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Forecasting Volatility (forecasting + volatility)
Selected AbstractsFORECASTING VOLATILITY IN THE PRESENCE OF MODEL INSTABILITYAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2010John M. Maheu Summary Recent advances in financial econometrics have allowed for the construction of efficient,ex post,measures of daily volatility. This paper investigates the importance of instability in models of realised volatility and their corresponding forecasts. Testing for model instability is conducted with a subsampling method. We show that removing structurally unstable data of a short duration has a negligible impact on the accuracy of conditional mean forecasts of volatility. In contrast, it does provide a substantial improvement in a model's forecast density of volatility. In addition, the forecasting performance improves, often dramatically, when we evaluate models on structurally stable data. [source] Forecasting volatility with support vector machine-based GARCH modelJOURNAL OF FORECASTING, Issue 4 2010Shiyi Chen Abstract 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. [source] Forecasting volatility with noisy jumps: an application to the Dow Jones Industrial Average stocksJOURNAL OF FORECASTING, Issue 3 2008Basel M. A. Awartani Abstract Empirical high-frequency data can be used to separate the continuous and the jump components of realized volatility. This may improve on the accuracy of out-of-sample realized volatility forecasts. A further improvement may be realized by disentangling the two components using a sampling frequency at which the market microstructure effect is negligible, and this is the objective of the paper. In particular, a significant improvement in the accuracy of volatility forecasts is obtained by deriving the jump information from time intervals at which the noise effect is weak. Copyright © 2008 John Wiley & Sons, Ltd. [source] Forecasting volatility by means of threshold modelsJOURNAL OF FORECASTING, Issue 5 2007M. Pilar Muñoz Abstract The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out-of-sample forecasting ability of the SETAR-Threshold GARCH (SETAR-TGARCH) and the SETAR-Threshold Stochastic Volatility (SETAR-THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ,true' underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR-THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd. [source] Forecasting volatility for options valuationOPEC ENERGY REVIEW, Issue 3 2006Mahdjouba Belaifa The petroleum sector plays a neuralgic role in the basement of world economies, and market actors (producers, intermediates, as well as consumers) are continuously subjected to the dynamics of unstable oil market. Huge amounts are being invested along the production chain to make one barrel of crude oil available to the end user. Adding to that are the effect of geopolitical dynamics as well as geological risks as expressed in terms of low chances of successful discoveries. In addition, fiscal regimes and regulations, technology and environmental concerns are also among some of the major factors that contribute to the substantial risk in the oil industry and render the market structure vulnerable to crises. The management of these vulnerabilities require modern tools to reduce risk to a certain level, which unfortunately is a non-zero value. The aim of this paper is, therefore, to provide a modern technique to capture the oil price stochastic volatility that can be implemented to value the exposure of an investor, a company, a corporate or a Government. The paper first analyses the regional dependence on oil prices, through a historical perspective and then looks at the evolution of pricing environment since the large price jumps of the 1970s. The main causes of oil prices volatility are treated in the third part of the paper. The rest of the article deals with volatility models and forecasts used in risk management, with an implication for pricing derivatives. [source] Forecasting 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] Forecasting volatility with support vector machine-based GARCH modelJOURNAL OF FORECASTING, Issue 4 2010Shiyi Chen Abstract 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. [source] An outlier robust GARCH model and forecasting volatility of exchange rate returnsJOURNAL OF FORECASTING, Issue 5 2002Beum-Jo Park Abstract Since volatility is perceived as an explicit measure of risk, financial economists have long been concerned with accurate measures and forecasts of future volatility and, undoubtedly, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used for doing so. It appears, however, from some empirical studies that the GARCH model tends to provide poor volatility forecasts in the presence of additive outliers. To overcome the forecasting limitation, this paper proposes a robust GARCH model (RGARCH) using least absolute deviation estimation and introduces a valuable estimation method from a practical point of view. Extensive Monte Carlo experiments substantiate our conjectures. As the magnitude of the outliers increases, the one-step-ahead forecasting performance of the RGARCH model has a more significant improvement in two forecast evaluation criteria over both the standard GARCH and random walk models. Strong evidence in favour of the RGARCH model over other competitive models is based on empirical application. By using a sample of two daily exchange rate series, we find that the out-of-sample volatility forecasts of the RGARCH model are apparently superior to those of other competitive models. Copyright © 2002 John Wiley & Sons, Ltd. [source] | ||||||||||