Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial datasets, it is shown that the inverse gamma and generalized inverse gamma processes can greatly outperform the commonly used log normal volatility processes with Student’s t errors or jumps in the mean equation.     

频率选择性衰落信道中的RAKE合并接收方案

针对非扩频系统很难实现多径分集接收的问题,通过判决反馈IIR(DF-IIR)均衡器得到对发送数据的估计,并根据该初始估计重构所有多径信号,再将接收信号中的主径和各多径分量区分开,最后将各分量按照最大比的方式合并以及解调判决,从而实现RAKE合并接收。DF-IIR均衡器可以完全消除噪声累积的现象,然而判决反馈同时会产生误差传播的问题,多径合并可以显著提高信噪比,从而抑制误差传播问题。仿真结果表明新算法可以取得明显的效果。     

2-D FIR数字滤波的一种新结构(英文)

对2-D FIR数字滤波提出了一种有效的VLSI算法和实现结构,该算法结构在实现上大大减少了乘法器和延迟器的数目,因而有着广泛的应用前景。     

状态变量带通滤波器的研究

本文采用状态变量法来综合有源滤波器,它模拟标准二阶传输函数构成二阶带通状态变量滤波器,用这种滤波器作为基本节电路,构成四阶带通、参差调谐四阶带通和参差调谐六阶带通滤波器。研究构成高阶带通滤波器的几种新颖方法。这类带通滤波器时于低频、基低频、高 Q 值和良好的矩形因数等场合是很有用的。     

Seasonal Adjustment of Time Series Using One-Sided Filters

This article presents a model-based signal extraction seasonal adjustment procedure to extract estimates of the independent unobserved seasonal and nonseasonal components from an observed time series. The decomposition yields a one-sided filter that is optimal for adjusting the most recent observation under the assumption of using only the past observed series. Some advantages of this procedure are that no forecasts are required for implementation and there are no problems of revision of estimates or questions of concurrent adjustment. Comparisons are made with existing procedures using two-sided filters.     

Low-pass filter design using locally weighted polynomial regression and discrete prolate spheroidal sequences

The paper concerns the design of nonparametric low-pass filters that have the property of reproducing a polynomial of a given degree. Two approaches are considered. The first is locally weighted polynomial regression (LWPR), which leads to linear filters depending on three parameters: the bandwidth, the order of the fitting polynomial, and the kernel. We find a remarkable linear (hyperbolic) relationship between the cut-off period (frequency) and the bandwidth, conditional on the choices of the order and the kernel, upon which we build the design of a low-pass filter.The second hinges on a generalization of the maximum concentration approach, leading to filters related to discrete prolate spheroidal sequences (DPSS). In particular, we propose a new class of low-pass filters that maximize the concentration over a specified frequency range, subject to polynomial reproducing constraints. The design of generalized DPSS filters depends on three parameters: the bandwidth, the polynomial order, and the concentration frequency. We discuss the properties of the corresponding filters in relation to the LWPR filters, and illustrate their use for the design of low-pass filters by investigating how the three parameters are related to the cut-off frequency.     

新型的无监督纹理分割方法

提出了一种利用Gabor滤波器组对图像进行纹理特征提取,然后将这些特征向量作为外部刺激输入给PCNN对图像进行分割的新方法。该方法既保持了精确的分割结果,同时又解决了Gabor滤波器运算数据量大处理速度慢的问题。从实验结果可以看出,该方法与传统采用Gabor的纹理分割方法相比速度有很大的提高,而其分割精度与传统分割方法相似。     

多级完全自适应陷波滤波器的设计

设计了一种典型的自适应级联结构的IIR陷波滤波器,用来探测和跟踪频率随时变化的信号。该信号含有加性高斯白噪声,采用全通滤波器的频域转换和梯度截短算法。通过仿真模拟显示,该算法计算简单,收敛迅速,并且能产生较好的频率分辨率。     

A Novel Particle Filter Aided by Interval Analysis Approach

This paper addresses the issue of state estimation in nonlinear systems in the presence of non-Gaussian and bounded noises, under which the interval analysis based estimation is introduced as an auxiliary approach of generic particle filter (PF). This yields the so-called Set-Membership aided particle filter (SMPF). Unlike the mature alternatives of generic particle filter, the proposal distribution of SMPF approximates the posterior probability density function (PDF), not only on the numerical value but also on the definition-domain, and the performance analysis on the proposed alternative is proven through detailed formulations. In addition, contrasting simulations under SMPF and other mature alternatives also validate the effectiveness of SMPF.     

时变风险厌恶下的期权定价——基于上证50ETF期权的实证研究

传统的随机波动率（SV）期权定价是在投资者具有常数风险偏好假设下进行的.但近年来越来越多的研究表明,市场参与者具有时变风险厌恶特征.基于此,本文对时变风险厌恶条件下的期权定价问题进行深入研究.首先,对传统的（非仿射）常数风险厌恶SV（CRA-SV）期权定价模型进行扩展,构建时变风险厌恶SV（TVRA-SV）期权定价模型对期权进行定价,并分析时变风险厌恶对期权价格的影响;其次,采用标的资产与期权数据信息,建立基于连续粒子滤波的极大似然估计方法,对定价模型的客观与风险中性参数进行联合估计;最后,采用我国期权市场上的上证50ETF期权数据,对构建的定价模型进行实证检验.结果表明：TVRA-SV期权定价模型相比传统的CRA-SV期权定价模型具有更好的数据拟合效果,能够更充分地刻画标的上证50ETF收益率在客观与风险中性测度下的波动性;TVRA-SV期权定价模型相比传统的Black-Scholes（B-S）期权定价模型和CRA-SV期权定价模型都具有明显更高的定价精确性。