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Forecasting the volatility of Nikkei 225 futures

Asai, Manabu; McAleer, Michael - 2017 - Revised: January 2017

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting …

Persistent link: https://www.econbiz.de/10011590424

Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity

Martens, Martin; van Dijk, Dick; de Pooter, Michiel - 2004

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week...

Persistent link: https://www.econbiz.de/10010325218

Finite sample accuracy of integrated volatility estimators

Nielsen, Morten Ørregaard; Houmann Frederiksen, Per - 2005

We consider the properties of three estimation methods for integrated volatility, i.e. realized volatility, the Fourier estimator, and the wavelet estimator, when a typical sample of high-frequency data is observed. We employ several different generating mechanisms for the instantaneous...

Persistent link: https://www.econbiz.de/10010290394

Comparing predictive accuracy under long memory : with an application to volatility forecasting

Kruse, Robinson; Leschinski, Christian; Will, Michael - 2016

This paper extends the popular Diebold-Mariano test to situations when the forecast error loss differential exhibits long memory. It is shown that this situation can arise frequently, since long memory can be transmitted from forecasts and the forecast objective to forecast error loss...

Persistent link: https://www.econbiz.de/10011430242

Modeling and forecasting S&P 500 volatility : long memory, structural breaks and nonlinearity

Martens, Martin; Dijk, Dick van; Pooter, Michiel de - 2004

Persistent link: https://www.econbiz.de/10011335205

The memory of volatility

Wenger, Kai; Leschinski, Christian; Sibbertsen, Philipp - 2017 - This version: July 31, 2017

The focus of the volatility literature on forecasting and the predominance of the conceptually simpler HAR model over …

Persistent link: https://www.econbiz.de/10011715842

Long memory, realized volatility and HAR models

Baillie, Richard; Calonaci, Fabio; Cho, Dooyeon; Rho, … - 2019

The presence of long memory in Realized Volatility (RV) is a widespread stylized fact. The origins of long memory in RV have been attributed to jumps, structural breaks, non-linearities, or pure long memory. An important development has been the Heterogeneous Autoregressive (HAR) model and its...

Persistent link: https://www.econbiz.de/10011964976

Realized Volatility Risk

Allen, David E.; McAleer, Michael; Scharth, Marcel - 2013

of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting …

Persistent link: https://www.econbiz.de/10010326350

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

forecasting volatility. Key papers in this area include Andersen, Bollerslev, Diebold and Labys (2003), Corsi (2004), Andersen …

Persistent link: https://www.econbiz.de/10009771770

Modeling autoregressive processes with moving-quantiles-implied nonlinearity

Ishida, Isao; Kvedaras, Virmantas - In: Econometrics : open access journal 3 (2015) 1, pp. 2-54

using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear …

Persistent link: https://www.econbiz.de/10010478989

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