Long-Memory, Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets

Mighri, Zouheir; Jaziri, Raouf

doi:10.1007/s40953-022-00331-w

Cited by 3 publications

(2 citation statements)

References 74 publications

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“…Further, the Swedish Real Estate Sector Index's average mean-reverting half-life period is 49 days, which is longer than other developed markets such as NYSE Composite and Nikkei [63], but faster than emerging markets such as the Saudi Stock Exchange (30 months) [62]. In comparison to similar studies on the real estate sector, the asymmetry and long-range dependence identified in the Swedish real estate sector are consistent with other markets like the U.S, Australia, Canada, France, and the Netherlands [38][39][40]. However, the volatility pattern of the Swedish real estate sector is different to emerging markets like Hong Kong, where an asymmetric effect does not exist [71], and South Africa, where the effect of positive news outrides the effect of negative ones [72].…”

Section: Discussionsupporting

confidence: 81%

“…To analyze the volatility pattern, Booth et al [37] studied the stock volatility in Denmark, Finland, Norway, and Sweden using an EGARCH model and found both a volatility persistence and leverage effect. Further, previous research found that the attributes of asymmetry and long-range dependence are commonly found in listed real estate firms of developed markets with mature and complete legal and regulatory systems like the U.S, Australia, Canada, France, and the Netherlands [38][39][40]. These findings are consistent with the Prospect theory and its application to financial market that investors are more sensitive to bad news possibly leading to a loss than good news, and thus, markets react to negative shocks more aggressively than to positive shocks [41].…”

Section: Review Of Volatility Patternsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models

Zheng,

Song,

Liang

2024

Buildings

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In this paper, we focus on the dynamic volatility behavior of the daily Swedish Real Estate Sector Index and analyze the existence and degree of a long-range dependence or asymmetric news effect since 2003. More specifically, we give extra attention to the 2007–2008 financial crisis, the 2009–2012 European debt crisis, and the first two years of the global COVID-19 pandemic era (2020–2021). We examine changes in volatility during these extreme events. We apply standard GARCH models, asymmetric GARCH models, and long-memory GARCH models with various error distributions to identify the most accurate volatility models of the daily returns of the Swedish Real Estate Sector Index for the full sample period, January 2003 to June 2021. Our results show that the volatility of the Swedish Real Estate Sector Index is time-varying and highly volatile. The impacts of the global financial crisis, European debt crisis, and COVID-19 pandemic are noticeable. Moreover, the volatility pattern during COVID-19 displays significant time-varying long-range dependence and an asymmetrical news impact, which lead to market inefficiency. Finally, the volatility pattern shows a tendency towards increasing leverage effects and less persistent behavior, indicating that the market stakeholders are highly sensitive to negative returns and becoming quicker to respond to market changes.

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Section: Discussionsupporting

confidence: 81%

Section: Review Of Volatility Patternsmentioning

confidence: 99%

Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models

Zheng,

Song,

Liang

2024

Buildings

View full text Add to dashboard Cite

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The Effects of Overnight Events on Daytime Return: A Market Microstructure Analysis of Market Quality

Pullaykkodi,

Acharya

2023

Asia-Pac Financ Markets

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Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models

Khumalo,

Mashele,

Seitshiro

2023

DSFE

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<abstract><p>The South African financial market is developing with periods of high and low volatility. Employing an adequate volatility model is essential to manage market risk. This research study was designed to investigate the effectiveness of the fractionally integrated asymmetric power autoregressive conditional heteroskedasticity contrasted with long-memory GARCH-type models, such as the fractionally integrated generalized autoregressive conditional heteroskedasticity and the hyperbolic generalized autoregressive conditional heteroskedasticity for producing the measure of market risk known as the value at risk. These long-memory GARCH-type models assume that the distributions of the index returns follow normal, student-$ t $, skewed student-$ t $ and generalized error distributions. The historical closing price time series of the Johannesburg Stock Exchange all share, the mining and the banking indices are considered. The value at risk and its backtesting for short and long trading positions on the different confident levels are computed and they correspond to the right and left quantiles of the return distributions, respectively. The results reveal that FIAPARCH with a standard student-$ t $ distribution is an appropriate model for producing a robust value at risk in the context of mining and banking indices. Alternatively, FIGARCH with the assumed skewed student-$ t $ distribution model is a good fit to produce a value at risk for the Johannesburg Stock Exchange All Share Index.</p></abstract>

show abstract

Long-Memory, Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets

Cited by 3 publications

References 74 publications

Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models

Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models

The Effects of Overnight Events on Daytime Return: A Market Microstructure Analysis of Market Quality

Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models

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