Time–frequency varying estimations: comparison of discrete and continuous wavelets in the market line framework

Mestre, Roman; Terraza, Michel

doi:10.1007/s42786-019-00008-8

Cited by 4 publications

(10 citation statements)

References 16 publications

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“…Equation ( 13 ) provides a constant coefficient for each frequency band based on the standard representation of the market’s line. However, a rolling time–frequency window can be used to estimate a time-varying coefficient on each band (Mestre and Terraza 2019 ; McNevin and Nix 2018 ). McNevin and Nix ( 2018 ) used an L-sized rolling window to estimate time-varying betas on each frequency band from a unique MODWT.…”

Section: Theoretical Aspects Of Wavelets Time–frequency Analysis In T...mentioning

confidence: 99%

“…Such operations are useful for satisfying decomposition properties, particularly variance preservation. Furthermore, as Mestre and Terraza ( 2019 ) demonstrated, some bias can affect parameter estimations, particularly in the long-run.…”

Section: Theoretical Aspects Of Wavelets Time–frequency Analysis In T...mentioning

confidence: 99%

“…Mestre and Terraza ( 2019 ) proposed the time–frequency rolling window performing the MODWT inside the L-sized window. Thus, all rescaling and subsampling operations are compatible with estimating the beta parameter based on L points.…”

Section: Theoretical Aspects Of Wavelets Time–frequency Analysis In T...mentioning

confidence: 99%

“…The main goal of the preset study is to highlight the role of investment horizons on systematic risk in different market conditions. Following the research of Mestre and Terraza ( 2019 ) and McNevin and Nix ( 2018 ), we combine time-varying estimation and frequency decomposition. They proposed a rolling time–frequency estimation of the market line and developed the concept of time–frequency-varying beta parameters.…”

Section: Introductionmentioning

confidence: 99%

“…This study uses the time–frequency rolling window developed by Mestre and Terraza ( 2019 ) to analyze the time–frequency robustness of the initial risk-profile. We use the daily returns of selected stocks and the Eurostoxx index from 2012 to June 2022.…”

Section: Introductionmentioning

confidence: 99%

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Stock profiling using time–frequency-varying systematic risk measure

Mestre

2023

Financ Innov

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This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model (CAPM) framework. The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness. Furthermore, we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics. We use a daily panel of French stocks from 2012 to 2022. Results show that varying systematic risk varies in time and frequency, and that its short and long-run evolutions differ. We observe differences in short and long dynamics, indicating that a stock’s betas differently fluctuate to early announcements or signs of events. However, short-run and long-run betas exhibit similar dynamics during persistent shocks. Betas are more volatile during times of crisis, resulting in greater or lesser robustness of risk profiles. Significant differences exist in short-run and long-run risk profiles, implying a different asset allocation. We conclude that the standard CAPM assumes short-run investment. Then, investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.

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Section: Theoretical Aspects Of Wavelets Time–frequency Analysis In T...mentioning

confidence: 99%

Section: Theoretical Aspects Of Wavelets Time–frequency Analysis In T...mentioning

confidence: 99%

Section: Theoretical Aspects Of Wavelets Time–frequency Analysis In T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stock profiling using time–frequency-varying systematic risk measure

Mestre

2023

Financ Innov

Self Cite

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How likely is it to beat the target at different investment horizons: an approach using compositional data in strategic portfolios

Vega-Gámez,

Alonso-González

2024

Financ Innov

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Strategic portfolios are asset combinations designed to achieve investor objectives. A unique feature of these investments is that portfolios must be rebalanced periodically to maintain the initially established structure. This paper introduces a methodology to estimate the probability of not exceeding a specific profitability target with this type of portfolio to determine if this kind of build portfolio makes obtaining certain profitability targets easy. Portfolios with a specific distribution of fixed-income and equity securities were randomly replicated and their performance was studied over different time horizons. Daily data from 2004 to 2021 was used. Since the sum of all asset weights invariably equals the unit, the original data were transformed using the compositional data methodology. With these transformed data, the probabilities were estimated for each analyzed portfolio. The study also performed a sensitivity analysis of the estimated probabilities, modifying the weight of specific assets in the portfolio.

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