汽车倾斜牌照中字符的定位与提取

提出了汽车图像中倾斜汽车牌照字符的定位与提取算法。该算法针对汽车牌照的灰度特点,先确定牌照的位置,并从背景中分割出来。对二值化车牌定位图通过Hough变换,计算出车牌主要直线的倾斜角度,通过插值旋转后,重新确定车牌字符的边界,最后精确地提取出汽车牌照中的字符。与其它传统的方法比较,该方法在运算速度和精确性上有一定优势。     

微带压控振荡器设计与仿真

介绍了S波段微带压控振荡器的设计方法,并使用微波仿真软件Serenade8.5对振荡器进行仿真分析,给出了仿真和实际电路的结果。     

Scaledt分布、杠杆效应和上证综指的VaR风险

与正态分布相比,上证指数收益率的经验分布具有尖峰厚尾特征,但用Scaled t-分布比正态分布可以更好地拟合上证指数收益率的经验分布。本文以Scaled t-分布假设下的GJR模型为基础,测量了上证指数收益率波动性的杠杆效应,即信息对波动性的不对称影响：并根据GJR模型应用Monte Carlo模拟方法,测定上证指数日收益率和持有期收益率的风险价值（VaR）。根据GJR模型提供的结果,上证指数30天、60天和90天持有期收益率的风险值分别为12．1％、17．8％、22．0％。用GJR模型比均值-方差模型和历史模拟方法计算的5％显著性水平VaR值更接近实际收益率。     

MC's for MCMC'ists

We develop a minimum amount of theory of Markov chains at as low a level of abstraction as possible in order to prove two fundamental probability laws for standard Markov chain Monte Carlo algorithms:
1. The law of large numbers explains why the algorithm works: it states that the empirical means calculated from the samples converge towards their "true" expected values, viz. expectations with respect to the invariant distribution of the associated Markov chain (=the target distribution of the simulation).
2. The central limit theorem expresses the deviations of the empirical means from their expected values in terms of asymptotically normally distributed random variables. We also present a formula and an estimator for the associated variance.     

On the Application of Markov Chain Monte Carlo Methods to Genetic Analyses on Complex Pedigrees

Markov chain Monte Carlo methods are frequently used in the analyses of genetic data on pedigrees for the estimation of probabilities and likelihoods which cannot be calculated by existing exact methods. In the case of discrete data, the underlying Markov chain may be reducible and care must be taken to ensure that reliable estimates are obtained. Potential reducibility thus has implications for the analysis of the mixed inheritance model, for example, where genetic variation is assumed to be due to one single locus of large effect and many loci each with a small effect. Similarly, reducibility arises in the detection of quantitative trait loci from incomplete discrete marker data. This paper aims to describe the estimation problem in terms of simple discrete genetic models and the single-site Gibbs sampler. Reducibility of the Gibbs sampler is discussed and some current methods for circumventing the problem outlined.     

European systemic credit risk transmission using Bayesian networks

The analysis of systemic credit risk is one of the most important concerns within the financial system. Its complexity lies in adequately measuring how the transmission of systemic default spreads through assets or financial markets. The transmission structure of systemic credit risk across several European sectoral CDS is studied by dynamic Bayesian networks. The new approach allows for a more advanced analysis of systemic risk transmission, including long-term and more complex relationships. The modelling reveals as relevant only relationships between the original series and one- and two-lagged series. Network structure learning displays a robust and stationary underlying risk transmission structure, pointing to a consolidated transmission mechanism of systemic credit risk between CDSs. Between 5 % and 40 % of sectoral CDS series variances are explained by the network relationships. The modelling allows us to ascertain which relationships between the CDS series show positive (amplifier) and negative (reducer) effects of systemic risk transmission.     

The affine Heston model with correlated Gaussian interest rates for pricing hybrid derivatives

In this article we define a multi-factor equity–interest rate hybrid model with non-zero correlation between the stock and interest rate. The equity part is modeled by the Heston model and we use a Gaussian multi-factor short-rate process. By construction, the model fits in the framework of affine diffusion processes, allowing fast calibration to plain vanilla options. We also provide an efficient Monte Carlo simulation scheme.     

On the determination of an optimal sample size

Abstract

In works on sample survey theory and methods the sample size is usually regarded as determined by the sampling procedure and the total cost of the survey.     

Forward and spot exchange rates in a bivariate TAR framework

Structural exchange rate models explain only a small part of the movements in dollar exchange rate. Recent empirical work has focused on the failure to account for nonlinearities in the data generating mechanism, as an explanation of this bad performance. Here two bivariate threshold autoregressive models for the spot and forward exchange rates are considered. In the first model the regimes are determined by the log difference of the two rates; in the second one the regimes are driven by the forward spot no-arbitrage condition. These processes are able to capture the ‘swing’ behaviour observed in the exchange rate market. Finally the forecasting ability of the models for the dollar/DM exchange rate is evaluated by stochastic simulation.     

A New Method for Identifying the Effects of Foreign Exchange Interventions

Central banks react even to intraday changes in the exchange rate; however, in most cases, intervention data are available only at a daily frequency. This temporal aggregation makes it difficult to identify the effects of interventions on the exchange rate. We apply the Bayesian Markov‐chain Monte Carlo (MCMC) approach to this endogeneity problem. We use “data augmentation” to obtain intraday intervention amounts and estimate the efficacy of interventions using the augmented data. Applying this new method to Japanese data, we find that an intervention of 1 trillion yen moves the yen/dollar rate by 1.8%, which is more than twice as much as the magnitude reported in previous studies applying ordinary least squares to daily observations. This shows the quantitative importance of the endogeneity problem due to temporal aggregation.