Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance. © 1998 John Wiley & Sons, Ltd.     

Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models

We introduce a long‐memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid–ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid–ask spreads like the strong autocorrelation and discreteness of observations. We discuss theoretical properties of LMACP models and evaluate rolling‐window forecasts of quoted bid–ask spreads for stocks traded at NYSE and NASDAQ. We show that Poisson time series models significantly outperform forecasts from AR, ARMA, ARFIMA, ACD and FIACD models. The economic significance of our results is supported by the evaluation of a trade schedule. Scheduling trades according to spread forecasts we realize cost savings of up to 14 % of spread transaction costs. Copyright © 2013 John Wiley & Sons, Ltd.     

Contraction and volume reduction of the glycerolatedCarchesium spasmoneme: Effects of alkali earth cations

Summary The series Ca>Sr>Ba>Mg represents the relative activities of the alkali earth cations causing contraction of glycerolatedCarchesium stalks. During contraction, spasmonemal volume is reduced by 37%.The authors gratefully acknowledge the technical assistance ofR. Pimstein, R. B. Hawkes is a visiting postdoctoral research fellow of the Hebrew University, Jerusalem.     

Improving moving average trading rules with boosting and statistical learning methods

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out‐of‐sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy‐and‐hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.     

Evaluating the Rationality of Fixed-event Forecasts

A test of forecast rationality based on the weak efficiency of fixed-event forecasts was proposed by Nordhaus (1987). This paper considers the scope for pooling fixed-event forecasts across ‘events’, to deliver more powerful tests of the weak-efficiency hypothesis, when only a small number of fixed-event forecasts are available. In an empirical illustration we demonstrate the usefulness of this approach. We also suggest an interpretation of the rejection of the null hypothesis of weak efficiency in favour of negative autocorrelation in series of revisions to fixed-event forecasts. The relationship between weak efficiency and rationality when loss functions are asymmetric and prediction error variances are time-varying is also considered.© 1997 John Wiley & Sons, Ltd.     

Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias

In this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameters. Copyright © 2015 John Wiley & Sons, Ltd.     

The European Science Foundation: Excerpts from the annual report for 1987

Science Policy News

The European Science Foundation: Excerpts from the annual report for 1987     

Forecasting volatility with support vector machine‐based GARCH model

Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been successfully used for financial forecasting. This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the performance of which is compared with simple moving average, standard GARCH, nonlinear EGARCH and traditional ANN‐GARCH models by using two evaluation measures and robust Diebold–Mariano tests. The real data used in this study are daily GBP exchange rates and NYSE composite index. Empirical results from both simulation and real data reveal that, under a recursive forecasting scheme, SVM‐GARCH models significantly outperform the competing models in most situations of one‐period‐ahead volatility forecasting, which confirms the theoretical advantage of SVM. The standard GARCH model also performs well in the case of normality and large sample size, while EGARCH model is good at forecasting volatility under the high skewed distribution. The sensitivity analysis to choose SVM parameters and cross‐validation to determine the stopping point of the recurrent SVM procedure are also examined in this study. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecasting high‐frequency financial data with the ARFIMA–ARCH model

Financial data series are often described as exhibiting two non‐standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA–ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid ( 1987 ). For daily data on the Swiss 1‐month Euromarket interest rate during the period 1986–1989, the ARFIMA–ARCH (5,d,2/4) model with non‐integer d is selected by AIC. Model‐based out‐of‐sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (τ > 40). Regarding volatility forecasts, however, the selected ARFIMA–ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd.     

A new Markov regime-switching count time series approach for forecasting initial public offering volumes and detecting issue cycles

This study proposes a novel Markov regime-switching negative binomial generalized autoregressive conditional heteroskedasticity model for analyzing count data time series. We develop a likelihood-based method for parameter estimation and give the one-step-ahead forecasting algorithms for the mean, variance, and quantiles. An empirical analysis of both the U.S. initial public offering (IPO) and Chinese A-share IPO markets indicates that our method is very efficient in forecasting monthly IPO volumes and detecting hot/cold issue markets. The first-day IPO return is positively correlated with the IPO volume in a hot issue market but negatively correlated with the IPO volume in a cold issue market, in both the U.S. and Chinese IPO markets. However, the average first-day return in the previous hot issue market has a significant positive impact on the current IPO volume for only the U.S. IPO market. Our approach helps to more accurately model and understand the behavior of hot/cold IPO issue markets.     

Short-term forecasting of industrial production by means of quick indicators

This paper reports results on building transfer function models with linear combinations of quick indicators as inputs for very short-term prediction of the monthly time series of the volume of industrial production in Finland. The number of input variables in the transfer function models is reduced in two alternative ways: by replacing the original indicators by their two first principal components and by omitting certain indicators. The prediction accuracy of the transfer function models is checked outside the sample and found superior to that of corresponding ARIMA models. Neither of the two ways of reducing the number of input variables leads to consistently more accurate forecasts than the other. It is also found that the prediction accuracy of the transfer function models compares rather favourably with the preliminary values of the volume of industrial production published by the Central Statistical Office during the periods of rapid growth.     

Switzerland The Annual Report for 1987 of the Swiss National Science Foundation

Switzerland The Annual Report for 1987 of the Swiss National Science Foundation     

Forecasting Call Centre Arrivals

This article presents a novel neural network?based approach to the intra?day forecasting of call arrivals in call centres. We apply the method to individual time series of arrivals for different customer call groups. To train the model, we use historical call data from three months and, for each day, we aggregate the call volume in 288 intervals of 5 minutes. With these data, our method can be used for predicting the call volume in the next 5?minute interval using either previous real data or previous predictions to iteratively produce multi?step?ahead forecasts. We compare our approach with other conventional forecasting techniques. Experimental results provide factual evidence in favour of our approach. Copyright © 2013 John Wiley & Sons, Ltd.     

Dynamic relationship analysis of us gasoline and crude oil prices

This paper studies the dynamic relationships between US gasoline prices, crude oil prices, and the stock of gasoline. Using monthly data between January 1973 and December 1987, we find that the US gasoline price is mainly influenced by the price of crude oil. The stock of gasoline has little or no influence on the price of gasoline during the period before the second energy crisis, and seems to have some influence during the period after. We also find that the dynamic relationship between the prices of gasoline and crude oil changes over time, shifting from a longer lag response to a shorter lag response. Box-Jenkins ARIMA and transfer function models are employed in this study. These models are estimated using estimation procedure with and without outlier adjustment. For model estimation with outlier adjustment, an iterative procedure for the joint estimation of model parameters and outlier effects is employed. The forecasting performance of these models is carefully examined. For the purpose of illustration, we also analyze these time series using classical white-noise regression models. The results show the importance of using appropriate time-series methods in modeling and forecasting when the data are serially correlated. This paper also demonstrates the problems of time-series modeling when outliers are present.     

Forecasting the Antwerp maritime steel traffic flow: A case study

This paper uses multivariate time series models to specify the maritime steel traffic flow in the port of Antwerp. The time series considered are the total outgoing and total incoming maritime steel traffic and the total steel production in the EEC. The obtained time series models provide useful insight into the general behaviour of the maritime steel traffic flow during the period 1971–82. In particular, they provide a quantitative interpretation of important changes which took place in the European steel industry during that period. The multivariate time series models produce forecasts which are a substantial improvement over those obtained by univariate time series models. This is especially the case for the series of total incoming maritime steel traffic in the port of Antwerp, when differencing and transformation of the original data are applied.     

Forecasting demand in international markets: The case of correlated time series

In this paper a data analysis tool for analyzing highly correlated time series data is suggested. The main objective is to unify multiple time series into a single series and then apply a univariate method for the purpose of prediction. This method is essentially efficient for analyzing multiple time series with sparse data. Several time series data of relative demand for black and white television receivers in various countries are analyzed and quite accurate predictions are obtained.     

Components of biological,including seasonal,variation in hematological measurements and plasma fibrinogen concentrations in normal humans

This study has been carried out in order to examine the components of biologicalaand, in particular, seasonal variation in hematologic measurements in normal humans. Toward this end, 26 normal volunteers had monthly blood samplings during one calendar year for determination of number of red blood cells (RBC) and platelets, hemoglobin (Hb), hematocrit (Ht), mean corpuscular volume (MCV), MC Hb (MCH), MC Hb concentration (MCHC), RBC distribution width (RDW), mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), and plasma fibrinogen concentrations. The data were analyzed by means of spectral analyses of a group of time series or a single time series, and by means of repeated measures analyses of variance. Most of the hematologic variables show seasonal rhythms, such as annual rhythms or harmonics, which are expressed as a group phenomenon. An important part of the variance (>15%) in Ht, MCV, MCH, MCHC, RDW, number of platelets, MPV and plasma fibrinogen was explained by a yearly variation. The peak-trough differences (expressed as a percentage of the mean) in the yearly variations in number of RBC, Ht, MCV, MCH, MCHC and RDW were very low (all<8.5%). Number of platelets (14.4%) and plasma fibrinogen values (28%) showed a high-amplitude yearly variation. All hematological variables, except MCHC, show a high interindividual variability which exceeds by far the intraindividual variability.     

Merging monthly and quarterly forecasts: Experience with mqem

Forecasts from quarterly econometric models are typically revised on a monthly basis to reflect the information in current economic data. The revision process usually involves setting targets for the quarterly values of endogenous variables for which monthly observations are available and then altering the intercept terms in the quarterly forecasting model to achieve the target values. A formal statistical approach to the use of monthly data to update quarterly forecasts is described and the procedure is applied to the Michigan Quarterly Econometric Model of the US Economy. The procedure is evaluated in terms of both ex post and ex ante forecasting performance. The ex ante results for 1986 and 1987 indicate that the method is quite promising. With a few notable exceptions, the formal procedure produces forecasts of GNP growth that are very close to the published ex ante forecasts.     

Forecasting economic time series that require a power transformation: Case of state tax receipts

In this study, time series analysis is applied to the problem of forecasting state income tax receipts. The data series is of special interest since it exhibits a strong trend with a high multiplicative seasonal component. An appropriate model is identified by simultaneous estimation of the parameters of the power transformation and the ARMA model using the Schwarz (1978) Bayesian information criterion. The forecasting performance of the time series model obtained from this procedure is compared with alternative time series and regression models. The study illustrates how an information criterion can be employed for identifying time series models that require a power transformation, as exemplified by state tax receipts. It also establishes time series analysis as a viable technique for forecasting state tax receipts.     

Seasonal adjustment and kalman filtering: Extension to periodic variances

This paper reviews the relations between the methods of seasonal adjustment used by official statistical agencies and the ‘model-based’ methods that postulate explicit stochastic models for the unobserved components of a time series and apply optimal signal extraction theory to obtain a seasonally adjusted series. The Kalman filter implementation of the model-based methods is described and some recent results on its properties are reviewed. The model-based methods employ homogeneous or time-invariant models that assume in particular that the autocovariance structure does not vary with the season. Relaxing this leads to the class of models known as periodic models, and an example of a seasonally heterosceclastic unobserved-components ARIMA (SHUCARIMA) model is presented. The calculation of the standard error of a seasonally adjusted series via the Kalman filter is extended to this periodic model and illustrated for a monthly rainfall series.