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Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models
Yildirim, Hakan; Bekun, Festus Victor - In: Future business journal 9 (2023), pp. 1-8
market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of … to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an … the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection …
Persistent link: https://www.econbiz.de/10014382180
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Bitcoin return dynamics volatility and time series forecasting
Anand, Punit; Sharan, Anand Mohan - In: International Journal of Financial Studies : open … 13 (2025) 2, pp. 1-16
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes … reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA …
Persistent link: https://www.econbiz.de/10015433952
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Chapter 62 A Practitioner's Approach to Estimating Intertemporal Relationships Using Longitudinal Data: Lessons from Applications in Wage Dynamics
MaCurdy, Thomas - In: Handbook of econometrics : volume 6A, (pp. 4057-4167). 2007
This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...
Persistent link: https://www.econbiz.de/10014024953
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Generalized exogenous processes in DSGE : a Bayesian approach
Meyer-Gohde, Alexander; Neuhoff, Daniel - 2015
The Reversible Jump Markov Chain Monte Carlo (RJMCMC) method can enhance Bayesian DSGE estimation by sampling from a posterior distribution spanning potentially nonnested models with parameter spaces of different dimensionality. We use the method to jointly sample from an ARMA process of unknown...
Persistent link: https://www.econbiz.de/10010503919
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Generalized exogenous processes in DSGE : a Bayesian approach
Meyer-Gohde, Alexander; Neuhoff, Daniel - 2018 - This version: September 24, 2018
We relax the standard assumption in the dynamic stochastic general equilibrium (DSGE) literature that exogenous processes are governed by AR(1) processes and estimate ARMA (p,q) orders and parameters of exogenous processes. Methodologically, we contribute to the Bayesian DSGE literature by using...
Persistent link: https://www.econbiz.de/10011901706
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Explosive episodes and time-varying volatility : a new MARMA-GARCH model applied to cryptocurrencies
Hecq, Alain W. J.; Velasquez-Gaviria, Daniel - In: Econometrics : open access journal 13 (2025) 2, pp. 1-25
Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility … explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are … reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news (𝛼) while …
Persistent link: https://www.econbiz.de/10015437136
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How to compare market efficiency? : the Sharpe ratio based on the ARMA-GARCH forecast
Liu, Lin; Chen, Qiguang - In: Financial innovation : FIN 6 (2020) 38, pp. 1-21
This paper derives a new method for comparing the weak-form efficiency of markets. The author derives the formula of the Sharpe ratio from the ARMA-GARCH model and finds that the Sharpe ratio just depends on the coefficients of the AR and MA terms and is not affected by the GARCH process. For...
Persistent link: https://www.econbiz.de/10012296006
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Generalized exogenous processes in DSGE: A Bayesian approach
Meyer-Gohde, Alexander; Neuhoff, Daniel - 2018
We relax the standard assumption in the dynamic stochastic general equilibrium (DSGE) literature that exogenous processes are governed by AR(1) processes and estimate ARMA (p,q) orders and parameters of exogenous processes. Methodologically, we contribute to the Bayesian DSGE literature by using...
Persistent link: https://www.econbiz.de/10011902326
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Generalized exogenous processes in DSGE: A Bayesian approach
Meyer-Gohde, Alexander; Neuhoff, Daniel - 2015
The Reversible Jump Markov Chain Monte Carlo (RJMCMC) method can enhance Bayesian DSGE estimation by sampling from a posterior distribution spanning potentially nonnested models with parameter spaces of different dimensionality. We use the method to jointly sample from an ARMA process of unknown...
Persistent link: https://www.econbiz.de/10011335461
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A Flexible State Space Model and its Applications
Qian, Hang - Volkswirtschaftliche Fakultät, … - 2012
The standard state space model (SSM) treats observations as imprecise measures of the Markov latent states. Our flexible SSM treats the states and observables symmetrically, which are simultaneously determined by historical observations and up to first-lagged states. The only distinction between...
Persistent link: https://www.econbiz.de/10011108582