单因子利率期限结构模型的广义矩估计及对中国货币市场的实证检验

本文在多个著名的单因子利率模型的基础上，提出了一个新的一般模型，其漂移项涵盖了线性和非线性两种形式。并用广义矩方法进行参数估计，在多种指标的比较下得到了一个较好模型。此模型的漂移项为非线性形式，具有显著的均值回复效应，且利率波动对利率水平极为敏感。     

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。     

煤炭价格对燃煤发电项目内部收益率影响关系的研究

煤炭价格对燃煤发电项目经济效益具有决定性影响,煤价与电价的相对水平决定了煤炭行业、电力行业、政府间的利益分配。电力行业的市场化进度相对落后于其上游的煤炭产业,煤炭价格波动对电力企业的运营构成较大的成本风险。内部收益率是燃煤电厂经济效益的关键指标,文章以内部收益率作为研究对象,分析煤炭价格变动对内部收益率的影响,运用回归方法拟合煤炭价格对内部收益率的影响曲线,进一步通过数值差分法得到煤炭价格对内部收益率的微分曲线即边际影响曲线,并利用双曲函数拟合边际影响曲线。研究结果表明,在较低价格区间内,煤炭价格变化对内部收益率的边际影响度较小;在较高价格范围内,边际影响度以双曲函数的形式快速下降。     

非参数利率期限结构模型的理论与实证研究

本文利用核估计方法建立了非参数的利率期限结构模型，实证结果表明短期利率模型的扩散函数和漂移函数都是非线性的，否定了参数模型一先对扩散函数和漂移函数的参数假设。同时建立了债券和利率衍和证券的PDE定价方程。     

基于Vasicek模型的债券定价实证研究

本文利用传统的单因子利率期限结构对我们债券市场利率期限结构进行实证研究，通过回归方法，同时对不同债券进行定价并与显示价格进行对比，以说明这些模型在我国现阶段的适应程度。     

吉林省农村居民与城镇居民消费模式分析

文章将基于绝对收入和相对收入假说的消费模型扩展为时变参数模型,利用卡尔曼滤波对模型进行估计,得到时变边际消费倾向,识别了吉林省农村居民和城镇居民消费函数中边际消费倾向系数的时变特征,并比较了吉林省农村居民与城镇居民消费模式上的差异。结果表明,基于相对收入假说的消费函数模型能够更好地描述吉林省农村居民和城镇居民的消费行为。吉林省农村居民的边际消费倾向低于城镇居民的边际消费倾向,主要是由于农村居民对未来的预期没有城镇居民乐观。因此,提高农村居民的收入水平,增强其对未来收入的预期是扩大农村居民消费的有效途径。     

与有弹性钉住通货膨胀相一致的货币政策规则

在与有弹性钉住通货膨胀相一致的货币政策动态优化问题中，本文将货币政策工具对不同目标变量影响的时滞引入损失函数，并分别在确定性等价条件下，以及模型参数不确定性条件下给出了最优的利率反应函数，进而讨论了模型参数以及模型参数不确定性对最优利率反应函数的影响。     

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。     

公共项目投资评价的贴现率选择

从可持续发展的角度看,现行以市场利率为基础的贴现率是过高的,不利于社会的可持续发展。笔者认为,确定公共项目投资贴现率的较为科学的方法是双曲贴现函数法。     

边际消费倾向的吸引子,倍周期和混沌

本文用消费函数和结构乘数模型,建立了边际消费倾向的二次函数模型。讨论了边际消费倾向随消费函数参数变化的性质,描述了边际消费倾向的吸引子、倍周期和混沌现象。     

On the construction of approximate multi-factor designs from given marginals using the iterative proportional fitting procedure

Summary For multifactor designs based on linear models, the information matrix generally depends on a certain set of marginal tables created from the design itself. This note considers the problems of whether a set of marginal tables is consistent, in that a design exists that can yield them, and of calculating such a design when at least one does exist. The results are obtained by direct analogy with the problem of maximum likelihood estimation in longlinear models for categorical data.     

非参数核密度估计在异方差模型中的应用

针对非参数核密度估计中最优窗宽的选择在实际建模中的不足,提出了一个新的最优窗宽选择的迭代方法,克服了使用传统的经验法则所带来的局限性。并在此基础上用一种新的非参数核密度估计ML方法应用到了中国股票市场,通过与极大似然估计对比论证了此方法的有效性和可行性。实证分析表明,通过与实际值的模拟对比,运用非参数估计技术得到上证指数日收益率的拟合值要优于极大似然估计的拟合值。     

The comparative forecast performance of univariate and multivariate models: an application to real interest rate forecasting

Does the use of information on the past history of the nominal interest rates and inflation entail improvement in forecasts of the ex ante real interest rate over its forecasts obtained from using just the past history of the realized real interest rates? To answer this question we set up a univariate unobserved components model for the realized real interest rates and a bivariate model for the nominal rate and inflation which imposes cointegration restrictions between them. The two models are estimated under normality with the Kalman filter. It is found that the error-correction model provides more accurate one-period ahead forecasts of the real rate within the estimation sample whereas the unobserved components model yields forecasts with smaller forecast variances. In the post-sample period, the forecasts from the bivariate model are not only more accurate but also have tighter confidence bounds than the forecasts from the unobserved components model.     

Econometric modelling in finance and risk management: An overview

This paper gives an overview about the sixteen papers included in this special issue. The papers in this special issue cover a wide range of topics. Such topics include discussing a class of tests for correlation, estimation of realized volatility, modeling time series and continuous-time models with long-range dependence, estimation and specification testing of time series models, estimation in a factor model with high-dimensional problems, finite-sample examination of quasi-maximum likelihood estimation in an autoregressive conditional duration model, and estimation in a dynamic additive quantile model.     

Estimation of affine term structure models with spanned or unspanned stochastic volatility

We develop new procedures for maximum likelihood estimation of affine term structure models with spanned or unspanned stochastic volatility. Our approach uses linear regression to reduce the dimension of the numerical optimization problem yet it produces the same estimator as maximizing the likelihood. It improves the numerical behavior of estimation by eliminating parameters from the objective function that cause problems for conventional methods. We find that spanned models capture the cross-section of yields well but not volatility while unspanned models fit volatility at the expense of fitting the cross-section.     

Estimating the structural credit risk model when equity prices are contaminated by trading noises

The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan [Duan, J.-C., 1994. Maximum likelihood estimation using price data of the derivative contract. Mathematical Finance 4, 155–167] is extended to account for the fact that observed equity prices may have been contaminated by trading noises. With the presence of trading noises, the likelihood function based on the observed equity prices can only be evaluated via some nonlinear filtering scheme. We devise a particle filtering algorithm that is practical for conducting the MLE estimation of the structural credit risk model of Merton [Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449–470]. We implement the method on the Dow Jones 30 firms and on 100 randomly selected firms, and find that ignoring trading noises can lead to significantly over-estimating the firm’s asset volatility. The estimated magnitude of trading noise is in line with the direction that a firm’s liquidity will predict based on three common liquidity proxies. A simulation study is then conducted to ascertain the performance of the estimation method.     

SMOOTH DYNAMIC FACTOR ANALYSIS WITH APPLICATION TO THE US TERM STRUCTURE OF INTEREST RATES

We consider the dynamic factor model and show how smoothness restrictions can be imposed on factor loadings by using cubic spline functions. We develop statistical procedures based on Wald, Lagrange multiplier and likelihood ratio tests for this purpose. The methodology is illustrated by analyzing a newly updated monthly time series panel of US term structure of interest rates. Dynamic factor models with and without smooth loadings are compared with dynamic models based on Nelson–Siegel and cubic spline yield curves. We conclude that smoothness restrictions on factor loadings are supported by the interest rate data and can lead to more accurate forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Zero truncated Poisson integer-valued AR(1) model

In this paper, we introduce a new stationary integer-valued autoregressive process of the first order with zero truncated Poisson marginal distribution. We consider some properties of this process, such as autocorrelations, spectral density and multi-step ahead conditional expectation, variance and probability generating function. Stationary solution and its uniqueness are obtained with a discussion to strict stationarity and ergodicity of such process. We estimate the unknown parameters by using conditional least squares estimation, nonparametric estimation and maximum likelihood estimation. The asymptotic properties and asymptotic distributions of the conditional least squares estimators have been investigated. Some numerical results of the estimators are presented and some sample paths of the process are illustrated. Some possible applications of the introduced model are discussed.     

On solving bias‐corrected non‐linear estimation equations with an application to the dynamic linear model

In a seminal paper, Mak, Journal of the Royal Statistical Society B, 55, 1993, 945, derived an efficient algorithm for solving non‐linear unbiased estimation equations. In this paper, we show that when Mak's algorithm is applied to biased estimation equations, it results in the estimates that would come from solving a bias‐corrected estimation equation, making it a consistent estimator if regularity conditions hold. In addition, the properties that Mak established for his algorithm also apply in the case of biased estimation equations but for estimates from the bias‐corrected equations. The marginal likelihood estimator is obtained when the approach is applied to both maximum likelihood and least squares estimation of the covariance matrix parameters in the general linear regression model. The new approach results in two new estimators when applied to the profile and marginal likelihood functions for estimating the lagged dependent variable coefficient in the dynamic linear regression model. Monte Carlo simulation results show the new approach leads to a better estimator when applied to the standard profile likelihood. It is therefore recommended for situations in which standard estimators are known to be biased.