Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)

Vogl, Markus

doi:10.1007/s43546-022-00359-3

Cited by 9 publications

(5 citation statements)

References 148 publications

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“…The Hurst exponent is often used to analyse the autocorrelation structure and predictability of stock price movements and is very useful to ascertain whether a time series exhibits a trend, mean-reversion, or random walk behaviour over different time horizons (Bui & Ślepaczuk, 2022). This model is very useful in estimating the extend in which a fitted a linear regression line correlates with a logarithm of the rescaled range as a function of the time series (Vogl, 2022). In its simplest form, the mathematical expression for the Hurst model is highlighted below;…”

Section: Research and Methodologymentioning

confidence: 99%

The long-term memory of stock markets: unveiling patterns and predictability

Enow

2024

IJRBS

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The efficient market hypothesis assumes that financial markets fully incorporate all available information, rendering past information irrelevant for predicting future prices. However, numerous studies challenge this notion and suggest the presence of long-term memory in market dynamics. Understanding long-term memory in financial markets has important implications for investors and policymakers. The aim of this study was to empirically investigate long term memory in financial markets. This study employed a Hurst model for a sample of 5 financial markets from June 1, 2018, to June 1, 2023. The findings revealed that four out of the five sampled financial market exhibits long term memory which challenges the efficient market hypothesis concept. Therefore, portfolio managers and active market participants can utilize long-term memory to optimize asset allocation decisions by considering the persistent effects of past returns and adjust portfolio weights to take advantage of potential return predictability and manage risk.

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Section: Research and Methodologymentioning

confidence: 99%

The long-term memory of stock markets: unveiling patterns and predictability

Enow

2024

IJRBS

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“…The purpose of performing a citation network analysis was to ensure that our overview of the literature was complete and that we did not omit any relevant communities, as well as to identify gaps that provide avenues for future research. A detailed guide for the citation network analysis following [39] can be found in Supplementary Material S1. Table 1 provides an overview of the metrics we used to analyze the citation network.…”

Section: Research Design and Methodologymentioning

confidence: 99%

The Relationship between Resilience and Sustainability in the Organizational Context—A Systematic Review

Weber

2023

Sustainability

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Crises such as the global COVID-19 pandemic and crisis-related factors such as the continued rise in commodity prices continue to have a tremendous impact on organizations. Organizational sustainability, understood as the ability of organizations to survive and thrive in the short and long term, depends on organizational resilience. This study bridges the literature on resilience and sustainability in the organizational context, covering the last 35 years of research, to provide a broader understanding of how organizations, adverse events, and organizational sustainability interrelate. In this paper, we perform a systematic review of the literature from 1985 to 2021, which we supplement with a content analysis, recent empirical findings, and a citation network analysis. We investigate connections between these two fields and identify studies on how the measures of or actions associated with organizational resilience and sustainability can complement or substitute for each other with regard to organizational performance. In addition, we develop an extended framework for categorizing the studies we consider here and the relationships between resilience and sustainability they examine. Our analysis identifies works that bridge the streams of research on organizational resilience and sustainability but also reveals gaps in the literature that open avenues for future research. Overall, most of the ‘bridging’ studies emphasize that the two fundamental concepts are interdependent and regard organizational resilience as a component of organizational sustainability.

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“…The Hurst exponent is often used to analyse autocorrelation and the predictability of stock price movements which is very useful to ascertain whether a time series exhibits a trend, mean-reversion, or random walk behaviour over different time horizons (Bui & Ślepaczuk, 2022). This model is an intuitive method of estimating the extend in which a fitted a linear regression line correlates with a logarithm of the rescaled range as a function of the time series (Vogl, 2022). In its simplest form, the mathematical expression for the Hurst model is highlighted below;…”

Section: Methodsmentioning

confidence: 99%

Investigating mean reversion in financial markets using Hurst Model

Enow

2023

IJRBS

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In the dynamic world of financial markets, the prices of assets can exhibit dramatic fluctuations, sometimes soaring to dizzying heights or plummeting to alarming lows. However, amidst the chaos, a fascinating phenomenon emerges: a tendency for prices to revert back to their long-term average or mean level. This concept known as mean reversion has intrigued traders, investors, and researchers for decades. Understanding mean reversion provides valuable insights into market dynamics, investor behavior, and the potential for profitable trading strategies. The aim of this study was to empirically investigate mean reversion in financial markets. This study employed a Hurst model for a sample of five financial markets from June 1, 2018 to June 1, 2023. The findings revealed that four out of the five sampled financial markets exhibit mean reversion, which challenges the efficient market hypothesis concept. Therefore, portfolio managers and active market participants can utilize long-term memory to optimize asset allocation decisions by considering the persistent effects of past returns and adjusting portfolio weights to take advantage of potential return predictability and manage risk.

show abstract

Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)

Cited by 9 publications

References 148 publications

The long-term memory of stock markets: unveiling patterns and predictability

The long-term memory of stock markets: unveiling patterns and predictability

The Relationship between Resilience and Sustainability in the Organizational Context—A Systematic Review

Investigating mean reversion in financial markets using Hurst Model

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