Editorial

3 pages, 166 KiB

Open AccessEditorial

by Christian M. Hafner

J. Risk Financial Manag. 2020, 13(1), 7; https://doi.org/10.3390/jrfm13010007 - 03 Jan 2020

Cited by 3 | Viewed by 3061

Alternative assets, defined by their low correlation with classical financial assets, have become an important investment vehicle in times of negative interest rates and in the aftermath of the global economic and financial crisis. Hedge funds increasingly invest in physical assets such as [...] Read more.

Alternative assets, defined by their low correlation with classical financial assets, have become an important investment vehicle in times of negative interest rates and in the aftermath of the global economic and financial crisis. Hedge funds increasingly invest in physical assets such as fine art, wine, or diamonds. Although digital and not physical, cryptocurrencies share many features of alternative assets, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This special issue covers a broad variety of topics in financial technology, and provides a state-of-the-art overview of cryptocurrencies from economic, financial, statistical and technical points of view. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

Research

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19 pages, 2834 KiB

Open AccessArticle

Do Diamond Stocks Shine Brighter than Diamonds?

by Vera Jotanovic and Rita Laura D’Ecclesia

J. Risk Financial Manag. 2019, 12(2), 79; https://doi.org/10.3390/jrfm12020079 - 03 May 2019

Cited by 4 | Viewed by 4401

Abstract

This paper addresses two practical investment questions: Is investing in the diamond equity market a more feasible and liquid alternative to investing in diamonds? Additionally, is diamond equity affected by polished diamond prices? We assemble an original database of diamond mining stock prices [...] Read more.

This paper addresses two practical investment questions: Is investing in the diamond equity market a more feasible and liquid alternative to investing in diamonds? Additionally, is diamond equity affected by polished diamond prices? We assemble an original database of diamond mining stock prices traded on main stock exchanges in order to assess their relationship with diamond prices. Our results show that the market of diamond-mining stocks does not represent a valid investment alternative to the diamond commodity. Diamond equity returns are not driven by diamond price dynamics but rather by local market stock indices. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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12 pages, 465 KiB

Open AccessArticle

Sentiment-Induced Bubbles in the Cryptocurrency Market

by Cathy Yi-Hsuan Chen and Christian M. Hafner

J. Risk Financial Manag. 2019, 12(2), 53; https://doi.org/10.3390/jrfm12020053 - 01 Apr 2019

Cited by 44 | Viewed by 9401

Abstract

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model [...] Read more.

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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20 pages, 3751 KiB

Open AccessArticle

Bitcoin at High Frequency

by Leopoldo Catania and Mads Sandholdt

J. Risk Financial Manag. 2019, 12(1), 36; https://doi.org/10.3390/jrfm12010036 - 15 Feb 2019

Cited by 20 | Viewed by 7714

Abstract

This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and [...] Read more.

This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above

80 %

. We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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15 pages, 947 KiB

Open AccessArticle

Statistical Arbitrage in Cryptocurrency Markets

by Thomas Günter Fischer, Christopher Krauss and Alexander Deinert

J. Risk Financial Manag. 2019, 12(1), 31; https://doi.org/10.3390/jrfm12010031 - 13 Feb 2019

Cited by 28 | Viewed by 15983

Abstract

Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., ^{Takeuchi and Lee} (²⁰¹³); ^{Moritz and Zimmermann} (²⁰¹⁴); ^{Krauss et al.} ( [...] Read more.

Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., ^{Takeuchi and Lee} (²⁰¹³); ^{Moritz and Zimmermann} (²⁰¹⁴); ^{Krauss et al.} (²⁰¹⁷). With our paper, we pose the question how such a statistical arbitrage approach would fare in the cryptocurrency space on minute-binned data. Specifically, we train a random forest on lagged returns of 40 cryptocurrency coins, with the objective to predict whether a coin outperforms the cross-sectional median of all 40 coins over the subsequent 120 min. We buy the coins with the top-3 predictions and short-sell the coins with the flop-3 predictions, only to reverse the positions after 120 min. During the out-of-sample period of our backtest, ranging from 18 June 2018 to 17 September 2018, and after more than 100,000 trades, we find statistically and economically significant returns of 7.1 bps per day, after transaction costs of 15 bps per half-turn. While this finding poses a challenge to the semi-strong from of market efficiency, we critically discuss it in light of limits to arbitrage, focusing on total volume constraints of the presented intraday-strategy. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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30 pages, 787 KiB

Open AccessArticle

Testing Stylized Facts of Bitcoin Limit Order Books

by Matthias Schnaubelt, Jonas Rende and Christopher Krauss

J. Risk Financial Manag. 2019, 12(1), 25; https://doi.org/10.3390/jrfm12010025 - 05 Feb 2019

Cited by 15 | Viewed by 9535

Abstract

The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on [...] Read more.

The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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15 pages, 1093 KiB

Open AccessArticle

Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

by Takuya Shintate and Lukáš Pichl

J. Risk Financial Manag. 2019, 12(1), 17; https://doi.org/10.3390/jrfm12010017 - 21 Jan 2019

Cited by 50 | Viewed by 14082

Abstract

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case [...] Read more.

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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18 pages, 929 KiB

Open AccessArticle

Inflation Propensity of Collatz Orbits: A New Proof-of-Work for Blockchain Applications

by Fabian Bocart

J. Risk Financial Manag. 2018, 11(4), 83; https://doi.org/10.3390/jrfm11040083 - 27 Nov 2018

Cited by 7 | Viewed by 5209

Abstract

Cryptocurrencies such as Bitcoin rely on a proof-of-work system to validate transactions and prevent attacks or double-spending. A new proof-of-work is introduced which seems to be the first number theoretic proof-of-work unrelated to primes: it is based on a new metric associated to [...] Read more.

Cryptocurrencies such as Bitcoin rely on a proof-of-work system to validate transactions and prevent attacks or double-spending. A new proof-of-work is introduced which seems to be the first number theoretic proof-of-work unrelated to primes: it is based on a new metric associated to the Collatz algorithm whose natural generalization is algorithmically undecidable: the inflation propensity is defined as the cardinality of new maxima in a developing Collatz orbit. It is numerically verified that the distribution of inflation propensity slowly converges to a geometric distribution of parameter

0.714 \approx \frac{(π - 1)}{3}

as the sample size increases. This pseudo-randomness opens the door to a new class of proofs-of-work based on congruential graphs. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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19 pages, 694 KiB

Open AccessArticle

Blockchain-Based ICOs: Pure Hype or the Dawn of a New Era of Startup Financing?

by Lennart Ante, Philipp Sandner and Ingo Fiedler

J. Risk Financial Manag. 2018, 11(4), 80; https://doi.org/10.3390/jrfm11040080 - 21 Nov 2018

Cited by 58 | Viewed by 12662

Abstract

This study explores the determinants of initial coin offering (ICO) success, where success is defined as the amount of capital a project could raise. ICOs are a tool for startups in the blockchain ecosystem to raise early capital with relative ease. The market [...] Read more.

This study explores the determinants of initial coin offering (ICO) success, where success is defined as the amount of capital a project could raise. ICOs are a tool for startups in the blockchain ecosystem to raise early capital with relative ease. The market for ICOs has grown at a rapid pace since its start in 2013. We analyze a unique dataset of 278 projects that finished their ICOs by August 2017 to assess determinants of funding success that we derive from the crowdfunding and venture capital literature. Our results show that ICOs exhibit similarities to classical crowdfunding and venture capital markets. Specifically, we identify resemblances in determinants of funding success regarding human capital characteristics, business model quality, project elaboration, and social media activity. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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18 pages, 2186 KiB

Open AccessArticle

An Analysis of Bitcoin’s Price Dynamics

by Frode Kjærland, Aras Khazal, Erlend A. Krogstad, Frans B. G. Nordstrøm and Are Oust

J. Risk Financial Manag. 2018, 11(4), 63; https://doi.org/10.3390/jrfm11040063 - 15 Oct 2018

Cited by 53 | Viewed by 21243

Abstract

This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of [...] Read more.

This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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19 pages, 802 KiB

Open AccessFeature PaperArticle

Can Bitcoin Replace Gold in an Investment Portfolio?

by Irene Henriques and Perry Sadorsky

J. Risk Financial Manag. 2018, 11(3), 48; https://doi.org/10.3390/jrfm11030048 - 14 Aug 2018

Cited by 36 | Viewed by 14088

Abstract

Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic [...] Read more.

Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An analysis of the economic value shows that risk-averse investors will be willing to pay a high performance fee to switch from a portfolio with gold to a portfolio with bitcoin. These results are robust to the inclusion of trading costs. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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12 pages, 875 KiB

Open AccessArticle

Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis

by Christian Conrad, Anessa Custovic and Eric Ghysels

J. Risk Financial Manag. 2018, 11(2), 23; https://doi.org/10.3390/jrfm11020023 - 10 May 2018

Cited by 157 | Viewed by 23316

Abstract

We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We [...] Read more.

We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility. Full article

(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)

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