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Nonparametric volatility prediction
Author(s) -
Klemelä Jussi
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1491
Subject(s) - nonparametric statistics , estimator , heteroscedasticity , econometrics , semiparametric regression , nonparametric regression , volatility (finance) , autoregressive model , stochastic volatility , kernel regression , statistics , mathematics
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