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Forecasting Horizons (forecasting + horizon)
Selected AbstractsForecasting volatility: Roles of sampling frequency and forecasting horizon,THE JOURNAL OF FUTURES MARKETS, Issue 12 2010Wing Hong Chan This study empirically tests how and to what extent the choice of the sampling frequency, the realized volatility (RV) measure, the forecasting horizon and the time-series model affect the quality of volatility forecasting. Using highly synchronous executable quotes retrieved from an electronic trading platform, the study avoids the influence of various market microstructure factors in measuring RV with high-frequency intraday data and in inferring implied volatility (IV) from option prices. The study shows that excluding non-trading-time volatility produces significant downward bias of RV by as much as 36%. Quality of prediction is significantly affected by the forecasting horizon and RV model, but is largely immune from the choice of sampling frequency. Consistent with prior research, IV outperforms time-series forecasts; however, the information content of historical volatility critically depends on the choice of RV measure. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark [source] An empirical investigation of the GARCH option pricing model: Hedging performanceTHE JOURNAL OF FUTURES MARKETS, Issue 12 2003Haynes H. M. Yung In this article, we study the empirical performance of the GARCH option pricing model relative to the ad hoc Black-Scholes (BS) model of Dumas, Fleming, and Whaley. Specifically, we investigate the empirical performance of the option pricing model based on the exponential GARCH (EGARCH) process of Nelson. Using S&P 500 options data, we find that the EGARCH model performs better than the ad hoc BS model both in terms of in-sample valuation and out-of-sample forecasting. However, the superiority of out-of-sample performance EGARCH model over the ad hoc BS model is small and insignificant except in the case of deep-out-of-money put options. The out-performance diminishes as one lengthens the forecasting horizon. Interestingly, we find that the more complicated EGARCH model performs worse than the ad hoc BS model in hedging, irrespective of moneyness categories and hedging horizons. For at-the-money and out-of-the-money put options, the underperformance of the EGARCH model in hedging is statistically significant. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:1191,1207, 2003 [source] EXPONENTIAL SMOOTHING AND NON-NEGATIVE DATAAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 4 2009Muhammad Akram Summary The most common forecasting methods in business are based on exponential smoothing, and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to non-negative data. We explore exponential smoothing state space models for non-negative data under various assumptions about the innovations, or error, process. We first demonstrate that prediction distributions from some commonly used state space models may have an infinite variance beyond a certain forecasting horizon. For multiplicative error models that do not have this flaw, we show that sample paths will converge almost surely to zero even when the error distribution is non-Gaussian. We propose a new model with similar properties to exponential smoothing, but which does not have these problems, and we develop some distributional properties for our new model. We then explore the implications of our results for inference, and compare the short-term forecasting performance of the various models using data on the weekly sales of over 300 items of costume jewelry. The main findings of the research are that the Gaussian approximation is adequate for estimation and one-step-ahead forecasting. However, as the forecasting horizon increases, the approximate prediction intervals become increasingly problematic. When the model is to be used for simulation purposes, a suitably specified scheme must be employed. [source] On the use of reactive power as an endogenous variable in short-term load forecastingINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 5 2003P. Jorge Santos Abstract In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on variables, which are transversal to most models. Among the most significant variables we can find load, expressed as active power (MW), as well as exogenous variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting endogenous variable. The inclusion of this additional variable is mainly due to the evidence that correlations between load and weather variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd. [source] Extended evidence on the use of technical analysis in foreign exchangeINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 4 2006Thomas Gehrig Abstract This work extends earlier survey studies on the use of technical analysis by considering flow analysis as a third form of information production. Moreover, the survey covers FX dealers and also the rising fund managers. Technical analysis has gained importance over time and is now the most equally spread kind of analysis. It has by far the greatest importance in FX dealing and is second in fund management. Charts are used for shorter-term forecasting horizons while flows dominate at the shortest-term and fundamentals at longer horizons. Preferred users of each kind of analysis exhibit different views about market frictions. Copyright © 2006 John Wiley & Sons, Ltd. [source] Long-term sales forecasting using holt,winters and neural network methodsJOURNAL OF FORECASTING, Issue 5 2005Apostolos Kotsialos Abstract The problem of medium to long-term sales forecasting raises a number of requirements that must be suitably addressed in the design of the employed forecasting methods. These include long forecasting horizons (up to 52 periods ahead), a high number of quantities to be forecasted, which limits the possibility of human intervention, frequent introduction of new articles (for which no past sales are available for parameter calibration) and withdrawal of running articles. The problem has been tackled by use of a damped-trend Holt,Winters method as well as feedforward multilayer neural networks (FMNNs) applied to sales data from two German companies. Copyright © 2005 John Wiley & Sons, Ltd. [source] A classifying procedure for signalling turning pointsJOURNAL OF FORECASTING, Issue 3 2004Lasse Koskinen Abstract A Hidden Markov Model (HMM) is used to classify an out-of-sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead of estimating past turning points using maximum likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method, here called a ,Markov Bayesian Classifier (MBC)', is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourably with earlier HMM studies. Some theoretical arguments for this are given. Copyright © 2004 John Wiley & Sons, Ltd. [source] Efficiency tests of agricultural commodity futures markets in ChinaAUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 2 2005H. Holly Wang The efficiency of the Chinese wheat and soybean futures markets is studied. Formal statistical tests were conducted based on Johansen's cointegration approach for three different cash markets and six different futures forecasting horizons ranging from 1 week to 4 months. The results suggest a long-term equilibrium relationship between the futures price and cash price for soybeans and weak short-term efficiency in the soybean futures market. The futures market for wheat is inefficient, which may be caused by over-speculation and government intervention. [source] | ||||||||||