Shaping the manufacturing industry performance: MIDAS approach

We aim to find out whether the exchange rate (against US dollar) or the interest rate (in local currency) is a better variable in predicting the capacity utilization rate of manufacturing industry (CUR) of Turkey after the 2008 global financial crisis. In that manner, we implement dynamic mixed data sampling (MIDAS) regression model to forecast monthly changes in CUR by using daily changes in the exchange rate and the interest rate separately. The results show that exchange rate has a better forecast performance suggesting that it is a stronger determinant in shaping the manufacturing industry.     

Time series forecasting with neural network ensembles: an application for exchange rate prediction

This paper investigates the use of neural network combining methods to improve time series forecasting performance of the traditional single keep-the-best (KTB) model. The ensemble methods are applied to the difficult problem of exchange rate forecasting. Two general approaches to combining neural networks are proposed and examined in predicting the exchange rate between the British pound and US dollar. Specifically, we propose to use systematic and serial partitioning methods to build neural network ensembles for time series forecasting. It is found that the basic ensemble approach created with non-varying network architectures trained using different initial random weights is not effective in improving the accuracy of prediction while ensemble models consisting of different neural network structures can consistently outperform predictions of the single ‘best’ network. Results also show that neural ensembles based on different partitions of the data are more effective than those developed with the full training data in out-of-sample forecasting. Moreover, reducing correlation among forecasts made by the ensemble members by utilizing data partitioning techniques is the key to success for the neural ensemble models. Although our ensemble methods show considerable advantages over the traditional KTB approach, they do not have significant improvement compared to the widely used random walk model in exchange rate forecasting.     

基于删失数据的ARMA模型参数估计与实证分析

利用生存分析中的K-M估计得到了删失数据下ARMA模型的参数估计,通过与完全数据下的参数估计进行对比,充分说明了该估计的效果.利用删失数据下ARMA模型的EM算法,对2013年5月2日到2014年5月8日的247个美元兑人民币的基准汇率数据进行建模分析和预测,并与实际数据进行对照,误差较小,说明估计和EM预测方法的可行性.     

大型海湾水交换计算中随机游动方法的应用研究

水交换矩阵是反映大型海湾不同子区域之间水交换关系的有效工具,该文对随机游动方法在水交换矩阵计算中的应用进行了研究．通过与对流扩散模型的对比,指出用随机游动方法计算水交换矩阵比用对流扩散方程求解更快更灵活．同时,为了对不同区域之间的水交换特性进行长期快速预测,采用了Thompson提出的预测矩阵方法．通过理论分析得出,影响该矩阵预测效果的因素主要有:粒子数目、分区数和预测矩阵取值时间．以一个矩形海湾为例,将数值模拟结果和预测矩阵预测结果进行对比,分析了以上各因素对预测效果的影响．结果表明,粒子数目越多,预测矩阵取值时间越长,预测矩阵的预测效果越好．在粒子数目足够多的情形下,分区数越多,预测效果越好．相反,粒子数目如果较少,过多的分区数将可能导致预测矩阵效果太差而不可用．     

基于IGRA-ISVD约减和RBF的森林火灾消防直升机需求预测模型

针对森林火灾消防直升机需求预测问题,提出了一种基于改进灰色关联分析(IGRA)和改进奇异值分解(ISVD)约简的径向基函数(RBF)神经网络预测模型.首先,基于既有研究梳理了森林火灾消防直升机需求预测指标体系;然后,在改进灰色关联分析和奇异值分解方法的基础上,分别对消防直升机需求预测数据信息进行属性约简和维度约简;最后,利用约简预测数据信息对RBF神经网络进行训练,进而构建消防直升机数量预测模型.案例分析和对比分析表明了本文所提方法的可行性和合理性.     

The efficiency of financial futures markets: Tests of prediction accuracy

The prices of financial futures contracts can be interpreted as forecasts of the spot rates, which will apply at the final delivery date of that contract. Financial futures contracts have been traded daily since the early 1980s and provide a substantial bank of data to test the forecasting efficiency of such contracts. Tests are carried out to examine whether the interest rates implied by the futures price for eurodollar and short sterling contracts are cointegrated with the final settlement price over forecasting horizons of 1, 2 and 3 months. Similar analysis is carried out for the yen/dollar exchange rate futures contract. The paper then examines the forecasting performance of the three contracts over the forecasting horizons of 1, 2 and 3 months and in particular whether the forecasts implied by the futures contract provide better predictions than the naı̈ve no-change (i.e. random walk), a vector error correction model (VECM) or an ARIMA model.An examination of the relative efficiency of the markets for the three markets over the three time horizons is carried out and finally trading strategies are simulated to see whether excess profits can be achieved. In fact the results suggest that both profits and losses would be attracted.     

Developing an early warning system to predict currency crises

The purpose of this paper is to develop an early warning system to predict currency crises. In this study, a data set covering the period of January 1992–December 2011 of Turkish economy is used, and an early warning system is developed with artificial neural networks (ANN), decision trees, and logistic regression models. Financial Pressure Index (FPI) is an aggregated value, composed of the percentage changes in dollar exchange rate, gross foreign exchange reserves of the Central Bank, and overnight interest rate. In this study, FPI is the dependent variable, and thirty-two macroeconomic indicators are the independent variables. Three models, which are tested in Turkish crisis cases, have given clear signals that predicted the 1994 and 2001 crises 12 months earlier. Considering all three prediction model results, Turkey’s economy is not expected to have a currency crisis (ceteris paribus) until the end of 2012. This study presents uniqueness in that decision support model developed in this study uses basic macroeconomic indicators to predict crises up to a year before they actually happened with an accuracy rate of approximately 95%. It also ranks the leading factors of currency crisis with regard to their importance in predicting the crisis.     

Expectation formation in an experimental foreign exchange market

Participants of an experimental foreign exchange market forecast an exchange rate with an unknown price reaction function. Aggregate demand is derived from their own forecasts and random shocks. Our experimental results indicate that the expectations of the subjects tend to be coordinated on a common prediction strategy. This strategy is best described as a trend-extrapolative, destabilizing expectation formation scheme. Deviations from common expectations are mainly caused by random shocks, which can be ascribed to the similarity of the subjects’ behavior within and between the different markets. The findings can be explained using insights of behavioral economics.     

A non random walk theory of exchange rate dynamics with applications to option pricing

A two dimensional stochastic process is developed to model exchange rate dynamics. We incorporate the non random walk influence of pur–chasing power parity, to synthesise the theories of international trade and foreign currency options. Our results, which include a closed form expression for the transition density function of the exchange rate and an exact formula to price currency options, offer a theoretical framework for further study of foreign exchange markets     

Zeno's walk: A random walk with refinements

Summary. A self-modifying random walk on is derived from an ordinary random walk on the integers by interpolating a new vertex into each edge as it is crossed. This process converges almost surely to a random variable which is totally singular with respect to Lebesgue measure, and which is supported on a subset of having Hausdorff dimension less than , which we calculate by a theorem of Billingsley. By generating function techniques we then calculate the exponential rate of convergence of the process to its limit point, which may be taken as a bound for the convergence of the measure in the Wasserstein metric. We describe how the process may viewed as a random walk on the space of monotone piecewise linear functions, where moves are taken by successive compositions with a randomly chosen such function. Received: 20 November 1995 / In revised form: 14 May 1996     

基于HP滤波和ARMA-GARCH模型的人民币汇率趋势预测

在复杂多变的金融市场,人民币汇率的变化受多种因素的影响,而人民币汇率的变化又影响着经济生活的方方面面,人民币汇率及其变化特征受到人们的广泛关注,研究人民币汇率变化特征,正确分析与预测人民币汇率的走势,对于国家和各个经济主体制定金融政策和投资决策具有十分重要的意义,采用HP滤波法将汇率数据序列分解为趋势成分序列和波动成分序列,然后使用自回归和ARMA-GARCH模型分别进行拟合和预测,通过实证分析发现模型有着较好的预测效果,可以为金融产品的预测研究和制定金融政策提供参考。     

Treasury Management Model with Foreign Exchange Exposure

In this paper we formulate a model for foreign exchange exposure management and (international) cash management taking into consideration random fluctuations of exchange rates. A vector error correction model (VECM) is used to predict the random behaviour of the forward as well as spot rates connecting dollar and sterling. A two-stage stochastic programming (TWOSP) decision model is formulated using these random parameter values. This model computes currency hedging strategies, which provide rolling decisions of how much forward contracts should be bought and how much should be liquidated.The model decisions are investigated through ex post simulation and backtesting in which value at risk (VaR) for alternative decisions are computed. The investigation (a) shows that there is a considerable improvement to “spot only” strategy, (b) provides insight into how these decisions are made and (c) also validates the performance of this model.     

Bay of Bengal wave forecast based on genetic algorithm: A comparison of univariate and multivariate approaches

Prediction of significant wave height (SWH) field is carried out in the Bay of Bengal (BOB) using a combination of empirical orthogonal function (EOF) analysis and genetic algorithm (GA). EOF analysis is performed on 4 years (2005–2008) of numerical wave model generated SWH field, and analyzed fields of zonal (U) and meridional (V) winds. This is to decompose the space-time distributed data into spatial modes ranked by their temporal variances. Two different variants of GA are tested. In the first one, univariate GA is applied to the time series of the first principal component (PC) of SWH in the training dataset after a filtering with singular spectrum analysis (SSA) for effecting noise reduction. The generated equations are used to carry out forecast of SWH field with various lead times. In the second method, multivariate GA is applied to the SSA filtered time series of the first PC of SWH, and time- lagged first PCs of U and V and again forecast equations are generated. Once again the forecast of SWH is carried out with same lead times. The quality of forecast is evaluated in terms of root mean square error of forecast. The results are also compared with buoy data at a location. It is concluded that the method can serve as a cost-effective alternate prediction technique in the BOB.     

Strong dependence in the nominal exchange rates of the Polish zloty

We examine the nominal exchange rates of six currencies (Canadian, Australian and U.S. dollars, euro, Japanese yen and U.K. pound) against the Polish zloty by means of statistical techniques based on unit roots and other long memory processes. We use both parametric and semiparametric methods for estimating and testing integer and fractional orders of integration at the long run or zero frequency. The results show that unit roots are likely to occur in relation with the U.S. and the Canadian dollars, the Japanese yen and the U.K. pound. However, for the Australian dollar and the euro, this hypothesis is rejected in favour of smaller degrees of integration, implying mean reversion in their behaviour. Thus, for the former currencies, in the event of an exogenous shock affecting the exchange rates, strong policy actions must be required to bring the variables back to their original levels. On the other hand, for the Australian dollar and the euro, there exists less need of action since the series will return to their levels sometime in the future. Copyright © 2006 John Wiley & Sons, Ltd.     

Non-stationarity in Sugar Prices

Time series analysis of unstable series such as raw material prices contains several methodological pitfalls. These are illustrated by a survey of the Box-Jenkins method of analysis applied to 12 years of daily sugar prices.Two problems in particular have received insufficient attention in previous analyses. First, the model variance fluctuates substantially over time. The logarithmic transformation of prices is recommended as significantly reducing the fluctuations. Second, it is essential to study the constant term in the Box-Jenkins model, which is shown to differ significantly from zero. This proves the existence of price trends. The commonly accepted random walk model for commodity prices is thus incorrect, at least for sugar.However, these price trends vary over time and this possibility is not covered by the Box-Jenkins models. Consequently, a new model for price movements is proposed, characterized by frequent changes in the daily price trend yet consistent with the results of the autocorrelation analysis.     

GARCH(1,1)模型及其在汇率条件波动预测中的应用

检验人民币/日元汇率与波动的时间序列特征,证实存在简单单位根过程及条件异方差性。计算表明,其汇率变化率的ARMA及ARMA/GARCH组合模型的建模不成立,GARCH、EGARCH、IGARCH模型的建模效果接近,且GARCH(1,1)拟合效果最好。GARCH(1,1)模型的跨度为一年的样本外条件异方差预测,显示出该年末汇率的震荡,与实际情况一致。GARCH(1,1)是汇率数据建娱的首选模型。     

基于GARCH模型的中美汇率实证分析

汇率制度改革后,加强人民币汇率风险管理已成为摆在各大经济主体面前的重大课题.基于2010年1月1日至2012年5月10日的美元对人民币日汇率值,利用广义条件异方差自回归(GARCH)模型,对中美汇率日数据进行处理与检验,得到了残差存在异方差性.在此基础上建立了汇率GARCH模型,实证分析表明精确性高.     

基于MCMC算法的人民币汇率市场的分析——双门限非对称GARCH模型的应用

本文旨在考察,汇改后美元/人民币汇率前期收益的影响下,人民币汇率市场上非美元/人民币汇率收益均值和波动不对称的程度。为了捕捉非美元汇率收益的均值和波动不对称的特点,我们设定双门限非线性的GARCH模型,结合GJR效应(即加入非美元收益利空或利好消息的影响),利用基于MCMC算法的贝叶斯推断来完成。应用中我们选取了美元(欧元、日元、港元)/人民币日汇率数据进行分析,发现了门限非线性的结果,表明在美元和非美元汇率本身双重变化的影响下,非美元汇率收益的均值和波动同时表现出非对称的特点。并且在美元收益利好消息的影响下,美元汇率对非美元汇率的溢出效应明显增强,非美元表现出低均值回归的特点。     

人民币汇率对我国进出口的影响研究

自2005年7月人民币汇率制度改革至今,人民币对美元汇率不断升值.人民币的汇率升值作为一种对国内及国际经济的调节方式,对各行业经济发展的影响应该都是巨大的,尤其是对进出口的影响更是首当其中.基于2005年1月—2008年3月进出口、人民币对美元、欧元汇率等各项指标数据和VAR模型,得到了人民币汇率与进出口关系的向量误差修正模型,很好地解释了汇率变化对我国进出口行业的影响.     

Modeling stock index returns by means of partial least‐squares methods: An out‐of‐sample analysis for three stock markets

We analyze the underlying economic forces of the stock markets in Germany, the U.K. and the U.S. Identifying a number of variables evincing return predictability, we follow a partial least‐squares (PLS) approach to combine these observables into a few latent factors. Conditional on European markets, our findings indicate (i) superior prediction performance of PLS‐based schemes in comparison with both, a random walk and a first‐order autoregressive benchmark model, (ii) consistent profitable trading on the German and British market, (iii) profitable linear forecast combinations, (iv) the U.S. stock market is diagnosed as informationally efficient. Copyright © 2010 John Wiley & Sons, Ltd.