散流的形式.     

红外辐射场理论模拟中的普适性模型研究

在现有海面目标红外辐射场理论建模工作的基础上建立了具有普适性意义的海面目标红外辐射场理论建模通用框架,重点对建立目标的几何构型、适用于各种微元的物理模型、及通用的三维数据场可视化这三个问题进行了叙述。所得结果不仅对海面各种目标红外辐射场的计算机模拟研究具有普遍的意义,且对空中、陆地及其他场景中目标的红外辐射场理论建模研究也有一定的借鉴作用。     

体温计模型的临界行为

提出一个可用离散朗之万方程描述的体温计模型.该体温计的特点是其温度示数只能随外界温度的升高而上升,当外界温度降低时,其示数却不能下降.根据体温计示数这种只升不降的特点,定义了“停顿”事件.用随机行走的理论解析地推导了停顿时间分布函数数值模拟和解析结果都显示这种分布函数呈幂律形式D(s)∝s^-ξ,揭示出在这一过程中所表现出的临界性. 关键词：     

La0.7-xDyxSr0.3MnO3中的相分离和输运行为

通过测量样品的M-T曲线、 M-H曲线、 ESR曲线、ρ-T曲线和MR-T曲线, 研究了Dy掺杂(0.0≤x≤0.30)对La0.7Sr0.3MnO3磁电性质的影响. 实验结果表明 在TC处所有样品都经历了顺磁到铁磁的转变; 当T＜TC时, 掺杂样品进入自旋团簇玻璃态, 低温时显示出反铁磁性; x=0.20时, 样品在TC以上温区发生相分离; Dy掺杂引起的磁结构变化将导致CMR效应.     

推广的磁通活脱钉模型（EMTAD）中的中值能量问题

本文详细论证了推广的磁通热激活脱钉模型与一维周期势中布朗运动稳态解的一致性地利用中值定理引入截止能量ＥＣ可能带来的误差作了估计。     

有限维希尔伯空间谐振子的振幅消相干及其推广

为了研究二态体系和谐振子体系在振幅消相干方面的联系,我们研究有限维希尔伯空间谐振子的振幅消相干性质,然后推广到能谱等间隔体系,发现这些体系虽然在某些方面差别很大,但是由于体系的能谱等间隔这个特点,使得它们的振幅消相干性质完全类似.     

等同双原子Jaynes-Cummings模型的量子特性及系统光谱

在全量子理论的背景下提出两个二能级原子分别与一单模腔场相互作用的系统模型,利用量子主方程和数值模拟计算等方法,研究该体系中腔场平均光子数、Mandel's Q因子及二阶量子相关度在非稳态时的变化规律。此外,对体系中原子及腔场中光谱结构进行了分析。结果表明:减小腔场耗散系数,增大原子间耦合系数,体系量子特性愈加明显。体系光谱呈现出Mollow三重峰结构,且原子辐射谱强度远大于腔场辐射谱强度。当原子跃迁频率与腔场跃迁频率为近共振时,Mollow峰值为三峰中最大值。此外,增大原子与腔场间耦合系数,可增大原子光谱的中峰强度;而增大腔场光谱的中峰强度,则需减小原子与腔场间耦合系数。     

金融物理的若干基本问题与研究进展(Ⅰ)--价格的统计分析与价格涨落的随机过程模型

金融物理学是物理学概念和方法应用于金融分析的一门新的交叉学科，近年来受到人们的广泛关注．文章简述了金融物理的研究方向和研究方法，重点讨论了价格涨落的统计分析和相关的物理模型．     

The timing of information transmission in financial markets

This article shows turbulent behavior in a series of financial indexes assuming that they follow a cascade process of the same type as do turbulent fluids. With such a model, the energy flux between the eddies that emerge in the fluid is analogous to the financial information flux over the course of time. The results obtained confirm the variability of variation of the indexes for the considered time scale (the turbulent intermittency typical for fluids), and they also confirm that when we descend along the cascade, that is to say, when we consider smaller time intervals, the rate at which the hypothetical eddies of information dissipate becomes greater than the rate at which the information is transmitted. This fact can explain the cyclical nature of crises: ultimately, financial events have a memory of the past. Besides, the NASDAQ singular behavior regarding the number of jumps, the degree of intermittency of the turbulence and the life time of the hypothetical eddies has been analysed.     

Self-organized model for information spread in financial markets

A self-organized model with social percolation process is proposed to describe the propagations of information for different trading ways across a social system and the automatic formation of various groups within market traders. Based on the market structure of this model, some stylized observations of real market can be reproduced, including the slow decay of volatility correlations, and the fat tail distribution of price returns which is found to cross over to an exponential-type asymptotic decay in different dimensional systems. Received 15 March 2000     

A generalized spin model of financial markets

We reformulate the Cont-Bouchaud model of financial markets in terms of classical “super-spins” where the spin value is a measure of the number of individual traders represented by a portfolio manager of an investment agency. We then extend this simplified model by switching on interactions among the super-spins to model the tendency of agencies getting influenced by the opinion of other managers. We also introduce a fictitious temperature (to model other random influences), and time-dependent local fields to model a slowly changing optimistic or pessimistic bias of traders. We point out close similarities between the price variations in our model with N super-spins and total displacements in an N-step Levy flight. We demonstrate the phenomena of natural and artificially created bubbles and subsequent crashes as well as the occurrence of “fat tails” in the distributions of stock price variations. Received 13 October 1998     

Self-organizing Ising model of financial markets

We study a dynamical Ising-like model of agents' opinions (buy or sell) with learning, in which the coupling coefficients are re-assessed continuously in time according to how past external news (time-varying magnetic field) have explained realized market returns. By combining herding, the impact of external news and private information, we find that the stylized facts of financial markets are reproduced only when agents misattribute the success of news to predict return to herding effects, thereby providing positive feedbacks leading to the model functioning close to the Ising critical point.     

The endogenous dynamics of financial markets: Interaction and information dissemination

We investigate the process that different interactions between investors will prompt information to propagate along a differentiated path and construct a financial market model. As information spreads, increasingly investors are attracted to participate in trading, then the “herding effect” is magnified gradually, which will induce the topology of market network to change and the price to fluctuate. Especially, under different initial conditions or parameters, the peak and fat-tail property is produced and the obtained statistic values coincide with empirical results: the power-law exponents between the peak value of return probability distribution and the time scales range from 0.579 to 0.747, and the exponents between the accumulation distribution and the return on the tail are close to 3. Besides, the extent of volatility clustering in our produced price series is close to that of S&P 500 and locates between NASDAQ and HSI. All the results obtained here indicate that the continuous variation of the “herding effect” resulting from information propagation among interacting investors may be the origin of stylized facts of price fluctuations.     

Minimal agent based model for financial markets I

We introduce a minimal agent based model for financial markets to understand the nature and self-organization of the stylized facts. The model is minimal in the sense that we try to identify the essential ingredients to reproduce the most important deviations of price time series from a random walk behavior. We focus on four essential ingredients: fundamentalist agents which tend to stabilize the market; chartist agents which induce destabilization; analysis of price behavior for the two strategies; herding behavior which governs the possibility of changing strategy. Bubbles and crashes correspond to situations dominated by chartists, while fundamentalists provide a long time stability (on average). The stylized facts are shown to correspond to an intermittent behavior which occurs only for a finite value of the number of agents N. Therefore they correspond to finite size effects which, however, can occur at different time scales. We propose a new mechanism for the self-organization of this state which is linked to the existence of a threshold for the agents to be active or not active. The feedback between price fluctuations and number of active agents represents a crucial element for this state of self-organized intermittency. The model can be easily generalized to consider more realistic variants.     

Modeling and asset allocation for financial markets based on a discrete time microstructure model

On the basis of the market microstructure theory and the continuous time stochastic volatility-style microstructure model, a discrete time stochastic volatility microstructure model with state-observability is proposed for describing the dynamics of financial markets. From the discrete time microstructure model proposed, estimates of two immeasurable state variables representing the market excess demand and liquidity respectively may be obtained. A simple trading strategy for dynamic asset allocation, based on the indirectly obtained excess demand information instead of the prediction for price, is presented. An approach to the estimation of the discrete time microstructure model using the extended Kalman filter and the maximum likelihood method is also presented. Case studies on financial market modeling and the estimated model-based asset dynamic allocation control for the JPY/USD (Japanese Yen/US Dollar) exchange rate and Japan TOPIX (TOkyo stock Price IndeX) show satisfactory modeling precision and control performance. Received 11 March 2002 / Received in final form 4 November 2002 Published online 4 February 2003 RID="a" ID="a"Currently a visiting researcher at the Institute of Statistical Mathematics, 4-6-7 Minami Azabu, Minato-ku, Tokyo 106-8569, Japan e-mail: peng@ism.ac.jp     

Geometry of financial markets—Towards information theory model of markets

Most parameters used to describe states and dynamics of financial market depend on proportions of the appropriate variables rather than on their actual values. Therefore, projective geometry seems to be the correct language to describe the theater of financial activities. We suppose that the objects of interest of agents, called here baskets, form a vector space over the reals. A portfolio is defined as an equivalence class of baskets containing assets in the same proportions. Therefore portfolios form a projective space. Cross ratios, being invariants of projective maps, form key structures in the proposed model. Quotation with respect to an asset Ξ (i.e. in units of Ξ) is given by linear maps. Among various types of metrics that have financial interpretation, the min-max metric on the space of quotations can be introduced. This metric has an interesting interpretation in terms of rates of return. It can be generalized so that to incorporate a new numerical parameter (called temperature) that describes agent's lack of knowledge about the state of the market. In a dual way, a metric on the space of market quotation is defined. In addition, one can define an interesting metric structure on the space of portfolios/quotation that is invariant with respect to hyperbolic (Lorentz) symmetries of the space of portfolios. The introduced formalism opens new interesting and possibly fruitful fields of research.     

Asymmetric and symmetric meta-correlations in financial markets

In financial markets, the relation between fluctuations of stock prices and trading behaviors is complex. It is intriguing to quantify this kind of meta-correlation between market fluctuations and the synchronous behaviors. We refine the theoretical index leverage model proposed by Reigneron et al., to exactly quantify the meta-correlation under various levels of price fluctuations [Reigneron P A, Allez R and Bouchaud J P 2011 Physica A 390 3026]. The characteristics of meta-correlations in times of market losses, are found to be significantly different in Chinese and American financial markets. In addition,unlike the asymmetric results at the daily scale, the correlation behaviors are found to be symmetric at the high-frequency scale.     

Micro and macro benefits of random investments in financial markets

In this paper, making use of recent statistical physics techniques and models, we address the specific role of randomness in financial markets, both at the micro and the macro level. In particular, we review some recent results obtained about the effectiveness of random strategies of investment, compared with some of the most used trading strategies for forecasting the behaviour of real financial indexes. We also push forward our analysis by means of a self-organised criticality model, able to simulate financial avalanches in trading communities with different network topologies, where a Pareto-like power law behaviour of wealth spontaneously emerges. In this context, we present new findings and suggestions for policies based on the effects that random strategies can have in terms of reduction of dangerous financial extreme events, i.e. bubbles and crashes.