我国国债零息票收益曲线的构造

债券的期限和收益率在某一既定时间存在的关系称为利率期限结构,表示这种关系的曲线通常称为收益曲线;而零息票债券的即期收益率与其期限之间的关系曲线图则称为零息票收益曲线.零息票收益曲线描述的收益率排除了债券的违约风险、税率、可售性以及再投资等因素的影响,因而从零息票收益曲线中所导出的贴现因子、远期利率、即期利率以及各曲线间的利差都十分可靠,这就使得零息票收益曲线已成为金融工具估价、投资组合管理和金融风险管理的重要组成部分.而现金流债券定价方法根据利率期限结构理论,将债券在未来不同时刻产生的现金流采用与期限相对应的利率进行折现,并加总得到债券的市场价格.很明显,该法考虑了利率的期限结构及其变化,从而债券的定价更加精确;但其前提是必须事先构造零息票收益曲线.     

利率期限结构预测、国债定价及国债组合管理

国债利率期限结构是固定收益产品定价和投资组合管理的核心问题。本文利用NARX（Nonlinear AutoRegressive network with eXogenous inputs）神经网络模型研究利率曲线的运动机制,拟合并预测利率期限结构,在此基础上利用Hermite插值方法构造平滑的利率曲线并计算得到国债理论价格及其预测值。实证分析发现我国国债定价效率不足,交易价格显著偏离理论价格,但国债的理论价格的实际值和预测值均对交易价格具有显著的预测能力。基于上述发现本文提出了主动国债组合管理策略,通过预测的期限结构得到国债理论价格的预测值构建的多空对冲组合和单边多头组合均能获得显著的收益。本文的研究丰富了利率期限结构的研究方法,提出的主动国债组合管理策略对通过交易提高国债定价有效性具有参考价值。     

Hull-White随机利率模型在债券价值分析中的应用

Hull-White随机利率模型既考虑了短期利率的均值回复(mean reversion)特性,又与初始期限结构相符合.本文就如何将Hull-White模型应用于债券定价进行了探讨,并将之应用于债券价值的风险分析.     

基于MCMC方法的机制转换利率模型实证研究

文章分析了机制转换利率模型及相应的债券定价公式。利用银行间国债交易数据,使用马尔可夫链蒙特卡罗方法对机制转换利率模型进行了实证分析。结果表明机制转换利率模型能较好的刻画利率的期限结构,我国银行间国债市场的利率期限结构存在着机制转换的现象。     

随机利率下欧式双向期权的定价

假设无风险利率是随机利率,本文在考虑基础变量-无风险证券(债券)和风险证券(股票)价格行为特征的基础上,利用鞅方法推导了随机利率下欧式双向期权的定价公式.     

具有违约风险的零息票债券的三因子定价模型

文章假定具有信用风险的零息票债券的价格受短期利率,公司资本结构和短期信用价差三个随机因子的影响,并进一步假定了这三个因子的运动形式。在这些假定的前提下,推导出了具有信用风险的零息票债券的价格所满足的偏微分方程,并给出了具有信用风险的零息票债券的定价公式。同时,对具有信用风险的零息票债券的信用风险价差的期限结构和其收益率的敏感性做了进一步的分析。     

我国同业拆借市场利率期限结构的实证研究

一、利率期限结构理论利率期限结构是指在某一确定时点上利率到期期限和到期收益率之间的函数关系 ,又称收益率曲线。利率期限结构理论主要集中于研究收益率曲线形状及其形成原因。并用于解释下面三个重要的经验事实 :1 不同期限债券的利率随着时间一起变动 ;2 如果短期利率低 ,回报率曲线更趋向于向上倾斜 ;如果短期利率高 ,则回报率曲线更可能向下倾斜。 3 典型的回报率曲线总是向上倾斜。在历史上有相关的三种理论引起人们的注意 ,这就是预期理论 (ＥｘｐｅｃｔａｔｉｏｎｓＨｙｐｏｔｈｅｓｉｓ)、流动性偏好理论(ＬｉｑｕｉｄｉｔｙＰ…     

随机利率情形下的可转换债券的定价

一、可转换债券的价值构成可转换债券,简称可转债,是一种可以在一定期限之后,按规定的转换比率或转换价格转换成发行公司普通股股票的债券。可转换债券兼具债券和股票的特性,主要有以下几个特点。(1)债权性。与其他债券一样,可转换债券也有规定的利率和期限。投资者可以选择持有债券到期,收取本金和利     

我国国债回购利率的主成分分析

人们在研究利率动态行为时,通常借助利率期限结构来加以反映,利率期限结构是在某个时点上不同期限的利率所组成的一条利率曲线。它可以表示为在某个时点不同期限的零息债券得到其收益率所组成的一天收益率曲线。利率期限结构是债券市场中最为重要的概念之一,体现了利率与剩余期限之间的关系。从横截面的角度看,利率期限结构可以用一条无风险债券的收益率曲线表示。它有各种可能的形状,如水平、下凹、上凸等,它的变动也有平行和非平行移动等,尤其是非平行移动是利率风险的重要组成部分,它的存在常常导致债券投资组合中久期和凸性套期保值策略的有效性削弱。为了揭示数据所暗示的利率变动潜在     

巨灾债券的定价模型比较研究

巨灾债券的定价是巨灾债券的核心技术及难题。文章从规范学的角度来分析巨灾债券的定价,以金融衍生品的无套利定价方法确定巨灾债券的价格;从实证学角度分析巨灾债券的定价,以利用精算学中的Wang变换和双因素变换模型为定价方法,分析巨灾债券的价格。通过对实际巨灾债券的价格实证分析得到;双因素模型能更好的拟合实际价差,对单一事件单一期限的巨灾债券,运用双因素模型得到较高的拟合优度。     

A Stochastic Volatility Model With Realized Measures for Option Pricing

Abstract

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options.     

Oracle properties of SCAD-penalized support vector machine

In many scientific investigations, a large number of input variables are given at the early stage of modeling and identifying the variables predictive of the response is often a main purpose of such investigations. Recently, the support vector machine has become an important tool in classification problems of many fields. Several variants of the support vector machine adopting different penalties in its objective function have been proposed. This paper deals with the Fisher consistency and the oracle property of support vector machines in the setting where the dimension of inputs is fixed. First, we study the Fisher consistency of the support vector machine over the class of affine functions. It is shown that the function class for decision functions is crucial for the Fisher consistency. Second, we study the oracle property of the penalized support vector machines with the smoothly clipped absolute deviation penalty. Once we have addressed the Fisher consistency of the support vector machine over the class of affine functions, the oracle property appears to be meaningful in the context of classification. A simulation study is provided in order to show small sample properties of the penalized support vector machines with the smoothly clipped absolute deviation penalty.     

A Stochastic Volatility Model With Conditional Skewness

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on current factors and past information, which we term contemporaneous asymmetry. Conditional skewness is an explicit combination of the conditional leverage effect and contemporaneous asymmetry. We derive analytical formulas for various return moments that are used for generalized method of moments (GMM) estimation. Applying our approach to S&P500 index daily returns and option data, we show that one- and two-factor SVS models provide a better fit for both the historical and the risk-neutral distribution of returns, compared to existing affine generalized autoregressive conditional heteroscedasticity (GARCH), and stochastic volatility with jumps (SVJ) models. Our results are not due to an overparameterization of the model: the one-factor SVS models have the same number of parameters as their one-factor GARCH competitors and less than the SVJ benchmark.     

Identification of vector AR models with recursive structural errors using conditional independence graphs

In canonical vector time series autoregressions, which permit dependence only on past values, the errors generally show contemporaneous correlation. By contrast structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Such models having a recursive structure can be described by a directed acyclic graph. We show, with the use of a real example, how the identification of these models may be assisted by examination of the conditional independence graph of contemporaneous and lagged variables. In this example we identify the causal dependence of monthly Italian bank loan interest rates on government bond and repurchase agreement rates. When the number of series is larger, the structural modelling of the canonical errors alone is a useful initial step, and we first present such an example to demonstrate the general approach to identifying a directed graphical model.     

Affine LIBOR models driven by real-valued affine processes

The class of affine LIBOR models is appealing since it satisfies three central requirements of interest rate modeling. It is arbitrage-free, interest rates are nonnegative, and caplet and swaption prices can be calculated analytically. In order to guarantee nonnegative interest rates affine LIBOR models are driven by nonnegative affine processes, a restriction that makes it hard to produce volatility smiles. We modify the affine LIBOR models in such a way that real-valued affine processes can be used without destroying the nonnegativity of interest rates. Numerical examples show that in this class of models, pronounced volatility smiles are possible.     

Comment: Uses of Administrative Records: A Social Security Point of View

Recent evidence indicates that using multiple forward rates sharply predicts future excess returns on U.S. Treasury Bonds, with the R²'s being around 30%. The projection coefficients in these regressions exhibit a distinct pattern that relates to the maturity of the forward rate. These dimensions of the data, in conjunction with the transition dynamics of bond yields, offer a serious challenge to term structure models. In this article we show that a regime-shifting term structure model can empirically account for these challenging data features. Alternative models, such as affine specification, fail to account for these important features. We find that regimes in the model are intimately related to bond risk premia and real business cycles.     

A Class of Multidimensional Latent Class IRT Models for Ordinal Polytomous Item Responses

We propose a class of multidimensional Item Response Theory models for polytomously-scored items with ordinal response categories. This class extends an existing class of multidimensional models for dichotomously-scored items in which the latent abilities are represented by a random vector assumed to have a discrete distribution, with support points corresponding to different latent classes in the population. In the proposed approach, we allow for different parameterizations for the conditional distribution of the response variables given the latent traits, which depend on the type of link function and the constraints imposed on the item parameters. Moreover, we suggest a strategy for model selection that is based on a series of steps consisting of selecting specific features, such as the dimension of the model (number of latent traits), the number of latent classes, and the specific parameterization. In order to illustrate the proposed approach, we analyze a dataset from a study on anxiety and depression on a sample of oncological patients.     

Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure

ABSTRACT

We introduce a class of large Bayesian vector autoregressions (BVARs) that allows for non-Gaussian, heteroscedastic, and serially dependent innovations. To make estimation computationally tractable, we exploit a certain Kronecker structure of the likelihood implied by this class of models. We propose a unified approach for estimating these models using Markov chain Monte Carlo (MCMC) methods. In an application that involves 20 macroeconomic variables, we find that these BVARs with more flexible covariance structures outperform the standard variant with independent, homoscedastic Gaussian innovations in both in-sample model-fit and out-of-sample forecast performance.     

Empirical Performance and Asset Pricing in Hidden Markov Models

Abstract

To improve the empirical performance of the Black-Scholes model, many alternative models have been proposed to address leptokurtic feature, volatility smile, and volatility clustering effects of the asset return distributions. However, analytical tractability remains a problem for most alternative models. In this article, we study a class of hidden Markov models including Markov switching models and stochastic volatility models, that can incorporate leptokurtic feature, volatility clustering effects, as well as provide analytical solutions to option pricing. We show that these models can generate long memory phenomena when the transition probabilities depend on the time scale. We also provide an explicit analytic formula for the arbitrage-free price of the European options under these models. The issues of statistical estimation and errors in option pricing are also discussed in the Markov switching models.     

Bayesian inference for nonlinear stochastic SIR epidemic model

ABSTRACT

Inference for epidemic parameters can be challenging, in part due to data that are intrinsically stochastic and tend to be observed by means of discrete-time sampling, which are limited in their completeness. The problem is particularly acute when the likelihood of the data is computationally intractable. Consequently, standard statistical techniques can become too complicated to implement effectively. In this work, we develop a powerful method for Bayesian paradigm for susceptible–infected–removed stochastic epidemic models via data-augmented Markov Chain Monte Carlo. This technique samples all missing values as well as the model parameters, where the missing values and parameters are treated as random variables. These routines are based on the approximation of the discrete-time epidemic by diffusion process. We illustrate our techniques using simulated epidemics and finally we apply them to the real data of Eyam plague.