On Forecasting Daily Stock Volatility: The Role of Intraday Information and Market Conditions

Fuertes, Ana-Marı́a; Kalotychou, Elena; Izzeldin, Marwan

doi:10.2139/ssrn.1137997

Cited by 18 publications

(24 citation statements)

References 71 publications

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“…(3) According to the comparison of the predictive power of the three types of models, the GARCH-RV model performs the better in predicting the future volatility than the GARCH-type models, which is consistent with Koopman, Fuertes et al, and Lehnert et al [6][7][8].…”

Section: Discussionsupporting

confidence: 74%

“…They built a GARCH-RV model and found that the GARCH-RV model has stronger predictive power than the GARCH model. Fuertes et al and Frijns et al [7,8] also showed that the GARCH-RV model has stronger power to predict the asset volatility than the GARCH model. But in realistic financial markets, the asset volatility is a continuous process with some jump components.…”

Section: Introductionmentioning

confidence: 97%

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GARCH-Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

Zhang

Lan

2014

Mathematical Problems in Engineering

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On the basis of GARCH-RV-type model, we decomposed the realized volatility into continuous sample path variation and discontinuous jump variation, then proposed a new volatility model which we call the GARCH-type model with continuous and jump variation (GARCH-CJ-type model). By using the 5-minute high frequency data of HUSHEN 300 index in China, we estimated parameters of the GARCH-type model, the GARCH-RV-type model, and the GARCH-CJ-type model and compared the three types of models’ predictive power to the future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility, but the discontinuous jump variation does not have that kind of function. What is more, the GARCH-CJ-type model has a more power to predict the future volatility than the other two types of models. Therefore, the GARCH-CJ-type model is much more useful for the research on the capital assets pricing, the derivative security valuation, and so on.

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Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 97%

GARCH-Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

Zhang

Lan

2014

Mathematical Problems in Engineering

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“…This is very important for investors in option markets, as an under (over)-prediction of implied volatility is more likely to be of greater concern to a seller (buyer) than a buyer (seller). The measure has been employed previously in studies evaluating volatility forecasting techniques such as Brailsford and Fa (1996) and Fuertes et al (2009).…”

Section: Mean Mixed Error (Mme)mentioning

confidence: 99%

Forecasting implied volatility in foreign exchange markets: a functional time series approach

Kearney

Cummins

Murphy

2017

The European Journal of Finance

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We utilise functional time series (FTS) techniques to characterise and forecast implied volatility in foreign exchange markets. In particular, we examine the daily implied volatility curves of FX options, namely; EUR-USD, EUR-GBP, and EUR-JPY. Based on existing techniques in the literature, the FTS model is shown to produce both realistic and plausible implied volatility shapes that closely match empirical data during the volatile 2006-2013 period. Furthermore, the FTS model signicantly outperforms implied volatility forecasts produced by traditionally employed parametric models. The evaluation is performed under both an in-sample and out-of-sample testing framework with our ndings shown to be robust across various currencies, moneyness segments, contract maturities, forecasting horizons, and out-of-sample window lengths. The economic signicance of the results is highlighted through the implementation of a simple trading strategy.

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“…There is a modeling strategy in the literature that broadly resembles this univariate approach in that AR-ARFIMA equations are fitted to time-series of daily open-to-close returns and realized volatilities computed from open-to-close intraday prices (e.g., Wu [31]; Liu and Maheu [32]; Fuertes et al [33]; Corsi et al [16]). In addition, we exploit the overnight return that is available at the market open when the forecasts are made.…”

Section: Univariate Ex Post Overnight Modeling Approachmentioning

confidence: 99%

On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

Fuertes

Olmo

2016

JRFM

Self Cite

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This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et al. that constructs the forecast at the market close instead and, accordingly, it models separately the daytime and overnight return processes and their covariance. For a small cap portfolio, the bivariate VaR approach affords superior predictive ability than the ex post overnight VaR approach whereas for a large cap portfolio the results are reversed. The contrast indicates that price discovery at the market open is less efficient for small capitalization, thinly traded stocks.

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On Forecasting Daily Stock Volatility: The Role of Intraday Information and Market Conditions

Abstract: All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission, provided that full acknowledgement is given.

Cited by 18 publications

References 71 publications

GARCH-Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

GARCH-Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

Forecasting implied volatility in foreign exchange markets: a functional time series approach

On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

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