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Metcalfe's law and herding behaviour in the cryptocurrencies market
Pele, Daniel Traian; Mazurencu-Marinescu-Pele, Miruna - 2019
In this paper, the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe’s law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's...
Persistent link: https://www.econbiz.de/10011976108
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Metcalfe's law and log-period power laws in the cryptocurrencies market
Pele, Daniel Traian; Mazurencu-Marinescu-Pele, Miruna - 2019
In this paper the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe’s law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's...
Persistent link: https://www.econbiz.de/10012007412
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Conditional Threshold Effects of Stock Market Volatility on Crude Oil Market Volatility
Motegi, Kaiji; Hamori, Shigeyuki - 2023
This paper analyzes conditional threshold effects of stock market volatility on crude oil market volatility. We use the conditional threshold autoregressive (CoTAR) model, a novel extension of TAR from a constant to time-varying threshold. The conditional threshold is specified as an empirical...
Persistent link: https://www.econbiz.de/10014353102
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Backtesting Global Growth-at-Risk
Brownlees, Christian T. - 2020
We conduct an out-of-sample backtesting exercise of Growth-at-Risk (GaR) predictions for 24 OECD countries. We consider forecasts constructed from quantile regression and GARCH models. The quantile regression forecasts are based on a set of recently proposed measures of downside risks to GDP,...
Persistent link: https://www.econbiz.de/10012847547
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Are Cryptocurrencies Real Financial Bubbles? Evidence from Quantitative Analyses
Bianchetti, Marco - 2018
The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices which is mainly driven by investor sentiment, becoming a...
Persistent link: https://www.econbiz.de/10012931458
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Flight to Safety in Volatility Forecasting
Ruzzi, Dario - 2018
The benefits of using flight-to-safety (FTS) in volatility forecasting are assessed within a multivariate GARCH …
Persistent link: https://www.econbiz.de/10012916710
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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
forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation …
Persistent link: https://www.econbiz.de/10011553303
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Asymmetric realized volatility risk
Allen, David E.; McAleer, Michael; Scharth, Marcel - 2014 - This version: June 2014
forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation …
Persistent link: https://www.econbiz.de/10010366935
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Deep learning, jumps, and volatility bursts
Bashchenko, Oksana; Marchal, Alexis - 2020
We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the … generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the …: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach …
Persistent link: https://www.econbiz.de/10012181300
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Forecasting Stock Returns in Good and Bad Times : The Role of Market States
Huang, Dashan - 2017
This paper proposes a two-state predictive regression model and shows that stock market 12-month return (TMR), the time-series momentum predictor of Moskowitz, Ooi, and Pedersen (2012), forecasts the aggregate stock market negatively in good times and positively in bad times. The out-of-sample...
Persistent link: https://www.econbiz.de/10012974764