不完全市场天然气期货定价及季节性影响研究

天然气具有运输不便及存储成本较高等特点,其供应无法满足季节性需求,即天然气价格具有强烈的季节性。另外天然气作为商品,其弱流动性导致了其期货市场的不完全性。本文考虑了天然气期货市场的不完全性和现货价格的随机季节性因素,运用随机贴现因子方法推导出天然气期货的定价模型。为了验证模型的实用性,利用纽约商品交易所(NYMEX)的天然气期货日常价格数据对模型进行实证分析,结果表明:由于天然气期货市场的不完全性而导致的市场波动主要由短期偏离、中期偏离和随机季节性因素造成的;天然气价格季节性周期为一年;天然气期货价格短期和中期偏离具有较强的均值回复性,及其期货价格有长期增长的趋势。考虑期货市场的不完全性和价格的随机季节行为,并用随机贴现因子推导的定价方法的拟合效果远好于传统的期货定价模型。     

Do Central Bank Liquidity Facilities Affect Interbank Lending Rates?

In response to the global financial crisis that started in August 2007, central banks provided extraordinary amounts of liquidity to the financial system. To investigate the effect of central bank liquidity facilities on term interbank lending rates near the start of the crisis, we estimate a six-factor arbitrage-free model of U.S. Treasury yields, financial corporate bond yields, and term interbank rates. This model can account for fluctuations in the term structure of credit and liquidity spreads observed in the data. A significant shift in model estimates after the announcement of the liquidity facilities suggests that these central bank actions did help lower the liquidity premium in term interbank rates.     

Estimation and tests of hypotheses for the initial mean and covariance in the kalman filter model

Kalman filtering techniques are widely used by engineers to recursively estimate random signal parameters which are essentially coefficients in a large-scale time series regression model. These Bayesian estimators depend on the values assumed for the mean and covariance parameters associated with the initial state of the random signal. This paper considers a likelihood approach to estimation and tests of hypotheses involving the critical initial means and covariances. A computationally simple convergent iterative algorithm is used to generate estimators which depend only on standard Kalman filter outputs at each successive stage. Conditions are given under which the maximum likelihood estimators are consistent and asymptotically normal. The procedure is illustrated using a typical large-scale data set involving 10-dimensional signal vectors.     

Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search

We propose a Bayesian stochastic search approach to selecting restrictions on multivariate regression models where the errors exhibit deterministic or stochastic conditional volatilities. We develop a Markov chain Monte Carlo (MCMC) algorithm that generates posterior restrictions on the regression coefficients and Cholesky decompositions of the covariance matrix of the errors. Numerical simulations with artificially generated data show that the proposed method is effective in selecting the data-generating model restrictions and improving the forecasting performance of the model. Applying the method to daily foreign exchange rate data, we conduct stochastic search on a VAR model with stochastic conditional volatilities.     

A recursive approach to parameter estimation in regression and time series models

In this paper we discuss the recursive (or on line) estimation in (i) regression and (ii) autoregressive integrated moving average (ARIMA) time series models. The adopted approach uses Kalman filtering techniques to calculate estimates recursively. This approach is used for the estimation of constant as well as time varying parameters. In the first section of the paper we consider the linear regression model. We discuss recursive estimation both for constant and time varying parameters. For constant parameters, Kalman filtering specializes to recursive least squares. In general, we allow the parameters to vary according to an autoregressive integrated moving average process and update the parameter estimates recursively. Since the stochastic model for the parameter changes will "be rarely known, simplifying assumptions have to be made. In particular we assume a random walk model for the time varying parameters and show how to determine whether the parameters are changing over time. This is illustrated with an example.     

铌酸锂电光长周期波导光栅

该文对用铌酸锂制作电光长周期波导光栅的研究进展进行了综述。讨论了用铌酸锂制作长周期光栅的难点,介绍了解决此难点所用的特殊铌酸锂波导的制作原理和方法,再重点论述了对铌酸锂电光长周期波导光栅的研究,包括包层模阶数、铌酸锂波导的结构参数对光栅性能的影响以及光栅的谐振波长的温度灵敏度。研究表明铌酸锂长周期波导光栅的强度和谐振波长可分别由电压和温度调控,使该器件能进一步开发成为新型高速动态光滤波器或调制器。目前在最佳样品中实现25dB光栅强度调控所需的驱动电压不超过49V,其谐振波长的温度灵敏度为-1.0nm/℃。     

基于线性正则变换的时频信号分离方法

时频信号分析是当今信号处理领域研究的一个热点问题,各种时频分布函数得到了广泛的研究和应用。线性正则变换是一种重要的时频分析工具。文中研究了线性正则变换与传统时频分布函数的关系;并基于这些关系,提出了一种新的时频信号分离方法,能够把在时频面上互不重叠、但在时域和频域均存在较强耦合的多分量合成信号有效地分离。仿真实例表明了该方法的正确性和实用性。     

EBPSK系统抗脉冲干扰性能研究

首先介绍了扩展的二元相移键控(EBPSK)调制波形及脉冲干扰模型,提出了一种简单的基于冲击滤波器和限幅的抗干扰传输模型,从理论上分析了该系统对抗脉冲干扰的优势.仿真实验表明,在严重的脉冲干扰情况下,EBPSK系统表现出超强的抗干扰能力,同时,也通过误码率曲线表明了基于限幅的系统模型的有效性,从而为超窄带的EBPSK系统迈入实际应用阶段奠定了基础.     

基于多因子仿射利率期限结构模型的国债定价

本文构建了具有平方根扩散特征的三因子仿射利率期限结构模型,给出了基于卡尔曼滤波法的模型参数估计过程,利用蒙特卡罗模拟对我国国债进行定价预测,并与Longstaff-Schwartz模型、Vasicek模型、Cox-Inger-soll-Ross模型的定价效果进行实证比较.结果表明多因子模型要优于单因子模型,双因子模型要略优于三因子模型,从而为我国国债合理定价提供技术支持.     

Exponential family state space models based on a conjugate latent process

This paper introduces non-linear and non-Gaussian state space models with analytic updating recursions for filtering and prediction. This new class of models involves some well-known results in the theory of exponential models and of exponential dispersion models and the latent process is defined in such a way that both the filtering and the prediction distributions turn out to be conjugate to the observation distribution at each step. The corresponding analytic and inferential properties are investigated and some simple examples are presented.