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Nonparametric volatility prediction

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Abstract Volatility can be defined as the conditional expectation of the squared return of a financial asset. Many of the classical nonparametric regression estimators can be applied in volatility prediction. Examples of nonparametric estimators include moving averages and kernel estimators. However, it has been difficult to beat some parametric estimators from the generalized autoregressive conditionally heteroscedastic family using nonparametric estimators. We review some promising suggestions for nonparametric volatility prediction. This article is categorized under: Applications of Computational Statistics > Computational Finance Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Statistical Models > Time Series Models
Time series of predictions and realized values. (a) Predictions of the next day squared logarithmic returns (red) and the realized values (black) over the complete time period and (b) over the last 20 months. (c) Predictions of 20‐day realized volatility (red) and the realized values (black) over the complete time period and (d) over the last 20 months
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Time series of differences of cumulative sums of squared prediction errors when the benchmark is the GARCH(1,1) predictor. (a) The competitor is the Heston‐Nandi predictor. (b) The competitor is the asymmetric exponentially weighted moving average. The green horizontal lines are drawn at the level zero
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Statistical Models > Time Series Models
Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Applications of Computational Statistics > Computational Finance

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