Browsing by Subject "Wavelet Analysis"

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    Analyzing the forecast performance of S&P 500 Index Options implied volatility
    (2012) Erdemir, Aytaç
    This study examines the comparative performance of the call and put implied volatility (IV) of at-the-money European-style SPX Index Options on the S&P 500 Price Index as a precursor to the ex-post realized volatility. The results confirm that implied volatility contains valuable information regarding the ex-post realized volatility during the last decade for the S&P 500 market. The empirical findings also indicate that the put implied volatility has a higher forecast performance. Furthermore, from the wavelet estimations it has been concluded that the long-run variation of the implied volatility is consistent and unbiased in explaining the long-run variations of the ex-post realized volatility. Wavelet estimations further reveal that in the long-run put and call implied volatility contain comparable information regarding the realized volatility of the market. However, in the short-run put implied volatility dynamics have better predictive ability.
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    ItemOpen Access
    Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals
    (2011) Ayrulu-Erdem, B.; Barshan, B.
    We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction. © 2011 by the authors; licensee MDPI, Basel, Switzerland.