Are combination forecasts of S&amp;P 500 volatility statistically superior?

Becker, Ralf; Clements, Adam

doi:10.1016/j.ijforecast.2007.09.001

Cited by 75 publications

(43 citation statements)

References 26 publications

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“…Then, we use these results to form and indicator function and then to estimate the time-varying combinations, following equation (6). Finally, we evaluate the performance of the proposed time-varying composite forecasts.…”

Section: Conditional Combinationsmentioning

confidence: 99%

Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts

Benavides¹,

Capistrán²

2012

Journal of Empirical Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: This paper provides empirical evidence that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, unconditional combinations, and hybrid forecasts. Superior forecasting performance is achieved by both, taking into account the conditional expected performance of each model given current information, and combining individual forecasts. The method used in this paper to produce conditional combinations extends the application of conditional predictive ability tests to select forecast combinations. The application is for volatility forecasts of the Mexican Peso-US Dollar exchange rate, where realized volatility calculated using intra-day data is used as a proxy for the (latent) daily volatility. Terms of use: Documents in

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Section: Conditional Combinationsmentioning

confidence: 99%

Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts

Benavides¹,

Capistrán²

2012

Journal of Empirical Finance

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“…In contrast to the general conclusions, Canina and Figlewski [16] examined volatility implied in S&P 100 index options from 1983 to 1987 and suggested that implied volatility is a poor indicator of subsequently realized volatility. Becker and Clements [17] indicated that combinations of various model-based forecasts of S&P 500 realized volatility outperformed its implied volatility index-VIX. Outside of the US, recent empirical findings were mixed and suggested a slight lead of implied volatility.…”

Section: Introductionmentioning

confidence: 99%

The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

Chen

2015

Ann. Data. Sci.

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Crude oil volatility index (OVX) is a new index published by Chicago BoardOption Exchange since 2007. In recent years it emerged as an important alternative measure to track and analyze the volatility of future oil prices. In this paper we firstly model and analyze the dynamic relationship between OVX changes and future crude oil price returns with time-varying coefficients, modeled using the Kalman filter, in the regression models. Empirical results show a weak negative relationship between OVX changes and future crude oil price returns movement, and extremely high/low levels of OVX cannot predict future positive/negative returns well. Secondly, this paper explores whether OVX can predict future realized volatility of crude oil price returns . The empirical findings suggest that OVX serves as an unbiased but not an efficient estimate of the future realized volatility and it includes information of the future realized volatility. Finally the incorporation of information of OVX in measuring market risk is analyzed. The empirical result indicates that Kalman filter based model provides the improved performance than the linear regression model in terms of forecasting accuracy for realized volatility prediction and the reliability for VaR estimate. B Kaijian He

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“…Another interesting type of benchmark that could be considered is models for the realized volatility or a combination of such models, see e.g. Becker and Clements (2008). Parameter estimates and standard errors for these three models are given in the last six columns of Table 4.…”

Section: Benchmark Modelsmentioning

confidence: 99%

Option pricing with asymmetric heteroskedastic normal mixture models

Rombouts

Stentoft

2015

International Journal of Forecasting

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a b s t r a c tWe propose an asymmetric GARCH in mean mixture model and provide a feasible method for option pricing within this general framework by deriving the appropriate risk neutral dynamics. We forecast the out-of-sample prices of a large sample of options on the S&P 500 index from January 2006 to December 2011, and compute dollar losses and implied standard deviation losses. We compare our results to those of existing mixture models and other benchmarks like component models and jump models. Using the model confidence set test, the overall dollar root mean squared error of the best performing benchmark model is significantly larger than that of the best mixture model.

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Are combination forecasts of S&P 500 volatility statistically superior?

Cited by 75 publications

References 26 publications

Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts

Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts

The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

Option pricing with asymmetric heteroskedastic normal mixture models

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