Work detail

Does wavelet decomposition and neural networks help to improve predictability of realized volatility?

Author: Mgr. Tomáš Křehlík
Year: 2013 - summer
Leaders: doc. PhDr. Jozef Baruník Ph.D.
Consultants:
Work type: Economic Theory
Masters
Language: English
Pages: 74
Awards and prizes: M.A. with distinction from the Dean of the Faculty of Social Sciences for an excellent state-final examination performance.
Link:
Abstract: I perform comprehensive comparison of the standard realised volatility estimators including a novel
wavelet time-frequency estimator (Barunik and Vacha 2012) on wide variety of assets: crude oil, gold
and S&P 500. The wavelet estimator allows to decompose the realised volatility into several investment
horizons which is hypothesised in the literature to bring more information about the volatility time
series. Moreover, I propose artificial neural networks (ANN) as a tool for forecasting of the realised
volatility. Multi-layer perceptron and recursive neural networks typologies are used in the estimation. I
forecast cumulative realised volatility on 1 day, 5 days, 10 days and 20 days ahead horizons. The
forecasts from neural networks are benchmarked to a standard autoregressive fractionally integrated
moving averages (ARFIMA) model and a mundane model. I confirm favourable features of the novel
wavelet realised volatility estimator on crude oil and gold, and reject them in case of S&P 500. Possible
explanation is an absence of jumps in this asset and hence over-adjustment of data for jumps by the
estimator. In forecasting, the ANN models outperform the ARFIMA in terms of information content
about dynamic structure of the time series.

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