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Long memory in the volatility of selected cryptocurrencies: Bitcoin, Ethereum and Ripple
Soylu, Pınar Kaya; Okur, Mustafa; Çatıkkaş, Özgür; … - In: Journal of risk and financial management : JRFM 13 (2020) 6/107, pp. 1-20
This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory …
Persistent link: https://www.econbiz.de/10012305060
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GARCH modelling of cryptocurrencies
Chu, Jeffrey; Chan, Stephen; Nadarajah, Saralees; … - In: Journal of risk and financial management : JRFM 10 (2017) 4, pp. 1-15
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms...
Persistent link: https://www.econbiz.de/10011854856
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Bitcoin return volatility forecasting : a comparative study between GARCH and RNN
Shen, Ze; Wan, Qing; Leatham, David J. - In: Journal of risk and financial management : JRFM 14 (2021) 7, pp. 1-18
One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility … volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is … bitcoin's return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in …
Persistent link: https://www.econbiz.de/10012626254
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Does the assumption on innovation process play an important role for filtered historical simulation model?
Altun, Emrah; Tatlidil, Huseyin; Ozel, Gamze; … - In: Journal of risk and financial management : JRFM 11 (2018) 1, pp. 1-13
Most of the financial institutions compute the Value-at-Risk (VaR) of their trading portfolios using historical simulation-based methods. In this paper, we examine the Filtered Historical Simulation (FHS) model introduced by Barone-Adesi et al. (1999) theoretically and empirically. The main goal...
Persistent link: https://www.econbiz.de/10011855007
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The effect of COVID-19 on cryptocurrencies and the stock market volatility : a two-stage DCC-EGARCH model analysis
Ampountolas, Apostolos - In: Journal of risk and financial management : JRFM 16 (2023) 1, pp. 1-17
This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices …, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility … and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk …
Persistent link: https://www.econbiz.de/10014295230
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On setting day-ahead equity trading risk limits
Fuertes, Ana María; Olmo, Jose - In: Journal of risk and financial management : JRFM 9 (2016) 3, pp. 1-20
This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight...
Persistent link: https://www.econbiz.de/10011543115
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Quantification of VaR : a note on VaR valuation in the South African equity market
Mabitsela, Lesedi; Maré, E.; Kufakunesu, Rodwell - In: Journal of risk and financial management : JRFM 8 (2015) 1, pp. 103-126
The statistical distribution of financial returns plays a key role in evaluating Value-at-Risk using parametric methods. Traditionally, when evaluating parametric Value-at-Risk, the statistical distribution of the financial returns is assumed to be normally distributed. However, though simple to...
Persistent link: https://www.econbiz.de/10011552897
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Asymmetric realized volatility risk
Allen, David E.; McAleer, Michael; Scharth, Marcel - In: Journal of risk and financial management : JRFM 7 (2014) 2, pp. 80-109
degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which … forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation … distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized …
Persistent link: https://www.econbiz.de/10011553303
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Predictors of excess return in a green energy equity portfolio : market risk, market return, value-at-risk and or expected shortfall?
Abraham, Rebecca; El-Chaarani, Hani; Tao, Zhi - In: Journal of risk and financial management : JRFM 15 (2022) 2, pp. 1-31
The rapid growth of electric vehicles, solar roofs, and wind power suggests that the potential growth in green equity investments is an emerging trend. Accordingly, this study measured the predictors of excess equity returns in a portfolio of global green energy producers, from 2010 to 2019....
Persistent link: https://www.econbiz.de/10012872607
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Forecasting volatility and tail risk in electricity markets
Naimoli, Antonio; Storti, Giuseppe - In: Journal of risk and financial management : JRFM 14 (2021) 7, pp. 1-17
This paper investigates the benefits of jointly using several realized measures in predicting daily price volatility … significantly increases the accuracy of volatility forecasts, while in forecasting Value-at-Risk and Expected Shortfall at different …
Persistent link: https://www.econbiz.de/10012622471