Social Interaction, Stochastic Volatility, and Momentum

He, X. T.; Li, Kai; Santi, Caterina; Shi, Lei

doi:10.2139/ssrn.2916490

Cited by 3 publications

(2 citation statements)

References 73 publications

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“…‡ While the vast majority of the papers in the HIA literature rely mainly in computational methods, the present paper combines computational methods with analytical results in a novel way which allows for better economic insights. Even though our analysis is methodologically original, the recent papers of Chiarella and Di Guilmi (2011), Di Guilmi and Carvalho (2017) and He et al (2017) are also good examples where analytical tools are used in combination to computational methods.…”

Section: Relevant Literaturementioning

confidence: 99%

Heterogeneity and clustering of defaults

Karlis

Galanis

Terovitis

et al. 2021

Quantitative Finance

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This paper studies how the degree of heterogeneity among hedge funds' demand orders for a risky asset affects the possibility of their defaults being clustered. We find that fire-sales caused by margin calls is a necessary, yet not a sufficient condition for defaults to be clustered. We show that when the degree of heterogeneity is sufficiently high, poorly performing HFs are able to obtain a higher than usual market share, which leads to an improvement of their performance. Consequently, their survival time is prolonged, increasing the probability of them remaining in operation until the downturn of the next leverage cycle. This leads to an increase in the probability of poorly and highperforming hedge funds to default in sync at a later time, and thus also in the probability of collective defaults. Our analytical results establish a connection between the nontrivial aggregate statistics and the presence of infinite memory in the process governing the hedge funds' defaults.

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Section: Relevant Literaturementioning

confidence: 99%

Heterogeneity and clustering of defaults

Karlis

Galanis

Terovitis

et al. 2021

Quantitative Finance

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“…Finally, we contribute to the growing literature that uses textual analysis of social networks posts to capture sentiment and disagreement (see e.g. Sun et al, 2016;Renault, 2017;Cookson and Niessner, 2020;Booker et al, 2022;He et al, 2022). Several works have proposed the use of Twitter to measure climate sentiment (Cody et al, 2015;Dahal et al, 2019;Loureiro and Alló, 2020), however there is still limited evidence on the link between climate sentiment and financial markets.…”

Section: Introductionmentioning

confidence: 99%

Investor climate sentiment and financial markets

Santi

2023

International Review of Financial Analysis

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Macroeconomic and stock market interactions with endogenous aggregate sentiment dynamics

Flaschel

Charpe

Galanis

et al. 2018

Journal of Economic Dynamics and Control

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Social Interaction, Stochastic Volatility, and Momentum

Cited by 3 publications

References 73 publications

Heterogeneity and clustering of defaults

Heterogeneity and clustering of defaults

Investor climate sentiment and financial markets

Macroeconomic and stock market interactions with endogenous aggregate sentiment dynamics

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