随机波动率LIBOR市场利率动态模型的理论估计与蒙特卡罗模拟

本文首先基于诸多Libor市场模型改进方法的基础之上,在标准市场模型中加入Heston随机波动率过程,建立随机波动率假设的新型Libor市场模型;其次,运用Black逆推参数校正方法和MCMC参数估计方法对该Libor利率市场模型中的局部波动率和随机波动率过程中的参数进行校正和估计;最后是实证模拟。研究结论认为,在构建Libor利率动态模型时,若在单因子Libor利率市场模型基础上引入随机波动率过程,可大大提高利率模型的解释力。     

不确定性冲击下利率传导机制

本文考察了货币政策不确定性冲击下市场利率之间的传导机制。首先,应用时变系数马尔科夫区制转移模型提取代表货币政策不确定性的利率不确定性。其次,采用Dirichlet VAR模型考察了这种不确定性冲击下政策利率和市场利率之间的传导关系。最后,结合二元和多元变量之间的影响关系,给出了不确定性冲击下利率的核心传导结构。实证结果表明：不考虑不确定性冲击时,政策利率和货币市场利率之间存在基本的传导环路,货币市场和债券市场之间形成了市场化的传导环路。考虑不确定性冲击时,利率内在不确定性直接影响了股票市场收益率的变动;利率外在不确定性对债券市场和股票市场都会带来影响,进而影响市场上的融资成本。     

香港离岸人民币市场利率与汇率联动效应研究

文章运用两区制门限协整模型对香港离岸市场利率与汇率的联动效应进行实证研究,发现香港离岸人民币市场利率与汇率的联动效应呈现非线性特征;在低利差区制,香港离岸人民币市场可以实现有效的自我调节,而在高利差区制,市场无法进行有效的自我调节,且随着远期汇率期限的增加,市场自我调节能力进一步降低。进一步分析香港离岸人民币市场利率与汇率的联动门限效应的影响因素发现,中美贸易摩擦、"811"汇改、央行货币政策等因素都会对离岸市场的状态造成差异化影响。因此,央行应根据区制的不同采取差异化政策,加快离岸市场基础设施建设,完善跨境资本流动管理和监察体制,推进利率和汇率市场化改革。     

基于银行间国债回购利率时间序列的实证分析

预测市场利率的走势，对于商业银行利率风险管理非常重要。依据国债7天和14天回购利率数据，本文建立了利率预测综合自回归移动平均模型(ARIMA)和误差修正模型(ECM)。模拟结果表明，ARIMA模型不太理想，而ECM模型效果较好。     

单因子利率期限结构模型参数估计的数据选择

本文证明了单因子利率模型参数估计数据选择的相关性原则，并提出另一原则：选择交易最频繁、成交量最大的利率品种。依据这两个原则，R007是瞬时利率rt的最佳近似替代，在使用中国货币市场利率估计单因子利率模型的参数时，应该选择R007作为估计数据。     

中国货币政策绩效研究:2001-2014

本文选取狭义货币供给量、信贷余额、管制利率和市场利率四个指标作为描述中国货币政策数量与价格工具并存的代理变量,以中国宏观经济平稳向好和通货膨胀适宜可控为目标变量,运用随机前沿模型测度中国货币政策绩效,并对引致技术无效性的因素进行探究。实证结果显示:考察期内货币政策工具作用下,中国数量型货币政策绩效逐渐下降,政策绩效边界水平较小,存在较大的改进空间,市场利率的作用在一定程度上阐释了技术无效性。因此,在以存款利率上限为核心的利率双规制下,扭转中国货币政策两难困境时,需考虑利率市场化进程,结合并协调数量型和价格型货币政策。     

基于CVaR的我国银行间债券回购市场利率风险度量研究

在银行间债券回购市场利率基本特征分析基础上，利用我国银行间回购开始日1997年6月15日至2008年4月20日全部质押式回购每周加权平均利率进行实证研究，建立了基于ARMA-GARCH模型族的利率风险CVAR测度模型。结果表明我国银行间债券回购市场中存在杠杆效应；回购利率分布对CVAR计算结果影响较大，GED分布能较正态分布和t分布能更好刻画我国银行间回购利率序列的分布状况；并且在GED分布下，EGARCH模型计算得到的CVAR值要优于GARCH和TARCH模型得到的结果．     

利率在控制房地产投资规模中的作用

根据宏观经济学中最基本IS-LM模型建立了房地产市场的均衡产出和投资模型,并找出利率影响货币政策和均衡投资额的关键因子.在此基础上结合模型中的关键影响因子综合分析造成我国近年来利率对于房地产投资规模调控不力局面的原因.最后结合美国的相关经验,提出对我国改进利率调控机制的建议.     

利率对房价影响的实证分析——以北京市为例

本文从介绍房地产买卖与租赁市场的四象限模型理论分析开始,在构建利率与房价的理论分析框架的基础上结合北京市房地产市场的实际情况从实证角度分析了北京市利率变动与房价波动之间的关系,结果表明利率在一定程度上可以作为解释北京市房价上涨的真实原因,但房价对利率的敏感程度较小,但其影响不容忽视。此外还简单介绍了利率变化、房地产价格与房地产开发面积、竣工面积之间的连带关系。     

加息能改变我国居民的货币需求吗?

一、引言 2005年以来,市场流动性过剩问题日趋突出.央行在此期间多此运用货币政策工具调节当前市场流动性过剩,然而收效甚微.本文运用协整方法和误差修正模型,在分析了我国货币需求关系的基础上,建立了货币需求的误差修正模型,结果表明,长期内,我国货币需求符合传统西方经济的货币需求理论,但是在短期内,利率对货币需求的影响有明显的时滞,利率工具作为控制货币需求量的政策工具要考虑到其作用的滞后性.     

成交量、持仓量对我国大豆类期货价差波动影响的实证分析

采用GARCH(1,1)模型就成交量、持仓量对大豆类期货价差波动率的影响进行实证分析,结果显示：当期成交量、持仓量对大豆期货价差波动的整体影响是显著的;滞后成交量、持仓量对大豆期货价差波动的整体影响也是显著的;当成交量、持仓量同时进入条件方差方程时,它们对大豆类期货价差波动的影响整体上也是显著的。这一结论揭示了我国大豆期货市场信息传递过程,验证了我国大豆期货市场的信息非有效性,对期货市场投资者以及期货市场监管者具有一定的借鉴意义。     

Pricing Range Accrual Interest Rate Swap employing LIBOR market models with jump risks

This research derives the LIBOR market model with jump risks, assuming that interest rates follow a continuous time path and tend to jump in response to sudden economic shocks. We then use the LIBOR model with jump risk to price a Range Accrual Interest Rate Swap (RAIRS). Given that the multiple jump processes are independent, we employ numerical analysis to further demonstrate the influence of jump size, jump volatility, and jump frequency on the pricing of RAIRS. Our results show a negative relation between jump size, jump frequency, and the swap rate of RAIRS, but a positive relation between jump volatility and the swap rate of RAIRS.     

基于非对称扩散跳跃过程的利率模型研究

在短期利率的扩散跳跃模型基础上,进一步考虑了模型扩散项方差自相关性、非对称性以及跳跃项的均值回复性等设定,以捕捉短期利率的均值回复、波动率集聚、非零偏态和超额峰度以及非连续性等特征。利用上海银行同业拆放市场（SHIBOR）日交易利率数据得出以下结论。首先,SHIBOR利率市场存在均值回复效应,由跳跃设定引起的混合正态分布能捕捉利率增量的尖峰特征。其次,利率增量方差遵循显著的非对称自相关过程,且正的冲击会产生更大的波动性,导致有偏分布。最后,跳跃是利率均值回复速率的重要组成部分,也是利率的水平值动态,尤其是波动性动态的重要来源。     

Testing the stabilization hypothesis in the UK short-term interest rates: Evidence from a GARCH-X model

The objective of this paper is to examine the validity of one of the recurring arguments made against futures markets that they give rise to price instability. The paper concentrates on the impact of futures trading on the spot market volatility of short-term interest rates. The analytical framework employed is based on a new statistical approach aiming to reconcile the traditional models of short-term interest rates and the conditional volatility processes. More specifically, this class of models aims to capture the dynamics of short-term interest rate volatility by allowing volatility to depend on both scale effects and information shocks. Using a GARCH-X and asymmetric GARCH-X model four main conclusions emerge from the present study. First, the empirical results suggest that there is an indisputable change in the nature of volatility with evidence of mean reversion after the onset of futures trading. Second, the information flow into the market has improved as a result of futures trading. Third, a stabilization effect has been detected running from the futures market to the cash market by lowering volatility levels and decreasing the risk in the spot market. Finally, trying to capture the leverage effect the findings suggest that positive shocks have a greater impact on volatility than negative shocks.     

Asymmetric determinants of corporate bond credit spreads in China: Evidence from a nonlinear ARDL model

This study utilizes the nonlinear ARDL (NARDL) model proposed by Shin, Yu, and Greenwood-Nimmo (2014) to quantify the potentially asymmetric transmission of positive and negative changes in each of the possible determinants of industry-level corporate bond credit spreads in China. The determinants we consider include the corresponding industry stock price, China’s stock market volatility, the level and slope of the yield curve (i.e., the interest rate), the industrial production growth rate, and the inflation rate. The empirical results suggest substantial asymmetric effects of these determinants on credit spreads, with the positive changes in the determinants showing larger impacts than the negative changes for most industries we consider. Moreover, the corresponding industry stock prices, the interest rate, and the industrial production growth rate negatively drive the industry credit spreads for many industries. In turn, China’s stock market volatility and the inflation rate positively affect the credit spreads at each industry level. These findings may be helpful to investors, bond issuers and policymakers in understanding the dynamics of credit risks and corporate bond rates at the industry level.     

Some empirical evidence on the real effects of nominal volatility

It has been argued that volatility in nominal macroeconomic aggregates has had a negative effect on real output, in particular that such volatility contributed to slow output growth in the early 1980s. This paper reexamines the effects of volatility in nominal macroeconomic aggregates in the context of a modern simultaneous equation framework where the volatility of, nominal macroeconomic variables is modeled as the conditional variance of two variables of interest: the federal funds rate and inflation. The empirical framework is the recently developed multivariate GARCH-in-mean vector autoregressive model. We confirm evidence that inflation volatility and tight monetary policy have directly affected output growth, but find that volatility in the federal funds rate has not.     

The credibility of the Colombian exchange rate target zone: Its impact over the volatility of interest rate differentials

This paper analyzes the credibility of Colombia’s exchange rate target zone, and in particular its impact on the volatility of interest rate differentials. Bertola and Caballeros’ (1995) model of a target zone with imperfect credibility is used to derive the impact of a reduction in credibility over the variance of the interest rate differential. It is theoretically shown, that as credibility decreases the variance of the interest rate differential increases. This theoretical argument is used to estimate credibility in Colombia’s exchange rate target zone. Using a SWARCH model, the probability of being in a regime with high interest rate volatility is estimated, and is related with events in the exchange rate market. Results suggest that there is evidence of lack of credibility even before the recent international financial turmoil was triggered.     

Time series properties of ARCH processes with persistent covariates

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.     

On the Link between Volatility and Growth: A Spatial Econometrics Approach

This paper examines the link between macro volatility and economic growth in the lens of spatial econometrics. We present an unconstrained spatial Durbin Ramey-Ramey model. We test the extended model in a panel of 78 countries to investigate all the possible dimensions along which spatial interactions can affect the link between macro volatility and growth. In contrast to previous literature, we split the effects of volatility on growth into direct and indirect effects using partial derivative impacts approach. We found that both the direct and indirect effects of volatility on growth are negative; the latter effect suggesting the transmission of volatility shocks to neighbouring countries. Growth rates observed in neighbouring countries has a positive effect on growth rate of a particular country.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.