利率跳跃扩散模型的理论估计与蒙特卡罗模拟检验

我国货币政策调控已基本实现由直接调控向间接调控的转变,对利率的形成机制产生重大的影响,因此传统的假设利率服从扩散过程的模型日益受到挑战。本文在将跳跃因子加入到CIR模型的基础上,提出了一种服从跳跃扩散过程的利率变化模型,并用最大似然估计法对模型中的参数进行了估计;在此基础上,对我国存贷款利率的变化过程进行了蒙特卡罗模拟检验。研究发现,加入跳跃扩散过程后,模型不但能更好地拟合实际数据,而且揭示了利率回复和水平效应的部分原因,从而增加了模型的解释能力。     

基于跳跃-扩散利率模型的浮动利率抵押贷款支持证券定价研究

已有文献在研究抵押贷款支持证券的定价时,所假设的利率随机过程大多是连续的,没有考虑到利率受人为或突发事件的干扰而产生跳跃不连续的情形.本文利用跳跃-扩散模型模拟利率随机过程,结合我国借款人行为特点建立提前偿还比例危险模型,运用Monte Carlo模拟方法,研究了浮动利率抵押贷款支持证券的定价,讨论了利率模型各参数的变化对定价的影响.经模拟发现:利率跳跃的频率、跳跃幅度的波动越大,证券价格越大,而利率跳跃幅度的均值越大,证券价格却越小.     

基于动态跳跃的中国短期利率研究:1997—2010

短期利率研究对固定收益定价和风险管理具有重要意义.把GARCH-Jump及其动态扩展形式引入到VASICEK短期利率模型中,拟合了中国的短期利率过程,检验了理性投资者人假说.实证结果表明:中国的短期利率过程不仅存在GARCH波动,还存在动态的跳跃波动因素;非参数检验表明以贝叶斯决策过程为设定条件的动态跳跃模型,对短期利率拟合得更好,并且预测能力更优,从而验证了中国短期利率市场的投资者在应对异常事件时采取贝叶斯理性决策法则;短期利率跳跃模型对投机和宏观信息冲击有一定的解释能力.     

我国银行间同业拆借利率的动态研究——基于跳跃-扩散-机制转换模型的实证分析

针对我国短期利率易受政策影响,波动较大并存在结构变化等特点,构建了跳跃-扩散-机制转换模型,同时考察了银行间7天同业拆借利率的波动、跳跃和结构变化三种效应,发现我国同业拆借利率不仅具有均值回归特性而且还存在明显的跳跃与机制转换,并且该模型比其嵌套的受限模型表现更佳.在高波动状态下利率波动的水平效应和ARCH效应可以忽略...     

我国银行间同业拆借利率的动态研究———基于跳跃 扩散 机制转换模型的实证分析

针对我国短期利率易受政策影响,波动较大并存在结构变化等特点,构建了跳跃 扩散 机制转换模型,同时考察了银行间 7 天同业拆借利率的波动、跳跃和结构变化三种效应,发现我国同业拆借利率不仅具有均值回归特性而且还存在明显的跳跃与机制转换,并且该模型 比其嵌套的受限模型表现更佳． 在高波动状态下利率波动的水平效应和 ARCH 效应可以忽略; 低波动状态下,水平效应可以忽略． 另外,跳跃具有聚类效应,高( 低) 的跳跃概率和高( 低)状态概率对应着高( 低) 利率和较高( 低) 的波动率,跳跃主要发生在高状态机制下,低状态机 制下发生跳跃的可能性很小．     

基于跳跃-扩散过程的煤炭资源采矿权估价三因素模型

煤炭资源采矿权可以被认为是一个多期多阶段的复合看涨期权,煤炭价格、便利收益和利率的随机波动都对煤炭资源采矿权价值有较大的影响。基于期权理论,构建了煤炭价格服从跳跃-扩散过程,利率和便利收益服从均值回复过程的煤炭资源采矿权估价三因素模型。实例运用表明,该模型较单因素模型、双因素模型更能体现资源所有者的权益。随着采矿权有效期的临近,关于利率、便利收益、煤炭开采成本和煤炭价格的信息趋于透明,这些因素的变动对采矿权的影响逐渐减弱。     

以1年期储蓄存款利率为状态变量的跳跃型广义Vasicek模型

1年期储蓄存款利率被认为是中国利率体系的基准利率,可将其作为影响市场利率期限结构的状态变量.市场利率不同于官方利率,它们还受其他经济变量的影响.这些其他经济变量对市场利率的影响用1年期市场利率与1年期储蓄存款利率的差别来反映,把它作为影响市场利率的另外一个状态变量.分别用跳跃过程和均值回复过程描述这两个状态变量的变化.在仿射模型的框架下,它们决定了市场利率期限结构.本文给出了该模型下市场利率期限结构的分析表达式,并利用MCMC方法对模型进行了实证分析.实证表明该模型能够很好地拟合市场利率期限结构样本观测值的统计特征.实证分析还发现,债券的超额回报率显著受官方利率调整风险和市场利率随机波动风险的影响.     

跳跃-扩散条件下信用风险相关性度量的变结构Copula模型

针对现有研究大多只考虑扩散条件的不足,构建了跳跃-扩散条件下信用风险相关性度量的变结构Copula模型。运用1991~2010年中国上市公司的数据构建了行业信用风险指数,运用双指数跳跃扩散模型来识别行业信用风险的跳跃扩散点,发现在样本期,共同因素与行业特质因素引发了行业信用风险的多次跳跃。在识别跳跃点的基础上,构建了变结构Copula模型,该模型能较准确地描述信用风险相关性的变化,各行业之间的信用风险相关系数在0.5以上,并且上市公司信用风险的变化呈现出"一损俱损"的特征,而"一荣俱荣"的特征并不明显。构建的模型及实证结论将有助于理解信用风险相关或传染,从而为信贷组合管理和风险管理提供更多的方法与经验。     

时间相依利率扩散模型的非参数估计

利率的变动模式会随着时间的推移、经济环境的变化和金融制度的改变而发生变化.时变的扩散模型能更好的描述短期利率的随机行为,文章采用基于核回归的非参数方法,估计中国银行间市场7天回购利率的时间相依CKLS模型.最后比较了时间相依CKLS模型与时齐CKLS模型在波动率预报上的表现,结果表明,时间相依CKLS模型更好的反映了利率实时的变化,提高了预测利率变化的精度.     

跳跃风险如何影响期权复制收益?——基于多维跳跃扩散的模型与证据

文章在最一般的多维跳跃扩散过程假设下,推导出Delta对冲组合盈亏所遵循的随机过程,从理论上证明了Delta对冲组合会受到跳跃风险以及跳跃风险的风险溢酬的影响.并且,通过美国SPX期权数据对理论推导的结论进行分样本实证,实证结果表明,在考虑了模型风险、市场信息传递效率等以往学者未曾考虑到的控制变量后,跳跃风险对于对冲标的风险后的期权复制收益的影响仍然显著,但其影响看涨看跌期权的内在途径和机理在平时、危机时刻都不相同.     

Nonparametric Estimation of Triangular Simultaneous Equations Models

This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model. Our approach employs series approximations that exploit the additive structure of the model. The first step comprises the nonparametric estimation of the reduced form and the corresponding residuals. The second step is the estimation of the primary equation via nonparametric regression with the reduced form residuals included as a regressor. We derive consistency and asymptotic normality results for our estimator, including optimal convergence rates. Finally we present an empirical example, based on the relationship between the hourly wage rate and annual hours worked, which illustrates the utility of our approach.     

Nonparametric Instrumental Variables Estimation of a Quantile Regression Model

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression “error” conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill‐posed inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is mean‐square consistent, derive its rate of convergence in probability, and give conditions under which this rate is optimal in a minimax sense. The results of Monte Carlo experiments show that the estimator behaves well in finite samples.     

Instrumental Variable Estimation of Nonparametric Models

In econometrics there are many occasions where knowledge of the structural relationship among dependent variables is required to answer questions of interest. This paper gives identification and estimation results for nonparametric conditional moment restrictions. We characterize identification of structural functions as completeness of certain conditional distributions, and give sufficient identification conditions for exponential families and discrete variables. We also give a consistent, nonparametric estimator of the structural function. The estimator is nonparametric two‐stage least squares based on series approximation, which overcomes an ill‐posed inverse problem by placing bounds on integrals of higher‐order derivatives.     

Nonparametric Estimation with Nonlinear Budget Sets

Choice models with nonlinear budget sets provide a precise way of accounting for the nonlinear tax structures present in many applications. In this paper we propose a nonparametric approach to estimation of these models. The basic idea is to think of the choice, in our case hours of labor supply, as being a function of the entire budget set. Then we can do nonparametric regression where the variable in the regression is the budget set. We reduce the dimensionality of this problem by exploiting structure implied by utility maximization with piecewise linear convex budget sets. This structure leads to estimators where the number of segments can differ across observations and does not affect accuracy. We give consistency and asymptotic normality results for these estimators. The usefulness of the estimator is demonstrated in an empirical example, where we find it has a large impact on estimated effects of the Swedish tax reform.     

Applied Nonparametric Instrumental Variables Estimation

Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used to identify and estimate these parameters. However, linear and other finite‐dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators.     

Nonparametric Estimation of Nonadditive Random Functions

We present estimators for nonparametric functions that are nonadditive in unobservable random terms. The distributions of the unobservable random terms are assumed to be unknown. We show that when a nonadditive, nonparametric function is strictly monotone in an unobservable random term, and it satisfies some other properties that may be implied by economic theory, such as homogeneity of degree one or separability, the function and the distribution of the unobservable random term are identified. We also present convenient normalizations, to use when the properties of the function, other than strict monotonicity in the unobservable random term, are unknown. The estimators for the nonparametric function and for the distribution of the unobservable random term are shown to be consistent and asymptotically normal. We extend the results to functions that depend on a multivariate random term. The results of a limited simulation study are presented.     

非参数计量经济联立模型的局部线性广义矩估计

联立方程模型在经济政策制定、经济结构分析和经济预测方面起重要作用。本文在随机设计(模型中所有变量为随机变量)下,提出了非参数计量经济联立模型的局部线性广义矩估计并利用概率论中大数定理和中心极限定理在内点处研究了它的大样本性质,证明了它的一致性和渐近正态性。它在内点处的收敛速度达到了非参数函数估计的最优收敛速度。     

Fully Nonparametric Estimation of Scalar Diffusion Models

We propose a functional estimation procedure for homogeneous stochastic differential equations based on a discrete sample of observations and with minimal requirements on the data generating process. We show how to identify the drift and diffusion function in situations where one or the other function is considered a nuisance parameter. The asymptotic behavior of the estimators is examined as the observation frequency increases and as the time span lengthens. We prove almost sure consistency and weak convergence to mixtures of normal laws, where the mixing variates depend on the chronological local time of the underlying diffusion process, that is the random time spent by the process in the vicinity of a generic spatial point. The estimation method and asymptotic results apply to both stationary and nonstationary recurrent processes.