中国股市量价关系分析中的后验分布构造与模拟

研究中国股票市场中的两个重要指标:股票价格与交易量,随机波动模型具有长期波动性预测能力,只是由于参数估计的困难而没有受到重视.随着马尔可夫链蒙特卡罗(MCMC)方法和计算机计算能力的提高,这种困难是可以克服的.采用基于马尔可夫链蒙特卡罗(MCMC)模拟技术的贝叶斯估计方法,在基于引入预期交易量和非预期交易量的随机波动模型下,对模型参数进行后验分布的构造,并以2005年1月-2012年5月的上证综合指数的每日收盘指数及相应的日成交量序列为样本,通过实证仿真得到参数结果值.结果表明,非预期交易量对股市价格的影响要大于预期交易量.     

Adaptive Mixture Modeling Metropolis Methods for Bayesian Analysis of Nonlinear State-Space Models

We describe a strategy for Markov chain Monte Carlo analysis of nonlinear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis–Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the nonlinearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.     

银行信用风险演变的KMV模型分析—以五家中小商业银行为例

利用KMV模型方法,借助预期违约概率（EDF）和违约距离（DD）两个指标分析在我国A股上市的五家中小商业银行的信用风险。着重分析其预期违约概率的变化以及违约距离对股票价格、无风险利率、股权价值波动率等参数的敏感性。结果表明：五家商业银行在2008年前10个月的EDF上升明显,2008年11月EDF开始明显回落。从宁波银行的个案来看,违约距离对无风险利率的敏感性较弱、对股价在较低价位时的敏感性较强,而在较高价位时敏感性较弱,对股权价值波动率的敏感性较强。从违约距离对各参数的敏感性分析结论出发,阐述了稳定并提振我国A股股市的重要性。     

A micro-movement model with Bayes estimation via filtering: Application to measuring trading noises and costs

We highlight a general hybrid system as the micromovement model for asset price using counting processes recently introduced with its Bayes estimation via filtering. We construct a new simple micromovement model and apply it to analyze trade-by-trade stock price data in the light of the series of works initiated by Christie and Schultz [Why do NASDAQ market makers avoid odd-eighth quotes?, Finance 49 (1994) 1813–1840]. Through the new model, we propose more reasonable, but computationally intensive measures for trading noise including clustering noise and non-clustering noise, and for trading cost. We employ Bayes estimation via filtering to obtain parameter estimates of the new model and to provide numerical measures of trading noise and trading cost for three stocks from four chosen periods. Our empirical results support the important findings in [Christie, Harris, Schultz, Why did NASDAQ market makers stop avoiding odd-eighth quotes?, Finance 49 (1994) 1841–1860; Barclay, Christie, Harris, Kandel, Schultz, The effects of market reform on the trading costs and depths of NASDAQ stocks, J. Finance 54(1) (1999) 1–34].     

Stochastic equity volatility related to the leverage effect II: valuation of European equity options and warrants

We propose a general framework to assess the value of the financial claims issued by the firm, European equity options and warrantsin terms of the stock price. In our framework, the firm's asset is assumed to follow a standard stationary lognormal process with constant volatility. However, it is not the case for equity volatility. The stochastic nature of equity volatility is endogenous, and comes from the impact of a change in the value of the firm's assets on the financial leverage. In a previous paper we studied the stochastic process for equity volatility, and proposed analytic approximations for different capital structures. In this companion paper we derive analytic approximations for the value of European equity options and warrants for a firm financed by equity, debt and warrants. We first present the basic model, which is an extension of the Black-Scholes model, to value corporate securities either as a function of the stock price, or as a function of the firm's total assets. Since stock prices are observable, then for practical purposes, traders prefer to use the stock as the underlying instrument, we concentrate on valuation models in terms of the stock price. Second, we derive an exact solution for the valuation in terms of the stock price of (i) a European call option on the stock of a levered firm, i.e. a European compound call option on the total assets of the firm, (ii) an equity warrant for an all-equity firm, and (iii) an equity warrant for a firm financed by equity and debt. Unfortunately, to compute these solutions we need to specify the function of the stock price in terms of the firm's assets value. In general we are unable to specify this expression, but we propose tight bounds for the value of these options which can be easily computed as a function of the stock price. Our results provide useful extensions of the Black-Scholes model.     

股票价格遵循几何分式Brown运动的期权定价

讨论了股票价格过程遵循几何分式B row n运动的欧式期权定价.由于该过程存在套利机会使得传统的期权定价方法(如资本资产定价模型(CAPM),套利定价模型(APT),动态均衡定价理论(DEPT))不可能对该期权定价.利用保险精算定价法,在对市场无其它任何假设条件下,获得了欧式期权的定价公式.并讨论了在有效期内股票支付已知红利和红利率的推广公式.     

股本规模、涨跌幅限制与触限频率的实证研究

本文运用广义矩估计(GMM)方法,对沪深上市公司价格触限频率与股本规模之间的关系进行回归分析。结果表明:流通股本规模越小的公司,其价格触限频率更高,而中小企业板股票的价格触限频率高于主板股票近两个百分点。研究说明中小企业板的涨跌停幅度应大于主板幅度,以更好地体现价格限制制度的作用。     

On optimal proportional reinsurance and investment in a hidden Markov financial market

This paper investigates the optimal reinsurance and investment in a hidden Markov financial market consisting of non-risky (bond) and risky (stock) asset. We assume that only the price of the risky asset can be observed from the financial market. Suppose that the insurance company can adopt proportional reinsurance and investment in the hidden Markov financial market to reduce risk or increase profit. Our objective is to maximize the expected exponential utility of the terminal wealth of the surplus of the insurance company. By using the filtering theory, we establish the separation principle and reduce the problem to the complete information case. With the help of Girsanov change of measure and the dynamic programming approach, we characterize the value function as the unique solution of a linear parabolic partial differential equation and obtain the Feynman-Kac representation of the value function.     

Minimax estimation with random coefficients: Theory and application to stock returns

Testing the reliability of the capital asset pricing model (CAPM) for various stock market returns is an important task in capital market research. In all previous studies, a common feature consists in the application of ordinary least squares or Bayesian methods when it comes to estimation of parameters. The Bayesian approach seems to be fairly intractable by practitioners whereas the OLS approach often yields imprecise and thus doubtful results. In this paper, the CAPM is estimated by approximate minimax techniques extended to a random coefficient regression model (RCR). The method turns out to be efficient from both the economical and computational point of view.     

Sensitivity Analysis of Asset Flow Differential Equations and Volatility Comparison of Two Related Variables

In this study, I apply forward sensitivity analysis to the dynamical system of nonlinear asset flow differential equations (AFDEs). I find that all parameters in AFDEs are needed and can be estimated from market prices and net asset values data. Moreover, the market price is the most fluctuating state variable, and the coefficient for the trend-based investor' sentiment is the dominant parameter. Furthermore, I define and compare the extreme value-based volatilities of market price and net asset value for closed-end funds. I find that the extreme value-based volatility of market price is higher than that of net asset value for the vast majority of closed-end funds for both overlapping and non-overlapping cases.     

基于区制转换的股市泡沫动态监测模型

提出了两阶段区制转换模型,考虑了在两个时区之间的随机转换和退出泡沫时期的概率,使用序贯贝叶斯学习方法实时估计模型状态和参数。模拟试验表明,相比现有的其他方法,两阶段区制转换模型对于检测泡沫来说具有更好的效力。实证检验证实了模型的优势,并且得到一个重要的结论是2008年之后的A股市场的泡沫化程度较低。本文模型可以为机构投资者和监管部门动态监测股票市场可能存在的价格泡沫现象提供一些启示。     

The effects of asset liquidity on dynamic sell-out and bankruptcy decisions

We develop a dynamic bankruptcy model with asset illiquidity. In the model, a distressed firm chooses between sell-out and default, as well as its timing under the assumption that sell-out is feasible only at Poisson jump times, where the arrival rate of acquirers stands for asset liquidity. With lower asset liquidity, the firm increases the sell-out region to mitigate the risk of not finding an acquirer until bankruptcy. Despite the larger sell-out region, lower asset liquidity increases the default probability and decreases the equity, debt, and firm values. In the optimal capital structure, with lower asset liquidity, the firm reduces leverage, but the cautious capital structure does not fully offset the increased default risk. The stock price reaction caused by sell-out depends on the sell-out timing. When the firm’s asset value is not sufficiently high, the stock price jump size is an inverted U-shape with the economic state variable. Lower asset liquidity increases the jump size due to greater surprise. These results fit empirical observations.     

基于遗传算法和半参数回归的神经网络集成股市预测研究

股票时间序列预测在经济和管理领域具有重要的应用前景,也是很多商业和金融机构成功的基础.首先利用奇异谱分析对股市时间序列重构,降低噪声并提取趋势序列.再利用C-C算法确定股市时间序列的嵌入维数和延迟阶数,对股市时间序列进行相空间重构,生成神经网络的学习矩阵.进一步利用Boosting技术和不同的神经网络模型,生成神经网络集成个体.最后采用带有惩罚项的半参数回归模型进行集成,并利用遗传算法选择最优的光滑参数,以此建立遗传算法和半参数回归的神经网络集成股市预测模型.通过上证指数开盘价进行实例分析,与传统的时间序列分析和其他集成方法对比,发现该方法能获得更准确的预测结果.计算结果表明该方法能充分反映股票价格时间序列趋势,为金融时间序列预测提供一个有效方法.     

次分数布朗运动下可分离交易可转债的定价及其风险参数

科学合理的定价是可分离交易可转债交易的基础.考虑到金融资产价格序列的长记忆性,应用次分数布朗运动的It?公式和无风险套利原理,建立标的资产支付连续红利且资产价格遵循几何次分数布朗运动的可分离交易可转债定价模型.并利用Mellin变换求解得到定价模型的解析解.最后,分析几个风险参数对可分离交易可转债价值的影响,并通过数值...     

基于MCMC模拟的贝叶斯厚尾金融随机波动模型分析

针对现有金融时间序列模型建模方法难以刻画模型参数的渐变性问题,利用贝叶斯分析方法构建贝叶斯厚尾SV模型。首先对反映波动性特征的厚尾金融随机波动模型(SV-T)进行贝叶斯分析,构造了基于Gibbs抽样的MCMC数值计算过程进行仿真分析,并利用DIC准则对SV-N模型和SV-T模型进行优劣比较。研究结果表明:在模拟我国股市的波动性方面,SV-T模型比SV-N模型更优,更能反应我国股市的尖峰厚尾的特性,并且证明了我国股市具有很强的波动持续性。     

Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models

We study the class of state-space models and perform maximum likelihood estimation for the model parameters. We consider a stochastic approximation expectation–maximization (SAEM) algorithm to maximize the likelihood function with the novelty of using approximate Bayesian computation (ABC) within SAEM. The task is to provide each iteration of SAEM with a filtered state of the system, and this is achieved using an ABC sampler for the hidden state, based on sequential Monte Carlo methodology. It is shown that the resulting SAEM-ABC algorithm can be calibrated to return accurate inference, and in some situations it can outperform a version of SAEM incorporating the bootstrap filter. Two simulation studies are presented, first a nonlinear Gaussian state-space model then a state-space model having dynamics expressed by a stochastic differential equation. Comparisons with iterated filtering for maximum likelihood inference, and Gibbs sampling and particle marginal methods for Bayesian inference are presented.     

Parameter estimation of an agent‐based stock price model

The influence of the behavior and strategies of traders on stock price formation has attracted much interest. It is assumed that there is a positive correlation between the total net demand and the price change. A buy order is expected to increase the price, whereas a sell order is assumed to decrease it. We perform data analysis based on a recently proposed stochastic model for stock prices. The model involves long‐range dependence, self‐similarity, and no arbitrage principle, as observed in real data. The arrival times of orders, their quantity, and their duration are created by a Poisson random measure. The aggregation of the effect of all orders based on these parameters yields the log‐price process. By scaling the parameters, a fractional Brownian motion or a stable Levy process can be obtained in the limit. In this paper, our aim is twofold; first, to devise statistical methodology to estimate the model parameters with an application on high‐frequency price data, and second, to validate the model by simulations with the estimated parameters. We find that the statistical properties of agent level behavior are reflected on the stock price, and can affect the entire process. Moreover, the price model is suitable for prediction through simulations when the parameters are estimated from real data. The methods developed in the present paper can be applied to frequently traded stocks in general. Copyright © 2013 John Wiley & Sons, Ltd.     

股价指数时间序列的分形性质分析

用一种新的信号处理工具-小波变换,对股价指数数据进行分析,发现股价指数数据类似于一类更广的噪声一分形噪声,从而推广了传统上处理股价指数时间序列时总假定其为白噪声或高斯噪声的假设,用分形噪声能更好地刻划股价指数数据的波动特性.对上证指数和深证指数的实证分析显示,两市股价指数均存在正相关,我国股票市场不是弱式有效市场.实证也显示出小波变换是研究股价指数波动特性的一种有效的方法.     

EMD结合RBF神经网络新混合模型及股指期货价格预测

中国近4年才成立的股指期货市场价格呈现出非平稳、非线性的信号特征,传统的预测方法无法对长相关序列进行精确预测.将EMD与RBF相结合,建立了一种新的预测方法对我国股指期货日结算价格进行预测.结果显示本模型将原本具有长相关性质的原始序列分解为若干个短相关性质的不同频带,解决了原始序列随机性强,以及因相邻频带的干扰而造成的系统动力信息反映不足的缺陷;并与其他预测模型进行比较,显示出较高的预测精度.     

基于国际估值准则的A股定价实证分析

尽管金融学理论认为股票的价值是未来无限期预期现金流的一个贴现,但针对国际股市的实证结果显示,股票实际价格很大程度上取决于市场对其未来一两年内的盈利预期等中期基本面因素.近3年里,A股创造的世界罕见的大起大落,引起了社会各界对A股定价是否合理的广泛争论.为此,我们设计了一个相对估值模型,根据国际股市的定价规则来给A股定价.实证结果显示,A股已经从2007年的估值泡沫回落到2008年的合理估值水平.还可以经过进一步地扩展模型讨论单个股票的定价问题.最后,讨论了实证结果的引申含义和一些政策建议.