Rolling-sampled parameters of ARCH and Levy-stable models

Degiannakis, Stavros; Livada, Alexandra; Panas, Epaminondas

doi:10.1080/00036840600994039

Cited by 17 publications

(7 citation statements)

References 74 publications

(32 reference statements)

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“…Another group of studies applies existing weak‐form EMH tests in rolling estimation windows 14 . Examples are the rolling VR tests (Tabak, 2003; Kim and Shamsuddin, 2008; Hung, 2009), rolling ADF unit root test (Phengpis, 2006), rolling bicorrelation test (Lim, 2007; Todea and Zoicas‐Ienciu, 2008), rolling parameters of ARCH models (Degiannakis et al , 2008; Alagidede and Panagiotidis, 2009a) and rolling Hurst exponent (Costa and Vasconcelos, 2003; Cajueiro and Tabak, 2004b, 2005d, 2006, 2008; Zunino et al , 2007). The application of a rolling window essentially captures the persistence of stock price departures from a random walk benchmark over time.…”

Section: Rolling Estimation Windows With Fixed Parameter In Each Wmentioning

confidence: 99%

The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature

Lim

Brooks²

2011

Journal of Economic Surveys

313

152

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This paper provides a systematic review of the weak-form market efficiency literature that examines return predictability from past price changes, with an exclusive focus on the stock markets. Our survey shows that the bulk of the empirical studies examine whether the stock market under study is or is not weak-form efficient in the absolute sense, assuming that the level of market efficiency remains unchanged throughout the estimation period. However, the possibility of time-varying weak-form market efficiency has received increasing attention in recent years. We categorize these emerging studies based on the research framework adopted, namely non-overlapping sub-period analysis, time-varying parameter model and rolling estimation window. An encouraging development is that the documented empirical evidence of evolving stock return predictability can be rationalized within the framework of the adaptive markets hypothesis.

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Section: Rolling Estimation Windows With Fixed Parameter In Each Wmentioning

confidence: 99%

The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature

Lim

Brooks²

2011

Journal of Economic Surveys

313

152

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“…The topic of constancy of parameters across time is a longstanding historical debate as old as the role of econometrics in economics. Hendry (1996) notes : " Due to the fact that news information arrives daily in an unpredictable fashion, the estimated parameters should be revised on a daily basis (see Engle et al, 1990;Degiannakis et al, 2008). Figures 3 to 8 illustrate the time plot of the rolling-sampled estimated parameters from the FIGARCH and GARCH models.…”

Section: Rolling-sampled Parameter Estimatesmentioning

confidence: 99%

“…In particular, this paper contributes to the debate on the out-of-sample forecast performance of fractionally integrated models (see Ellis and Wilson, 2004 Degiannakis et al, 2008).…”

Section: Introduction -Motivation and Review Of Literaturementioning

confidence: 99%

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Degiannakis

Floros

Dent

2013

International Review of Financial Analysis

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The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Additionally, the results suggest that underestimation of the true VaR figure becomes less prevalent as the forecasting horizon increases. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered for the 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons. Finally, the rolling-sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models. Hence, the parameters' time-variant characteristic cannot be entirely due to the news information arrival process of the market; a portion must be due to the FIGARCH modelling process itself.

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“…The use of a restricted window length of 1000 trading days incorporates changes in trading behaviour more efficiently. For example, Angelidis et al (2004), Degiannakis et al (2008) and Engle et al (1993) provide empirical evidence that the use of restricted rolling window samples captures the changes in market activity more effectively 6,7 . The total number of observations is…”

Section: Mse Mape Loss Functions and The Model Confidence Setmentioning

confidence: 99%

Forecasting global stock market implied volatility indices

Degiannakis

Filis

Hassani

2018

Journal of Empirical Finance

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This study compares parametric and non-parametric techniques in terms of their forecasting power on implied volatility indices. We extend our comparisons using combined and model-averaging models. The forecasting models are applied on eight implied volatility indices of the most important stock market indices. We provide evidence that the non-parametric models of Singular Spectrum Analysis combined with Holt-Winters (SSA-HW) exhibit statistically superior predictive ability for the one and ten trading days ahead forecasting horizon. By contrast, the model-averaged forecasts based on both parametric (Autoregressive Integrated model) and non-parametric models (SSA-HW) are able to provide improved forecasts, particularly for the ten trading days ahead forecasting horizon. For robustness purposes, we build two trading strategies based on the aforementioned forecasts, which further confirm that the SSA-HW and the ARI-SSA-HW are able to generate significantly higher net daily returns in the out-of-sample period.

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Rolling-sampled parameters of ARCH and Levy-stable models

Cited by 17 publications

References 74 publications

The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature

The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Forecasting global stock market implied volatility indices

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