隐Markov链驱动关联性和波动性的传染分析

本文将隐Markov链对波动性和相关性的驱动分析引入DCC多元GARCH,对波动和相关分析建立起了直接的联系,进而考察次贷危机、欧洲债务危机在主要证券市场间的传染性。研究发现,高波动高相关机制为联动性提供了一种直接的表述方式,且这一机制在危机期间处于支配地位;次贷危机、欧洲债务危机具有传染性,传染期以区间的形式出现,且危机初期的市场在各机制间有较为频繁的转换,不可根据危机事件对样本进行武断地分割;同时,危机的传染在所考察的市场之间具有系统性,应对危机需要各国政策间的协调配合;另外,有证据显示美国次贷市场在2006年年中已显现出问题,有关国家贻误了深入分析和应对危机的时机。     

我国蔬菜价格调控机制研究综述

针对近年来我国蔬菜价格经历了"过山车"式的波动,从价格调控的目标、手段、波动特点、波动影响因素和蔬菜调控机制等方面,对现有文献进行梳理和评述。认为以下问题有待进一步研究:要明确蔬菜价格调控机制的目的,区分众多影响我国蔬菜价格上涨与波动因素中的合理成分和不合理成分;必须从时间维度区分影响蔬菜价格波动的诸因素及其影响力度,并加强对现有政策的绩效评价,有针对性地提出蔬菜价格调控的长期性政策、短期性政策,选择合适的政策手段和正确的介入时机,避免短期政策的长期化;必须考虑蔬菜产业链中价格波动的传导机制,理顺农资供应、生产、加工、储运、销售等环节中各个主体的行为模式及其收益分配机制。     

蔬菜价格时间序列的分解与分析——基于北京市2002-2012年数据

基于北京市2002-2012年的蔬菜价格数据,采用时间序列分解和H-P滤波技术,将蔬菜价格分解为季节性波动、随机性波动、周期性波动和长期趋势4部分,并测算各波动成分对蔬菜价格波动的贡献率。结果发现,季节性因子特征显著,是影响蔬菜价格变化的最主要因素,平均贡献率为62.3%;2002-2012年划分为6个周期,周期的时间跨度和波动幅度均有变大的趋势,周期性波动对蔬菜价格变化的贡献率为23.6%;突发和异常事件是引起蔬菜价格随机性波动的主要诱因,随机性波动对蔬菜价格变化的贡献率为14.1%;蔬菜价格长期趋势由持续上行转为波动上行,可分为升幅较缓、升速加快和波动上行3个阶段。〖JP2〗基于此,提出如下政策建议：完善蔬菜产业链条监测,强化信息流对物流引导作用的发挥;开展蔬菜市场预警建设,降低突发事件引起的随机性波动对市场的影响;自产蔬菜供应采取“见缝插针”的策略,在突发事件时起到平抑市场价格波动的作用等。     

国内外油脂期货市场溢出效应及信息传导关系

采用多元 VAR（1）-GARCH（1,1）-BEKK 模型研究了中国的棕榈油、豆油和菜籽油以及美国豆油、加拿大油菜籽五个期货市场之间的收益和波动溢出效应,进而通过协整检验和误差修正模型分析了市场间的信息传导关系。研究发现：美国豆油和加拿大油菜籽对中国三个油脂期货市场均有单向的收益溢出,中国的三大油脂期货表现为豆油对棕榈油、棕榈油对菜籽油存在单向的收益溢出;除了中国的豆油、棕榈油对菜籽油期货市场存在单向的波动溢出外,其他市场间均存在双向的波动溢出效应;五个市场间存在稳定的协整关系并具有相同的信息传导效率。上述研究结果表明美国芝加哥豆油期货市场和加拿大油菜籽期货市场分别是世界豆油和菜籽油的定价中心,大连期货市场是国内的油脂产品定价中心。     

中国货币政策与股票市场溢出效应研究——基于VAR-GARCH-BEKK模型

在对中国货币市场利率、汇率和股票市场进行总体经验分解的基础上,运用VAR-GARCH-BEKK模型对实证分析货币政策与股票市场的联动性进行实证研究。研究表明:利率与股市间存在双向溢出效应,汇率和股市、利率和汇率间存在非对称的溢出效应,市场间存在显著的传导性,影响的方向和力度有所差异,从长周期趋势来看,汇率相较利率对股票市场波动溢出影响更具持久性。应密切关注市场波动,避免金融市场风险影响宏观经济运行,在有效监管框架下货币政策应着眼长期趋势,甄别市场波动力度与持久性,适时适度地完成货币政策调控。     

券商业绩起伏与沪深股市波动的关系探析——基于2009-2013年面板数据的实证研究

为了探究券商业绩与股市波动的关系,选取不同区域的10家已上市券商为样本,采取2009—2013年的数据,通过Stata12软件采用多元经典回归方法对此进行实证研究。研究结果显示:券商的业绩明显正向地影响到沪深股市的量价波动;股市波动十分显著地影响到券商的业绩起伏,沪深股市涨幅对券商总资产增长率、经纪业务利润都有显著的正向影响,成交金额增长有利于券商利润提高,而沪深股市振幅则对券商业绩有一定程度的负面影响。券商需注重差异化竞争,及时调整自营业务的总量与结构,彻底完成集中交易模式以提高经纪业务的毛利率。     

Stochastic volatility models for exchange rates and their estimation using quasi-maximum-likelihood methods: an application to the South African Rand

This paper is concerned with the volatility modeling of a set of South African Rand (ZAR) exchange rates. We investigate the quasi-maximum-likelihood (QML) estimator based on the Kalman filter and explore how well a choice of stochastic volatility (SV) models fits the data. We note that a data set from a developing country is used. The main results are: (1) the SV model parameter estimates are in line with those reported from the analysis of high-frequency data for developed countries; (2) the SV models we considered, along with their corresponding QML estimators, fit the data well; (3) using the range return instead of the absolute return as a volatility proxy produces QML estimates that are both less biased and less variable; (4) although the log range of the ZAR exchange rates has a distribution that is quite far from normal, the corresponding QML estimator has a superior performance when compared with the log absolute return.     

Volatility model selection for extremes of financial time series

Although both widely used in the financial industry, there is quite often very little justification why GARCH or stochastic volatility is preferred over the other in practice. Most of the relevant literature focuses on the comparison of the fit of various volatility models to a particular data set, which sometimes may be inconclusive due to the statistical similarities of both processes. With an ever growing interest among the financial industry in the risk of extreme price movements, it is natural to consider the selection between both models from an extreme value perspective. By studying the dependence structure of the extreme values of a given series, we are able to clearly distinguish GARCH and stochastic volatility models and to test statistically which one better captures the observed tail behaviour. We illustrate the performance of the method using some stock market returns and find that different volatility models may give a better fit to the upper or lower tails.     

Bayesian Analysis of Stochastic Volatility Models

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of our Markov-chain method. In addition, multistep-ahead predictive densities can be constructed that reflect both inherent model variability and parameter uncertainty. We illustrate our method by analyzing both daily and weekly data on stock returns and exchange rates. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches.     

Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications that are natural extensions to certain existing models, one of which allows for time-varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time-varying correlations, heavy-tailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the best specifications are those that allow for time-varying correlation coefficients.