基于组合评价的纺织行业财务风险预警模型研究

将主成分分析（PCA）和最小二乘支持向量机（LSSVM）结合,提出了一种适用小样本空间的财务风险模型：PCA—LSSVM模型。以传统年度财务指标为基础,通过主成分分析,简化了输入变量,并利用LSSVM作为判别企业风险等级的工具。该模型可以在纺织行业中的上市公司选中1个或多个企业参与,避免了传统算法模型在解决财务风险预测的缺陷。算例结果表明了所提出模型能有效地提高预警方案的可行性,为财务风险的在线实施提供了方便。     

The interest rate pass-through in the Euro area during the global financial crisis

This paper uses panel vector autoregressive (VAR) models for euro area member countries to explore the widening of retail bank interest rate spreads that emerged in the course of the global financial crisis. We find that the interest rate pass-through was generally complete on impact before the outbreak of the financial crisis, but became significantly distorted in the period thereafter, which hampered the effectiveness of monetary policy. Empirical evidence suggests that the decrease in the interest rate pass-through can be related to a change in the structural parameters characterizing the economies and a substantial increase in the average size of structural shocks. DSGE model simulations show that an increase in the frictions that banks are subject to can explain the decrease in the retail bank interest rate pass-through.     

周期视角下半导体行业股价指数波动

半导体股价指数受行业周期影响呈现周期规律,运用向量自回归模型,从宏观和行业层面出发,结合半导体周期和硅周期进行分析。实证研究表明,行业周期和股价指数周期之间存在相关性,半导体行业周期波动是半导体股价指数周期波动的核心影响因素,股价指数周期先于行业周期2～6个月,证实股票市场发挥宏观经济晴雨表功能。从行业周期视角分析股价指数波动的内在规律,对优化投资者的投资策略具有一定的参考价值。     

A nonlinear long memory model, with an application to US unemployment

Two important empirical features of US unemployment are that shocks to the series seem rather persistent and that it seems to rise faster during recessions than that it falls during expansions. To jointly capture these features of long memory and nonlinearity, we put forward a new time series model and evaluate its empirical performance. We find that the model describes the data rather well and that it outperforms related competitive models on various measures of fit.     

ECB monetary policy and commodity prices

We assess the impact of ECB monetary policy on global aggregate and sectoral commodity prices over 2001–2019. We employ an SVAR model and separately assess periods before and after the global financial crisis. Our key results indicate that contractionary monetary policy shocks have positive effects on commodity prices during both conventional and unconventional monetary policy periods, indicating the effectiveness of unconventional monetary policy tools. The largest impact is documented on energy (fuel) and food commodities. Our results also suggest that the effect of ECB monetary policy on commodity prices transmits through the exchange rate channel, which influences European market demand.     

Unconventional monetary policies and the corporate bond market

The paper uses a reduced-form vector autoregressive framework to study the effects of quantitative easing and operation “twist”, as well as a conventional monetary expansion, on corporate bond yields and spreads. We construct rating- and maturity-based weekly bond portfolios using TRACE and simulate monetary policies as shocks to the Treasury yield curve. We find that none of the policies can persistently lower corporate spreads, and that operation twist is the only policy capable of lowering corporate yields. This latter finding can be accounted for by the operation twist’s ability to keep the monetary base constant and, therefore, to flatten the riskless yield curve without generating inflationary expectations.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

基于GA-SVM模型的福建省城镇登记失业率预测

将支持向量机应用在失业率预测中,采用遗传算法对传统的支持向量机进行改进,并以福建省城镇登记失业率为对象进行仿真和预测,其结果表明,该模型具有较好的学习和泛化能力,为失业率的预测提供了一条新的途径。     

一种新型400Hz航空地面静态电源的设计

文章对400 Hz地面静态电源的主电路结构及原理、控制电路及控制算法、保护机制、散热设计等进行了全面介绍。     

A dynamic demand analysis of the United States air-passenger service

This study examines the short- and long-run effects of various determinants on the demand for US air passenger-services using the Johansen cointegration analysis and a vector error-correction (VEC) model. Results show that, in the long-run, airfare, disposable income and NASDAQ have significant effects on US air travel demand. The combined short-run dynamic effects of disposable income, NASDAQ, population and airfare jointly explain changes in air passenger-miles. Finally, we find that the 9/11 terrorist attacks drop air passenger demand by 5% during 2001:Q3-2002:Q2, which in turn pushes down the seat capacity by 4%. However, it has little impact on airfare.