Journal of Risk and Financial Management

Editorial

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3 pages, 170 KiB

Open AccessEditorial

Recent Developments in Cryptocurrency Markets: Co-Movements, Spillovers and Forecasting

by Thanasis Stengos

J. Risk Financial Manag. 2021, 14(3), 91; https://doi.org/10.3390/jrfm14030091 - 26 Feb 2021

Cited by 1 | Viewed by 2573

Abstract

The emergence of Bitcoin and other cryptocurrencies has led to an explosion of trading and speculation in once nontraditional markets [...] Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

Research

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16 pages, 1196 KiB

Open AccessArticle

Regime-Dependent Good and Bad Volatility of Bitcoin

by Kislay Kumar Jha and Dirk G. Baur

J. Risk Financial Manag. 2020, 13(12), 312; https://doi.org/10.3390/jrfm13120312 - 7 Dec 2020

Cited by 6 | Viewed by 2559

Abstract

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility [...] Read more.

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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15 pages, 266 KiB

Open AccessArticle

Forecasting the Returns of Cryptocurrency: A Model Averaging Approach

by Hui Xiao and Yiguo Sun

J. Risk Financial Manag. 2020, 13(11), 278; https://doi.org/10.3390/jrfm13110278 - 13 Nov 2020

Cited by 6 | Viewed by 3075

Abstract

This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several [...] Read more.

This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several popular penalized least squares estimators to explain the cryptocurrencies’ returns. We further introduce a novel model averaging approach or the shrinkage Mallows model averaging (SMMA) estimator for forecasting. First, we find that the returns for most cryptocurrencies are sensitive to volatilities from major financial markets. The returns are also prone to the changes in gold prices and the Forex market’s current and lagged information. Then, when forecasting cryptocurrencies’ returns, we further find that an ARDL(p,q) model estimated by the SMMA estimator outperforms the competing estimators and models out-of-sample. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

14 pages, 2915 KiB

Open AccessArticle

Dynamic Connectedness between Bitcoin, Gold, and Crude Oil Volatilities and Returns

by Serda Selin Ozturk

J. Risk Financial Manag. 2020, 13(11), 275; https://doi.org/10.3390/jrfm13110275 - 10 Nov 2020

Cited by 21 | Viewed by 4061

Abstract

This paper analyzes the connectedness among bitcoin, gold, and crude oil between 3 January 2017 and 31 December 2019. The paper’s motivation is based upon the idea that bitcoin can be similar to gold in terms of its hedging properties and can be [...] Read more.

This paper analyzes the connectedness among bitcoin, gold, and crude oil between 3 January 2017 and 31 December 2019. The paper’s motivation is based upon the idea that bitcoin can be similar to gold in terms of its hedging properties and can be used for hedging for different assets. Moreover, although it is more metaphorical, bitcoin is also accepted because it is mined like crude oil, namely, a commodity. These similarities can be investigated by analyzing the connectedness among these financial assets. The connectedness results derived from both total connectedness and frequency connectedness methods indicate that volatility connectedness is higher than the return connectedness among these assets. Furthermore, connectedness in volatilities is mostly driven by medium frequency, although connectedness in returns mostly exists in high frequency. Therefore, these results suggest that investors should consider these financial assets for their diversification decisions. The results suggest that although diversification among these three assets is more difficult in the short- and medium-term, investors may benefit from diversification in the long-run. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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21 pages, 1964 KiB

Open AccessArticle

A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020

by Beatriz Vaz de Melo Mendes and André Fluminense Carneiro

J. Risk Financial Manag. 2020, 13(9), 192; https://doi.org/10.3390/jrfm13090192 - 25 Aug 2020

Cited by 6 | Viewed by 5374

Abstract

After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important [...] Read more.

After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015–2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins’ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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11 pages, 860 KiB

Open AccessCommunication

True versus Spurious Long Memory in Cryptocurrencies

by Dooruj Rambaccussing and Murat Mazibas

J. Risk Financial Manag. 2020, 13(9), 186; https://doi.org/10.3390/jrfm13090186 - 19 Aug 2020

Cited by 9 | Viewed by 3157

Abstract

We test whether the selected cryptocurrencies exhibit long memory behavior in returns and volatility. We use data on five most traded cryptocurrencies: Bitcoin, Litecoin, Ethereum, Bitcoin Cash, and XRP. Using recent tests of long memory developed against persistent and nonlinear alternatives, this paper [...] Read more.

We test whether the selected cryptocurrencies exhibit long memory behavior in returns and volatility. We use data on five most traded cryptocurrencies: Bitcoin, Litecoin, Ethereum, Bitcoin Cash, and XRP. Using recent tests of long memory developed against persistent and nonlinear alternatives, this paper finds that long memory is mostly rejected in returns. The tests fail to reject the null hypothesis of long memory in most cases across different volatility proxies and cryptocurrencies. The estimated memory parameters show that volatility is persistent, and when volatility is measured by log range, it is borderline nonstationary. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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15 pages, 2072 KiB

Open AccessArticle

GARCH Generated Volatility Indices of Bitcoin and CRIX

by Pierre J. Venter and Eben Maré

J. Risk Financial Manag. 2020, 13(6), 121; https://doi.org/10.3390/jrfm13060121 - 11 Jun 2020

Cited by 12 | Viewed by 4376

Abstract

In this paper, the pricing performance of the generalised autoregressive conditional heteroskedasticity (GARCH) option pricing model is tested when applied to Bitcoin (BTCUSD). In addition, implied volatility indices (30, 60-and 90-days) of BTCUSD and the Cyptocurrency Index (CRIX) are generated by making use [...] Read more.

In this paper, the pricing performance of the generalised autoregressive conditional heteroskedasticity (GARCH) option pricing model is tested when applied to Bitcoin (BTCUSD). In addition, implied volatility indices (30, 60-and 90-days) of BTCUSD and the Cyptocurrency Index (CRIX) are generated by making use of the symmetric GARCH option pricing model. The results indicate that the GARCH option pricing model produces accurate European option prices when compared to market prices and that the BTCUSD and CRIX implied volatility indices are similar when compared, this is consistent with expectations because BTCUSD is highly weighted when calculating the CRIX. Furthermore, the term structure of volatility indices indicate that short-term volatility (30 days) is generally lower when compared to longer maturities. Furthermore, short-term volatility tends to increase to higher levels when compared to 60 and 90 day volatility when large jumps occur in the underlying asset. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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14 pages, 1730 KiB

Open AccessArticle

Does Bitcoin Hedge Commodity Uncertainty?

by Khanh Hoang, Cuong C. Nguyen, Kongchheng Poch and Thang X. Nguyen

J. Risk Financial Manag. 2020, 13(6), 119; https://doi.org/10.3390/jrfm13060119 - 9 Jun 2020

Cited by 7 | Viewed by 3007

Abstract

This paper examines the connectedness between Bitcoin and commodity volatilities, including oil, wheat, and corn, during the period Oct. 2013–Jun. 2018, using time- and frequency-domain frameworks. The time-domain framework’s results show that the connectedness is 23.49%, indicating a low level of connection between [...] Read more.

This paper examines the connectedness between Bitcoin and commodity volatilities, including oil, wheat, and corn, during the period Oct. 2013–Jun. 2018, using time- and frequency-domain frameworks. The time-domain framework’s results show that the connectedness is 23.49%, indicating a low level of connection between Bitcoin and the commodity volatilities. Bitcoin contributes only 2.55% to the connectedness, while the wheat volatility index accounts for 12.51% of the total connectedness. The frequency connectedness shows that Bitcoin’s contribution to the total connectedness increases from high-frequency to low-frequency bands, and the total connectedness reaches up to 22.47%. It also indicates that Bitcoin is the spillover transmitter to the wheat volatility, while being the spillover receiver from the oil and corn volatilities. The findings suggest that Bitcoin could be a hedger for commodity volatilities. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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21 pages, 601 KiB

Open AccessArticle

Cryptocurrency Returns before and after the Introduction of Bitcoin Futures

by Pinar Deniz and Thanasis Stengos

J. Risk Financial Manag. 2020, 13(6), 116; https://doi.org/10.3390/jrfm13060116 - 4 Jun 2020

Cited by 5 | Viewed by 4062

Abstract

This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for [...] Read more.

This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for the pre and post periods. Besides the neighbourhood of the break time, the current period is also investigated as returns start to recover after some time. Search intensity is observed to be the most important variable for Bitcoin for all periods, whereas for the other cryptocurrencies there are other variables that seem more important in the pre period, while search intensity still stands out in the post period. Furthermore, GARCH analyses suggest that search intensity increases the volatility of Bitcoin returns more in the post period than it does in the pre period. Our empirical findings suggest that the top five cryptocurrencies are substitutes before the launch of Bitcoin futures. However, this effect is lost, and moreover, there are spillover effects on altcoins during both the post and the recovery period. We find a spillover effect of the introduction of bitcoin futures on altcoins and this effect seems to persist during the recovery period. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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21 pages, 1016 KiB

Open AccessArticle

Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple

by Pınar Kaya Soylu, Mustafa Okur, Özgür Çatıkkaş and Z. Ayca Altintig

J. Risk Financial Manag. 2020, 13(6), 107; https://doi.org/10.3390/jrfm13060107 - 29 May 2020

Cited by 29 | Viewed by 5242

Abstract

This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi [...] Read more.

This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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10 pages, 913 KiB

Open AccessArticle

A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

by Theodore Panagiotidis, Thanasis Stengos and Orestis Vravosinos

J. Risk Financial Manag. 2020, 13(2), 33; https://doi.org/10.3390/jrfm13020033 - 13 Feb 2020

Cited by 15 | Viewed by 4286

Abstract

We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables [...] Read more.

We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017–2018 period. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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Review

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19 pages, 7497 KiB

Open AccessReview

Is Bitcoin Similar to Gold? An Integrated Overview of Empirical Findings

by Nikolaos A. Kyriazis

J. Risk Financial Manag. 2020, 13(5), 88; https://doi.org/10.3390/jrfm13050088 - 1 May 2020

Cited by 40 | Viewed by 9641

Abstract

This paper sets out to explore whether Bitcoin can be considered as a globally accepted asset that has a resemblance to gold, which is widely considered to be the safest choice. An integrated overview of the empirical findings generated by the nascent but [...] Read more.

This paper sets out to explore whether Bitcoin can be considered as a globally accepted asset that has a resemblance to gold, which is widely considered to be the safest choice. An integrated overview of the empirical findings generated by the nascent but increasingly proliferating literature concerning the nexus between Bitcoin and gold is provided. The majority of evidence reveals that Bitcoin has a long way to go before it acquires the same characteristics as the safe-haven asset of gold. Overall, Bitcoin is found to be an efficient hedge against oil and stock market indices, but to a lesser extent than gold. Bitcoin presents low or negative correlations or an asymmetric non-linear linkage with gold. Despite sharing some common features with traditional assets, Bitcoin is found to be a good hedging asset in portfolios with gold. Moreover, evidence reveals that gold is a better and more stable safe-haven investment than Bitcoin. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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17 pages, 1639 KiB

Open AccessReview

A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets

by Nikolaos A. Kyriazis

J. Risk Financial Manag. 2019, 12(4), 170; https://doi.org/10.3390/jrfm12040170 - 12 Nov 2019

Cited by 63 | Viewed by 9275

Abstract

This paper provides a systematic survey on return and volatility spillovers of cryptocurrencies based on the empirical results of relevant academic literature. Evidence reveals that Bitcoin is the most influential among digital coins mainly as a transmitter toward digital currencies but also as [...] Read more.

This paper provides a systematic survey on return and volatility spillovers of cryptocurrencies based on the empirical results of relevant academic literature. Evidence reveals that Bitcoin is the most influential among digital coins mainly as a transmitter toward digital currencies but also as a receiver of spillovers from virtual currencies and alternative assets. Ethereum, Litecoin, and Ripple present the most significant interlinkages with Bitcoin. Return spillovers are more pronounced but volatility spillovers often present a bi-directional character. Volatility shock transmission is detected among Bitcoin and national currencies, while economic policy uncertainty is not influential. This survey provides useful guidance in the hotly-debated issue of reform and decentralization of financial systems. Full article

(This article belongs to the Special Issue Recent Developments in Cryptocurrency Markets: Co-movements, Spillovers and Forecasting)

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