Extended AIC model based on high order moments and its application in the financial market

Xuegeng Mao and Pengjian Shang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 501, issue C, 264-275

Abstract: In this paper, an extended method of traditional Akaike Information Criteria(AIC) is proposed to detect the volatility of time series by combining it with higher order moments, such as skewness and kurtosis. Since measures considering higher order moments are powerful in many aspects, the properties of asymmetry and flatness can be observed. Furthermore, in order to reduce the effect of noise and other incoherent features, we combine the extended AIC algorithm with multiscale wavelet analysis, in which the newly extended AIC algorithm is applied to wavelet coefficients at several scales and the time series are reconstructed by wavelet transform. After that, we create AIC planes to derive the relationship among AIC values using variance, skewness and kurtosis respectively. When we test this technique on the financial market, the aim is to analyze the trend and volatility of the closing price of stock indices and classify them. And we also adapt multiscale analysis to measure complexity of time series over a range of scales. Empirical results show that the singularity of time series in stock market can be detected via extended AIC algorithm.

Keywords: Akaike Information Criteria (AIC); Skewness; Kurtosis; Wavelet transform; Financial market (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:501:y:2018:i:c:p:264-275

DOI: 10.1016/j.physa.2018.02.159

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