Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.
Klender Aimer Cortéz Alejandro, Martha del Pilar Rodríguez-García, Samuel Mongrut
In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models.
Suresh Kumar Oad Rajput, Ishfaque Ahmed Soomro, Najma Ali Soomro
This study introduces a comprehensive Google search volume based Bitcoin Sentiment Index (BSI) and investigates its symmetric and asymmetric association with Bitcoin's returns, volume, and volatility, and with United States Dollar (USD) exchange rates. Our results indicate a positive association of BSI with Bitcoin's returns and volume, but a negative relationship with its return volatility. Besides, Bitcoin's optimistic sentiments have an asymmetric relationship with the USD exchange rate in the short-run, and the Bitcoin price has an asymmetric and negative association with USD in the short-run and long-run. We also found a 35.47% speed of adjustment to long-run equilibrium.
Abstract. This research employs Capital Asset Pricing Model and foreign exchange exposure theory to explain how the value of financial stocks is affected by the home country cryptocurrency. Previous literature proposed that financial stocks were related to the economic or individual financial ratio, but rarely discussed the impact of a cryptocurrency variable in the digital economy. This paper presents specific findings to prove that cryptocurrency development causes structural change in the financial industry, by examining 67,166 panel data observations from China and Taiwan markets. We offer the following important conclusions: 1. Financial stocks in the China market suffer significantly higher impacts from home country cryptocurrency exposure than the Taiwan market. 2. Financial stocks in the China market are more greatly shocked by the CAPM three factors variables than the Taiwan market. 3. There are significant differences between the two financial markets. 4. The dynamics of the adjustment process of cryptocurrency evolution and the monetary system are key solutions for both markets. Keywords: cryptocurrency, Fin-Tech, Exchange rate Exposure. JEL Classification A14, D82, F65, G12, F3 Formulas: 2; fig.: 0; tabl.: 4; bibl. 31.
Ricardo de Souza Tavares, João F. Caldeira, Gerson de Souza Raimundo Júnior
This article analyzes whether cryptocurrencies’ inclusion improves stock portfolios’ performance and whether the application of portfolio selection methodologies would bring gains to investors in the digital currency market. Volatility Timing and Reward-to-Risk Timing methodologies were applied to a base containing only S&P 100 stocks, another containing only cryptocurrencies, and one mixing the previous two. The results suggest that the inclusion of cryptocurrencies has not brought performance gains to the stock portfolios and that the investor in the cryptocurrency market can benefit from the use of portfolio selection.
Lingling Qian, Yuexiang Jiang, Huaigang Long, RUOYI SONG
We are the first to explore the effect of economic policy uncertainty (EPU) and the COVID-19 pandemic on the correlation between the cryptocurrency index CRIX and the world stock market portfolio, as well as the hedging properties of CRIX. To this end, we mainly apply the dynamic conditional correlation model with mixed data sampling regressions, a threshold vector autoregressive model and the generalized impulse response function. We demonstrate that the correlation is influenced by the uncertainty stance of the economy and behaves differently in low-, medium- and high-uncertainty periods. Most of the abnormal market relations exist in high levels of EPU or during the COVID-19 period, and the impact of global EPU is greater than that of EPU originating in the United States, Europe, Russia and China. Moreover, the CRIX can serve as a hedge asset against the world stock market. The high (low) level of EPU has a significantly positive (negative) effect on the optimal hedge ratio of CRIX, which increases significantly during the COVID-19 period. Our findings have implications for risk management, portfolio allocations and hedging strategies.
This study explores the existing systemic barriers to intra-BRICS national currency use (“de-dollarization”) in currency swaps and trade finance. The author examines the current de-dollarization initiatives, as well as the actual levels of de-dollarization in Russia’s intra-BRICS settlements (as a representative sample), to find gaps between de-dollarization goals and current initiatives and offers a near-term phased solution to overcome these gaps and de-risk trade within BRICS. It is found that 1) the New Development Bank’s Contingency Reserve Arrangement has built-in systemic barriers which are preventing direct currency swaps between BRICS member states; 2) the Euro is replacing the Dollar as Russia’s preferred settlement currency within BRICS, indicating a gap between Russian traders’ settlement currency choice and BRICS de-dollarization priorities; and, furthermore, 3) while payment and settlement systems are being integrated and FinTech applications are being explored, efforts to fundamentally address the systemic market factors preventing national settlement use are missing. A phased solution is proposed to address the fundamental market barriers to national currencies by using smart contracts to de-risk intra-BRICS trade. Specific mechanisms are outlined to promote trade contracts in national currency and reduce dependency on both the Dollar/Euro and Western institutions (such as the IMF and Western commodities markets), a high-level architecture is proposed, and implementation considerations are discussed.
In recent years, there has been a growing interest on the combination of copulas with mixture model. The combination of vine copulas incorporated into a finite mixture model is also helpful to capture secret structures in a data. This paper aims to examine the relationship between bitcoin and other crypto money indicators with the CD Vine Copula Approach method. In the study, we use closing prices of Bitcoin, Bitcoin Cash, Ethereum, Litecoin, and IOT. The results show that there is a strong dependence between bitcoin and prominent financial indicators.
Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance. It is often that we encounter several risks, in practice, instead of single risk. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. The dependence structure is presented through vine copula. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. It is found that the VaR forecast of returns, by considering vine copula-based dependence among different returns, has higher forecast accuracy than that of returns under prefect dependence assumption as benchmark. In addition, through vine copula, the aggregate VaR forecast has not only lower value but also higher accuracy than the simple sum of individual VaR forecasts. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.
Andrew Meegan, Shaen Corbet, Charles Larkin, Brian M. Lucey
Blockchain technology appears to be ready to revolutionise a broad number of industries. However, the blockchain itself contains a number of inefficiencies and areas for improvement, namely: transaction fees and transaction speeds. Directed acyclic graphs (DAGs) address, and improve on these inefficiencies and a number of digital currencies utilising this technology have already begun to appear. This paper provides an explanation of the technology behind DAG-based assets, while identifying and highlighting strategic advantages that DAGs possess over traditional blockchains. We conduct an EGARCH volatility analysis of a range of blockchain-based and DAG-based cryptocurrencies in the aftermath of a range of market shocks, taking the form of regulatory announcements such as bans and broad restrictions for cryptocurrencies. We find that DAG-based assets become increasingly responsive to market shocks as they mature. Such behaviour mirrors that of established cryptocurrencies such as Bitcoin, Ethereum and Litecoin, providing evidence that DAG-based cryptocurrencies now share similar characteristics to traditional blockchain-chain based products.
Bill X. Hu, Joon Ho Hwang, Chinmay Jain, Jim Washam
We analyse 519.4 million Bitcoin orders placed on Gemini Exchange during January 2016-August 2019 and find limit orders dominate at 99.92%. We document order-based evidence of price manipulation during the Bitcoin bubble in late 2017, when the daily number of market orders during the bubble period more than triples the overall daily average. The changes in both prices and liquidity satisfy two criteria specified in Kyle and Viswanathan (2008) for the price manipulation definition. Moreover, we find a significant increase in market order imbalance associated with price manipulations modelled in Jarrow, Protter and Roch (2012).
Bu çalışmada amaç; Bitcoin, döviz kuru, Borsa İstanbul Endeksi ve faiz değişkenleri arasındaki ilişkileri Türkiye için 2013:11-2019:10 dönemi haftalık verileri kullanarak incelemektir. Çalışmada VAR modeli kurularak değişkenler arasındaki uzun dönem ve nedensellik ilişkileri araştırılmış, etki-tepki grafikleri ve varyans ayrışım tablosuyla analiz sonuçlandırılmıştır. Çalışma sonucunda Bitcoin ile diğer değişkenler arasında uzun dönemde herhangi bir eşbütünleşme ilişkisi ve nedensellik ilişkisi tespit edilememiş, ancak diğer değişkenlerin kendi aralarında nedensellik ilişkileri saptanmıştır. Etki-tepki grafiklerine göre Bitcoin’e verilen bir şoka döviz kuru üç haftalık negatif tepki göstermiş diğer haftalarda verilen tepki anlamsız olmuştur. Türkiye’de kripto paralar üzerinde belirli bir farkındalığın olduğu ancak bu farkındalığın uzun vadeli yatırım boyutunda ve makro değişkenleri etkileyebilecek güçte olmadığı görülmektedir. Türkiye’de yeni sayılabilecek olan bu teknolojinin yaygınlaşabilmesi için belirli bir zamana ihtiyaç vardır.
Abstract Accurate measurement of relationship between assets is sensitive to different market conditions in different horizons and has implications for portfolio optimization. Cryptocurrencies are new category of assets that can reduce the risk of well‐diversified portfolio including gold. The paper explores the connections between seven cryptocurrencies and gold at bear (bull) markets across time to uncover the hedging properties of cryptocurrencies for gold investors. Wavelet technique was used to decompose the daily return series of the assets into short‐, medium‐ and long‐term frequencies. Quantile regression (QR) and quantile‐in‐quantile regression (QQR) were applied on the decomposed series to establish the association between the assets over 19 quantiles ( τ = 0.05 to 0.95). QR results show all cryptocurrencies as hedges for gold regardless of market regime in the medium to long‐terms. QQR results depict inverse association at bear market but positive association at bull market across time suggesting hedging possibilities at bear markets. Our study provides precise information to investors, regulators and policy makers on risk mitigating strategies for extreme gold market fluctuations across time and market states.
Çalışmanın amacı; kripto para birimlerinden Bitcoin ve Litecoin piyasalarının etkinliğini ölçerek haftanın günü etkisinin varlığını 29.04.2013- 29.02.2020 tarihleri arasında günlük kapanış fiyatları kullanılarak incelenmesidir. İlgili dönemlerde her iki para birimine ait piyasaların etkinliğini incelemede ARMA, haftanın günü etkisinin olup olmadığının tespitinde ise Kruskal Wallis H testinden faydalanılmıştır. Çalışmanın sonunda her iki kripto para biriminin getirilerinin bir önceki zamandan bağımsız hareket ettiği yani ilgili dönemde bu kripto para piyasalarının etkin piyasaya benzer özellik taşıdığı ve haftanın günü etkisinin de varlığına rastlanılmadığı tespit edilmektedir.
Purpose In this paper, the authors examine the interconnectedness of four blockchain exchange-traded funds (ETFs) with other financial markets, such as stocks and cryptocurrencies. Design/methodology/approach A multivariate dynamic conditional correlation model is used to model the relationship of blockchain ETFs with equity and cryptocurrency markets. Risk-minimizing hedge ratios are calculated following the methods used in studies by Kroner and Sultan (1993) and Sadorsky (2012). Findings The empirical results show a high degree of correlation of blockchain ETF returns with returns of the NASDAQ Composite Index, while the level of comovement with Bitcoin is relatively low. Research limitations/implications The results imply that blockchain ETFs may be suitable for hedging purposes in a portfolio holding Bitcoin. Furthermore, investing in blockchain ETFs appears similar to investing in NASDAQ. Originality/value To the best of the authors’ knowledge, no studies have investigated the dynamic relationship of blockchain ETFs and other financial assets.
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.
Abstract While gaining more popularity both as a financial asset and a commodity, a number of cryptocurrencies are emerging with a loosely regulated market microstructure which is a challenge to their efficiency. We have ranked 6 out of the top 10 cryptocurrencies based on their inefficiency ratios, using a novel time‐varying generalised Hurst exponent methodology. All the six crypto markets exhibit a time‐varying efficiency throughout the studied period, thus indicating a varying degree of exploitable profitable trading opportunities. The inefficiency ratio indicates that Bitcoin is the third most inefficient market, while the first and second most inefficient markets are DASH and NEM, respectively, thus they provide the most abnormal profit opportunities. However, the most efficient crypto markets are Ethereum and Ripple according to the order of their rankings. Further research could be performed on the factors affecting the inefficiency index to understand the efficiency determination of these cryptocurrency markets.