Analysing the performance of managed funds using the wavelet multiscaling method

In, Francis Haeuck; Kim, Sangbae; Marisetty, Vijaya B.; Faff, Robert W.

doi:10.1007/s11156-007-0061-8

Cited by 23 publications

(9 citation statements)

References 36 publications

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“…Also, Garcia and Ghysels (1998) discussed the impact of structural change and regime switches on beta estimation. examining the relation between the future market and spot market, Kim and In (2003) and Gallegati (2005a) in testing the causality test between financial market and economic activity, Kim and In (2005b) and In, Kim, Marisetty, and Faff (2008) in calculation of multiscale Sharp ratio, and Zhang and Farley (2004) and Lee (2004) in analyzing causality of the international stock market, Kim and In (2007) in analyzing the mutiscale relation between stock prices and bond returns.…”

Section: Literature Reviewmentioning

confidence: 99%

Systematic risk and time scales: New evidence from an application of wavelet approach to the emerging Gulf stock markets

Masih

Alzahrani

Al‐Titi

2010

International Review of Financial Analysis

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Section: Literature Reviewmentioning

confidence: 99%

Systematic risk and time scales: New evidence from an application of wavelet approach to the emerging Gulf stock markets

Masih

Alzahrani

Al‐Titi

2010

International Review of Financial Analysis

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“…Mallat and Zhang, 1993;Gençay et al, 2001a;Gençay et al, 2003;Gençay et al, 2005;Vuorenmaa, 2006), or a tool to detect interdependence between variables (In and Kim, 2006;In et al, 2008;Kim and In, 2005;Kim and In, 2007).…”

Section: Introductionmentioning

confidence: 99%

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Dajčman¹

2013

Prague Economic Papers

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Abstract:This paper investigates multiscale interdependence between the stock markets of Germany, Austria, France, and the United Kingdom. Wavelet energy additive decomposition was analyzed to investigate which scales capture the most energy (volatility), whereas a wavelet cross-correlation estimator was used to analyze comovement and lead/lag relationship between stock markets' return dynamics on a scale-by-scale basis. The main fi ndings of the paper are as follows. First, major fi nancial market crises had a signifi cant impact on return volatility of investigated stock markets. Among them, the global fi nancial crisis of 2007-2008 had the greatest and the most durable impact. Second, the lowest scale (associated with stock markets' return dynamics over a 2-4 days horizon) and the second lowest scale (associated with stock markets' return dynamics over 4-8 days horizon) MODWT (maximal overlap discrete wavelet transform) decompositions of stock markets' returns captured the greatest share (together about 70-80%) of indices' returns volatility. Third, comovement between stock market returns is a scale-dependent phenomenon. Fourth, a strong comovement between stock market returns of Germany, France, and the United Kingdom exists at all scales, while the Austrian stock market is less correlated with the three biggest stock markets in Europe. Fifth, the dynamics of stock market returns seems to be well time-synchronized at daily (raw returns) and the lowest scale (scale ) return decomposition as most of the return innovations are transmitted between stock markets intraday. Sixth, at the highest investigated scale (associated with stock markets' return dynamics over a 64-128 days horizon), signifi cant leads and lags between dynamics of stock markets' returns were detected. The time-synchronization of the stock markets' return dynamics for investments of 64 to 128 days horizon is less perfect than for investments of shorter investment horizons.

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“…For more details, background, and applications of wavelet theory in finance, economics, management, and actuarial sciences, the readers may refer to the works in [46][47][48][49][50][51][52][53]66,67,[71][72][73][74][75][76][77][78][79][80][81][82][83][84].…”

Section: The Capm and The Time Factor Reviewmentioning

confidence: 99%

The Systematic Risk at the Crisis—A Multifractal Non-Uniform Wavelet Systematic Risk Estimation

Sarraj

Mabrouk

2021

Fractal Fract

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In the last decade, many factors, such as socio-political and econo-environmental ones, have led to a perturbation in the timeline of the worldwide development, and especially in countries and regions having political changes. This led us to introduce a new idea of risk estimation taking into account the non-uniform changes in markets by introducing a non-uniform wavelet analysis. We aim to explain the econo-political situation of Arab spring countries and the effect of the revolutions on the market beta. The main novelty is first the construction of a dynamic backward-forward model for missing data, and next the application of random non-uniform wavelets. The proposed procedure will be acted empirically on a sample corresponding to TUNINDEX stock as a representative index of the Tunisian market actively traded over the period from 14 January 2016 to 13 January 2021. The chosen 5-year period is important as it constitutes the first five years after the revolution and depends strongly on the socio-econo-political stability in the revolutionary countries. The results showed the efficiency of non-uniform wavelets in explaining the dynamics of the market well. They therefore may be good tools to explore important phenomena in the market such as the non-stationary aspect of financial series, non-constancy, and time-varying parameters. These facts in turn will have positive implications for investors as well as politicians in front of the evolution of the market. Besides, recommendations to extend the present method for other types of wavelets and markets will be of interest.

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Analysing the performance of managed funds using the wavelet multiscaling method

Abstract: Performance measure, Wavelet analysis, Sharpe ratio, Australian managed funds, G23, G21, G10,

Cited by 23 publications

References 36 publications

Systematic risk and time scales: New evidence from an application of wavelet approach to the emerging Gulf stock markets

Systematic risk and time scales: New evidence from an application of wavelet approach to the emerging Gulf stock markets

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

The Systematic Risk at the Crisis—A Multifractal Non-Uniform Wavelet Systematic Risk Estimation

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