Wavelet Instruments for Efficiency Gains in Generalized Method of Moment Models

Michis, Antonis; Sapatinas, Theofanis

doi:10.2202/1558-3708.1531

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…In addition, wavelets have also been used in economic and financial forecasting (see the studies by Michis 2014bMichis , 2015bMichis and Guy 2017;Rua 2017;Caraiani 2017;Risse 2019), econometric estimation (Michis and Sapatinas 2007;Gençay and Gradojevi 2011;Gilles et al 2009;Jensen 2000) and testing (Hong and Kao 2004;Fan and Gençay 2010;Gençay and Signori 2015), as well as in filtering-denoising applications (Fleming et al 2000;Capobianco 2003;Bruzda 2014;Michis 2011). Rua and Nunes (2009) analyzed international comovements between economic sector returns over different time scales.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiscale Partial Correlation Clustering of Stock Market Returns

Michis

2022

JRFM

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This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

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Section: Literature Reviewmentioning

confidence: 99%

Multiscale Partial Correlation Clustering of Stock Market Returns

Michis

2022

JRFM

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“…Michis mentioned a very important characteristic of wavelet theory in [16,18,28]. It permits investigation of the causal links between time-series cycles in marketing according to time scale.…”

Section: Literature Reviewmentioning

confidence: 99%

Wavelet Time-Scale Modeling of Brand Sales and Prices

Alanazi

Mabrouk

2022

Applied Sciences

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Marketing is the way we ensure our sales are the best in the market, our prices the most accessible, and our clients satisfied, thus ensuring our brand has the widest distribution. This requires sophisticated and advanced understanding of the whole related network. Indeed, marketing data may exist in different forms such as qualitative and quantitative data. However, in the literature, it is easily noted that large bibliographies may be collected about qualitative studies, while only a few studies adopt a quantitative point of view. This is a major drawback that results in marketing science still focusing on design, although the market is strongly dependent on quantities such as money and time. Indeed, marketing data may form time series such as brand sales in specified periods, brand-related prices over specified periods, market shares, etc. The purpose of the present work is to investigate some marketing models based on time series for various brands. We will precisely study the effect of the time scale on the persistence of brand sales in the market and on the forecasting of such persistence, according to the characteristics of the brand and the related market competition or competitors. Our study is based on a sample of Saudi brands during the period 22 November 2017 to 30 December 2021.

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“…Wavelet packet decomposition (WPD) uses the wavelet approach. Michis and Sapatinas [42] proposed a method that relies on wavelet packet transforms of the existing continuous explanatory variables to increase the efficiency of regression coefficient estimation. Despite the method being insufficiently utilized in finance, wavelet packet decomposition is a typical signal decomposition approach at both the approximate and detailed scales and outperforms traditional wavelet analysis.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Tail Risk Connectedness of Global Stock Markets: A LASSO‐Based Network Topology Approach

Zhang

Ding

et al. 2022

Complexity

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Due to the advent of deglobalization and regional integration, this article aims to adopt LASSO-based network connectedness to estimate the multiscale tail risk spillover effects of global stock markets. The results show that tail risk varies across frequencies and shocks. In static analysis, the risk is centered mostly on the developed European and North American markets at a low frequency (long term), and regionalization is imposed on the moderate frequency (midterm). Moreover, emerging markets could be sources of risk spillover, especially at the highest frequency (short term) where there is no absolute risk center. In dynamic analysis, we use rolling window estimation and find that different frequencies identify distinct episodes of shocks, which provides us with the reason for the diverse risk centers at different time scales in static analysis. Our findings provide heterogeneous financial practitioners, regulators, and investors with diverse characteristics of stock markets under multiple time horizons and help them operate their own trading strategies.

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Wavelet Instruments for Efficiency Gains in Generalized Method of Moment Models

Cited by 9 publications

References 30 publications

Multiscale Partial Correlation Clustering of Stock Market Returns

Multiscale Partial Correlation Clustering of Stock Market Returns

Wavelet Time-Scale Modeling of Brand Sales and Prices

Multiscale Tail Risk Connectedness of Global Stock Markets: A LASSO‐Based Network Topology Approach

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