Regime changes in Bitcoin GARCH volatility dynamics
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TLDR
It is found strong evidence of regime changes in the GARCH process and it is shown that MSGARCH models outperform single–regime specifications when predicting the VaR.About:
This article is published in Finance Research Letters.The article was published on 2019-06-01 and is currently open access. It has received 171 citations till now. The article focuses on the topics: Volatility (finance) & Autoregressive conditional heteroskedasticity.read more
Citations
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Cryptocurrencies as a financial asset: A systematic analysis
TL;DR: A systematic review of the empirical literature based on the major topics that have been associated with the market for cryptocurrencies since their development as a financial asset in 2009 is presented in this article, where the authors provide a systematic analysis of the main topics that influence the perception of cryptocurrencies as a credible investment asset class and legitimate of value.
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Conditional tail-risk in cryptocurrency markets
TL;DR: In this article, the authors used C o V a R to estimate the conditional tail-risk in the markets for bitcoin, ether, ripple and litecoin and found that these cryptocurrencies are highly exposed to tail risk within cryptomarkets.
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Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis
TL;DR: In this article, the conditional volatility dynamics along with interlinkages and conditional correlations between three pairs of cryptocurrencies, namely Bitcoin-Ether, Bitcoin-Litecoin, and Ether Litecoin, were examined through the application of three pairwise bivariate BEKK models.
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Modelling volatility of cryptocurrencies using Markov-Switching GARCH models
TL;DR: This paper aims to select the best model or set of models for modelling volatility of the four most popular cryptocurrencies, i.e. Bitcoin, Ethereum, Ripple and Litecoin by backtesting VaR and ES as well as using a Model Confidence Set (MCS) procedure for their loss functions.
Posted Content
Cryptocurrency Trading: A Comprehensive Survey.
Fan Fang,Carmine Ventre,Michail Basios,Hoiliong Kong,Leslie Kanthan,Lingbo Li,David Martinez-Regoband,Fan Wu +7 more
TL;DR: This paper provides a comprehensive survey of cryptocurrency Trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto- assets, technical trading and others).
References
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Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
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Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
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On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.
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Evaluating Interval Forecasts.
TL;DR: In this paper, a consistent framework for conditional interval forecast evaluation with higher-order moment dynamics is presented. But this framework is not suitable for the case of exchange rate forecasting, where higher order moment dynamics are present.
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CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles *
TL;DR: In this article, the authors propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies (such as normality or i.i.d. returns).