Forecasting Value-at-Risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model

Jiang, Kunliang; Zeng, Linhui; Song, Jiashan; Liu, Yimeng

doi:10.1016/j.ribaf.2022.101634

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…The results support other scientists' findings that the bitcoin market is inefficient (Bariviera, 2017;Chuen et al, 2018;Corbet et al, 2018). Also, a time-varying approach used by Jiang et al (2022) makes an advantage when used for tracking the dynamic efficiency. One of the main reasons why efficiency is not maintained is the lack of reasonable pricing mechanisms and irrational behavior of investors.…”

Section: Literature Reviewsupporting

confidence: 87%

“…Risks were precisely analyzed in the scientific literature by evaluating such risk measures as Value-at-Risk (VaR) and Expected Shortfall (Hrytsiuk et al, 2019;Likitratcharoen et al, 2018;Pele & Mazurencu-Marinescu-Pele, 2019;Trucíos et al, 2019). Besides the function of risk evaluation these measures of risk increase the accuracy of forecasting (Jiang et al, 2022;Görgen et al, 2022;Müller et al, 2022). Jiang et al (2022) used a method capturing the stylized facts and found that including analysis of stylized facts in the analysis is useful for capturing sudden changes in the density of cryptocurrency returns.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Besides the function of risk evaluation these measures of risk increase the accuracy of forecasting (Jiang et al, 2022;Görgen et al, 2022;Müller et al, 2022). Jiang et al (2022) used a method capturing the stylized facts and found that including analysis of stylized facts in the analysis is useful for capturing sudden changes in the density of cryptocurrency returns. Müller et al (2022) suggested that the main driver for the returns of cryptocurrencies is the conditional standard deviation and not the distribution of the error term.…”

Section: Literature Reviewmentioning

confidence: 99%

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Stylized Facts, Volatility Dynamics and Risk Measures of Cryptocurrencies

Bruzgė,

Černevičienė,

Šapkauskienė

et al. 2023

Journal of Business Economics and Management

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This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets as there was confirmed existence of volatility clusters when high volatility periods are followed by low volatility periods. Sensitivity analysis and measures of Value-at-Risk (VaR) and Expected Shortfall (ES) show the amount of losses investors can expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and possibilities for arbitrage opportunities. Findings could be beneficial for investors and policymakers as well as for scientific purposes as findings give us a better understanding of the behaviour of cryptocurrencies.

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Section: Literature Reviewsupporting

confidence: 87%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Stylized Facts, Volatility Dynamics and Risk Measures of Cryptocurrencies

Bruzgė,

Černevičienė,

Šapkauskienė

et al. 2023

Journal of Business Economics and Management

View full text Add to dashboard Cite

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“…Vilasuso (2002) brings up one of GARCH's major limitations where "its memory is sometimes not long enough to capture the persistence of some shocks that are observed to last for a very long time". Jiang et al (2022) propose a time-varying mixture model, which includes an accelerating generalized autoregressive score (aGAS) technique into the Gaussian-Cauchy mixture (TVM)-aGAS model for forecasting Value-at-Risk for cryptocurrencies. Recently, however, many researchers have turned to ever more powerful deep learning models for financial time series prediction.…”

Section: Deep Neural Network For Financial Time Series Predictionsmentioning

confidence: 99%

Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models

Herremans

Low

2022

SSRN Journal

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“…Furthermore, to examine the time-varying correlations between the three indicators, this paper employs a multivariate Student-t Generalized Autoregressive Score (GAS) model (Creal et al 2013;Harvey 2013). This model provides policymakers with new analytical tools for capturing the dynamic characteristics between the three indicators (Jiang et al 2022). The study also used the autoregressive distributed lag (ARDL) approach, a well-known method developed by (Pesaran and Shin 1999;Pesaran et al 2001), to examine the relationship between the three indicators.…”

Section: Introductionmentioning

confidence: 99%

An Understanding of How GDP, Unemployment and Inflation Interact and Change across Time and Frequency

Shiferaw

2023

Economies

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The main aim of this paper is to examine the dynamic relationship between the three pillars of the economy: unemployment, inflation, and GDP in Ethiopia using the cross-wavelet transform (XWT) analysis, the multivariate Student-t generalized autoregressive score (GAS) model, and the autoregressive distributed lag (ARDL) model. The dynamics between the three indicators were also investigated using the Toda–Yamamoto (TY) causality test. The empirical findings from the XWT method suggest a relationship between unemployment, inflation, and GDP, though the relationship varies over time and frequency. The estimation results from the multivariate Student-t GAS model show that the correlation between unemployment (overall, male, female, and youth) and inflation is highly significant, indicating that the correlation is dynamic. A dynamic relationship exists between GDP and unemployment, except for females and young people. The ARDL approach’s findings showed that unemployment significantly negatively impacted GDP. However, it was found that inflation significantly increased GDP. The general conclusion drawn from this study’s findings is that unemployment significantly affects GDP and inflation. Therefore, the government should aggressively implement policies to reduce unemployment, especially youth unemployment. Additionally, the administration must rehabilitate the country’s badly damaged economy and formalize a lasting cessation of hostilities between the federal government and the Tigray People’s Liberation Front (TPLF).

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Forecasting Value-at-Risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model

Cited by 18 publications

References 24 publications

Stylized Facts, Volatility Dynamics and Risk Measures of Cryptocurrencies

Stylized Facts, Volatility Dynamics and Risk Measures of Cryptocurrencies

Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models

An Understanding of How GDP, Unemployment and Inflation Interact and Change across Time and Frequency

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