共查询到20条相似文献，搜索用时 15 毫秒
1.
Kerry Patterson 《Journal of forecasting》2002,21(4):245-264
Much published data is subject to a process of revision due, for example, to additional source data, which generates multiple vintages of data on the same generic variable, a process termed the data measurement process or DMP. This article is concerned with several interrelated aspects of the DMP for UK Gross National Product. Relevant questions include the following. Is the DMP well behaved in the sense of providing a single stochastic trend in the vector time series of vintages? Is one of the vintages of data, for example the ‘final’, the sole vintage generating the long‐memory component? Does the multivariate framework proposed here add to the debate on the existence of a unit root in GNP? The likely implicit assumptions of users (that the DMP is well behaved and the final vintage is ‘best’) can be cast in terms of testable hypotheses; and we show that these ‘standard’ assumptions have not always been empirically founded. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
2.
This paper discusses the asymptotic efficiency of estimators for optimal portfolios when returns are vector‐valued non‐Gaussian stationary processes. We give the asymptotic distribution of portfolio estimators ? for non‐Gaussian dependent return processes. Next we address the problem of asymptotic efficiency for the class of estimators ?. First, it is shown that there are some cases when the asymptotic variance of ? under non‐Gaussianity can be smaller than that under Gaussianity. The result shows that non‐Gaussianity of the returns does not always affect the efficiency badly. Second, we give a necessary and sufficient condition for ? to be asymptotically efficient when the return process is Gaussian, which shows that ? is not asymptotically efficient generally. From this point of view we propose to use maximum likelihood type estimators for g, which are asymptotically efficient. Furthermore, we investigate the problem of predicting the one‐step‐ahead optimal portfolio return by the estimated portfolio based on ? and examine the mean squares prediction error. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
3.
Interest in online auctions has been growing in recent years. There is an extensive literature on this topic, whereas modeling online auction price process constitutes one of the most active research areas. Most of the research, however, only focuses on modeling price curves, ignoring the bidding process. In this paper, a semiparametric regression model is proposed to model the online auction process. This model captures two main features of online auction data: changing arrival rates of bidding processes and changing dynamics of prices. A new inference procedure using B‐splines is also established for parameter estimation. The proposed model is used to forecast the price of an online auction. The advantage of this proposed approach is that the price can be forecast dynamically and the prediction can be updated according to newly arriving information. The model is applied to Xbox data with satisfactory forecasting properties. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
4.
In recent years there has been a considerable development in modelling non‐linearities and asymmetries in economic and financial variables. The aim of the current paper is to compare the forecasting performance of different models for the returns of three of the most traded exchange rates in terms of the US dollar, namely the French franc (FF/$), the German mark (DM/$) and the Japanese yen (Y/$). The relative performance of non‐linear models of the SETAR, STAR and GARCH types is contrasted with their linear counterparts. The results show that if attention is restricted to mean square forecast errors, the performance of the models, when distinguishable, tends to favour the linear models. The forecast performance of the models is evaluated also conditional on the regime at the forecast origin and on density forecasts. This analysis produces more evidence of forecasting gains from non‐linear models. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
5.
More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure. 相似文献
6.
In the case of US national accounts the data are revised for the first few years and every decade, which implies that we do not really have the final data. In this paper we aim to predict the final data, using the preliminary data and/or the revised data. The following predictors are introduced and derived from a context of the non-linear filtering or smoothing problem, which are: (1) prediction of the final data of time t given the preliminary data up to time t- 1, (2) prediction of the final data of time t given the preliminary data up to time t, (3) prediction of the final data of time t given the preliminary data up to time T, (4) prediction of the final data of time t given the revised data up to time t -1 and the preliminary data up to time t- 1, and (5) prediction of the final data of time t given the revised data up to time t-1 and the preliminary data up to time t. It is shown that (5) is the best predictor but not too different from (3). The prediction problem is illustrated using US per capita consumption data. 相似文献
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8.
Let {Xt} be a stationary process with spectral density g(λ).It is often that the true structure g(λ) is not completely specified. This paper discusses the problem of misspecified prediction when a conjectured spectral density fθ(λ), θ∈Θ, is fitted to g(λ). Then, constructing the best linear predictor based on fθ(λ), we can evaluate the prediction error M(θ). Since θ is unknown we estimate it by a quasi‐MLE . The second‐order asymptotic approximation of is given. This result is extended to the case when Xt contains some trend, i.e. a time series regression model. These results are very general. Furthermore we evaluate the second‐order asymptotic approximation of for a time series regression model having a long‐memory residual process with the true spectral density g(λ). Since the general formulae of the approximated prediction error are complicated, we provide some numerical examples. Then we illuminate unexpected effects from the misspecification of spectra. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
9.
Juanjuan Wang;Shujie Zhou;Wentong Liu;Lin Jiang; 《Journal of forecasting》2024,43(6):1998-2020
Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality-based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions. 相似文献
10.
Chris Brooks 《Journal of forecasting》2001,20(2):135-143
This paper combines and generalizes a number of recent time series models of daily exchange rate series by using a SETAR model which also allows the variance equation of a GARCH specification for the error terms to be drawn from more than one regime. An application of the model to the French Franc/Deutschmark exchange rate demonstrates that out‐of‐sample forecasts for the exchange rate volatility are also improved when the restriction that the data it is drawn from a single regime is removed. This result highlights the importance of considering both types of regime shift (i.e. thresholds in variance as well as in mean) when analysing financial time series. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
11.
Michael P. Clements Philip Hans Franses Jeremy Smith Dick van Dijk 《Journal of forecasting》2003,22(5):359-375
We compare linear autoregressive (AR) models and self‐exciting threshold autoregressive (SETAR) models in terms of their point forecast performance, and their ability to characterize the uncertainty surrounding those forecasts, i.e. interval or density forecasts. A two‐regime SETAR process is used as the data‐generating process in an extensive set of Monte Carlo simulations, and we consider the discriminatory power of recently developed methods of forecast evaluation for different degrees of non‐linearity. We find that the interval and density evaluation methods are unlikely to show the linear model to be deficient on samples of the size typical for macroeconomic data. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
12.
As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies. 相似文献
13.
In this paper we introduce a new testing procedure for evaluating the rationality of fixed‐event forecasts based on a pseudo‐maximum likelihood estimator. The procedure is designed to be robust to departures in the normality assumption. A model is introduced to show that such departures are likely when forecasters experience a credibility loss when they make large changes to their forecasts. The test is illustrated using monthly fixed‐event forecasts produced by four UK institutions. Use of the robust test leads to the conclusion that certain forecasts are rational while use of the Gaussian‐based test implies that certain forecasts are irrational. The difference in the results is due to the nature of the underlying data. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
14.
In this article we propose an extension of singular spectrum analysis for interval-valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set-valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund. 相似文献
15.
As a consequence of recent technological advances and the proliferation of algorithmic and high‐frequency trading, the cost of trading in financial markets has irrevocably changed. One important change, known as price impact, relates to how trading affects prices. Price impact represents the largest cost associated with trading. Forecasting price impact is very important as it can provide estimates of trading profits after costs and also suggest optimal execution strategies. Although several models have recently been developed which may forecast the immediate price impact of individual trades, limited work has been done to compare their relative performance. We provide a comprehensive performance evaluation of these models and test for statistically significant outperformance amongst candidate models using out‐of‐sample forecasts. We find that normalizing price impact by its average value significantly enhances the performance of traditional non‐normalized models as the normalization factor captures some of the dynamics of price impact. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
16.
利用聚类算法预测股票的价格趋势,通过聚类技术先将某些具备相似特征的上市公司提取出来,这些公司的股票趋势往往具有相饭性,此时再对这些提取出的上市公司财务报表进行具体分析,从而达到准确预测该上市公司股票趋势的目的。通过测试结果得出此方法在股票的预测中具有一定的应用前景。 相似文献
17.
This paper applies the GARCH‐MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short‐term and long‐term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low‐frequency macroeconomic information in the GARCH‐MIDAS model improves the prediction ability of the model, particularly for the long‐term variance component. Moreover, the GARCH‐MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
18.
In this paper we consider a novel procedure to forecasting the US zero coupon bond yields for a continuum of maturities by using the methodology of nonparametric functional data analysis (NP‐FDA). We interpret the US yields as curves since the term structure of interest rates defines a relation between the yield of a bond and its maturity. Within the NP‐FDA approach, each curve is viewed as a functional random variable and the dynamics present in the sample are modeled without imposing any parametric structure. In order to evaluate forecast the performance of the proposed estimator, we consider forecast horizons h = 1,3,6,12… months and the results are compared with widely known benchmark models. Our estimates with NP‐FDA present predictive performance superior to its competitors in many situations considered, especially for short‐term maturities. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
19.
This paper explores the relationship between the Australian real estate and equity market between 1980 and 1999. The results from this study show three specific outcomes that extend the current literature on real estate finance. First, it is shown that structural shifts in stock and property markets can lead to the emergence of an unstable linear relationship between these markets. That is, full‐sample results support bi‐directional Granger causality between equity and real estate returns, whereas when sub‐samples are chosen that account for structural shifts the results generally show that changes within stock market prices influence real estate market returns, but not vice versa. Second, the results also indicate that non‐linear causality tests show a strong unidirectional relationship running from the stock market to the real estate market. Finally, from this empirical evidence a trading strategy is developed which offers superior performance when compared to adopting a passive strategy for investing in Australian securitized property. These results appear to have important implications for managing property assets in the funds management industry and also for the pricing efficiency within the Australian property market. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
20.
John L. Turner 《Journal of forecasting》2004,23(7):513-539
This article introduces a novel framework for analysing long‐horizon forecasting of the near non‐stationary AR(1) model. Using the local to unity specification of the autoregressive parameter, I derive the asymptotic distributions of long‐horizon forecast errors both for the unrestricted AR(1), estimated using an ordinary least squares (OLS) regression, and for the random walk (RW). I then identify functions, relating local to unity ‘drift’ to forecast horizon, such that OLS and RW forecasts share the same expected square error. OLS forecasts are preferred on one side of these ‘forecasting thresholds’, while RW forecasts are preferred on the other. In addition to explaining the relative performance of forecasts from these two models, these thresholds prove useful in developing model selection criteria that help a forecaster reduce error. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
