This study examines the impact of incorporating cryptocurrencies into global asset portfolios using ensemble approaches and a tracing strategy. We considered cryptocurrency ratios of 1%, 3%, and 5% for including cryptocurrencies. Benchmarking was performed using classical portfolio optimization strategies such as minimum variance portfolio (MVP), maximum diversification portfolio (MDP), equal risk contribution portfolio (ERCP), and hierarchical risk parity (HRP). The ensemble methods and tracing strategies we evaluated were the equally weighted portfolio (EWP), the linear combination portfolio (LCP), the return tracing portfolio (RTP), and the return volatility tracing portfolio (RVTP). EWP averages the weights of classical methods, while LCP combines the objective functions of three optimization methods. RTP and RVTP represent tracing strategy portfolios with monthly rebalancing, selecting the best-performing portfolio based on cumulative returns or a combination of cumulative returns and annualized volatility. Our findings reveal that increasing the cryptocurrency allocation improves performance metrics in ensemble portfolios but also leads to higher risk. In addition, including cryptocurrencies reduces transaction fees, especially evident in the LCP with a 5% allocation. In the case of a 3-month RTP, HRP emerged as the preferred strategy, outperforming the use of HRP alone. In the case of a 6-month RVTP, MVP remained the preferred choice, consistently achieving lower volatility.
Ilyas Agakishiev, Wolfgang Karl Härdle, Denis Becker, Xiaorui Zuo
Abstract There are many ways to model complex time series. The simplest approach is to increase the complexity, and thus, the flexibility of the model, for the entire time series. As an example, one could use a neural network. Another solution would be to change the parameters of a model dependent on the âstateâ or âregimeâ of the time series. A typical example here would be the Hidden Markov model (HMM). This paper combines the two concepts to create a Reinforcement Learning (RL) model that adds variables that depend on the state of the time series. To test the concept, the RL model is used with cryptocurrency data to determine the share to invest into the cryptocurrency index CRIX in order to maximize wealth. The results have shown that cryptocurrency metadata is useful as supplementary data for analysis of the respective prices. The Reinforcement learning model with regimes shows potential for investment management, but comes with some caveats.
This article provides a comparative analysis of financial regulations across different jurisdictions, including the United States the UK and the European Union that could be applied to crypto market. It discusses the economic, social, and technological factors driving the need for crypto regulation and explores the challenges and opportunities these regulations present for financial stability, consumer protection, and innovation. By examining the different regulatory approaches, the article offers insights into the development of a balanced regulatory framework that addresses the unique aspects of digital currencies, maintaining the innovative approach of the crypto market while safeguarding against risks.
Spot bitcoin ETFs have been recently approved in the U.S., increasing retail and insti tutional investorsâ attention to crypto. To contribute to the debate on whether bitcoin protects against inflation, we analyze the effect of inflation shocks on bitcoin returns through the estimation and inference of Vector Autoregressive Models (VARs), iden tifying inflation shocks as surprises in the U.S.âs CPI and Core PCE announcements. Based on monthly data between August 2010 and January 2023, the results indicate that bitcoin returns increase significantly after a positive inflationary shock, corrob orating empirical evidence that bitcoin can act as an inflation hedge. However, we observe that bitcoinâs inflationary hedging property is sensitive to the price index â it only holds for CPI shocks â and to the period of analysis â the hedging property stems primarily from sample periods before the increasing institutional adoption of BTC (âearly daysâ). Notably, the inflation hedge property of bitcoin (Gold) has dis appeared (strengthened) from the COVID-19 outbreak onwards. We conclude that the inflation-hedging property of bitcoin is context-specific and is likely to be diminishing as adoption increases.
Cryptocurrencies, which have begun to become an important rival to cash due to the changing lifestyle and technological developments, are gradually increasing their coverage area. Whether Bitcoin prices, which have exhibited different behaviors over the years since the day they were developed, are on a rational basis has become an important topic of discussion. Within the scope of this study, bitcoin prices between 2010 and 2023 were analyzed and factors that could make price behavior meaningful were tried to be determined. In addition, a forecast was also made in which Bitcoin prices for the coming years were calculated on a daily basis together with various statistical parameters using the the triple exponential smoothing method based on same historical data, and the results were discussed from various perspectives. In Bitcoin prices, which change mainly within the framework of supply and demand balance, attention has been drawn to the importance of different factors such as rational or irrational herd behavior, decisions taken about Bitcoin or news that may affect this balance and fall within the scope of behavioral finance. Along with the behavioral finance parameters that will make Bitcoin price behavior meaningful, it may not always be possible to attribute some changes in the relevant data to a specific reason. The main view supporting this situation is based on the personal nature of cryptocurrency itself.
Inflows to the newly-established bitcoin exchange traded funds (ETFs) surpassed $20 billion in the first several weeks of trading and are considered historic high by ETF standards. In this paper we provide early examination of the bitcoin spot ETFs listed on US exchanges, and their effect on bitcoin price formation. We establish several empirical facts: (1) daily capital flows to spot bitcoin ETFs exceed $500 million or roughly 10,000 bitcoins and surpass daily production of bitcoin by the factor of 5; (2) net flows to ETFs are a strong positive predictor of bitcoin price with R-squared of 95%; (3) most of bitcoin price appreciation is generated outside of the ETF trading hours; (4) increase in bitcoin price leads to an abnormal ETF trading volume; (5) inflows to bitcoin ETFs witness outflows from gold ETFs. Overall, during the period studied, capital flows to spot bitcoin ETFs emerge as a dominant single factor predicting positive valuation effects of bitcoin.
Adela BârĂŁ, Irina Georgescu, SimonaâVasilica Oprea, Marian Pompiliu Cristescu
In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiecâs Probability of Failure (POF) test and Christoffersenâs test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSDâBRENT and BTCUSDâS&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.
<abstract> <p>In this article, we describe the novel properties of Bitcoin and green financial assets and empirically examine the connectedness between Bitcoin and two green financial assets (i.e., carbon emissions, green bonds) and two representative markets of conventional assets (i.e., oil and emerging stock). This study also analyzes whether Bitcoin, carbon, green bonds, oil, and emerging stock assets can hedge against any market turbulence. From observed findings, Bitcoin was not an effective substitute for green bond assets. Thus, Bitcoin is not a valuable hedge instrument to substitute green bonds to mitigate climate risks. More precisely, the findings of the study show that carbon assets outperform emerging stock assets amidst the COVID-19 crisis, while the stock markets incurred significant losses. Crucially, the innovative findings also played an important role for policymakers interested in decarbonizing the crypto-assets.</p> </abstract>
This study examines the connectedness between technology stocks, cryptocurrencies, and non-fungible tokens (NFTs) using daily returns and risk data. We found that while there is strong connectedness within asset classes, connectedness between different types of assets is weak. Structural breaks in the VAR system did not change the degree of connectedness. Our findings suggest that interconnectivity between these assets is not significant enough to indicate a high level of correlation. This research provides valuable insights into the interplay between these markets and suggests diversifying portfolios to mitigate risks associated with these assets.
Mohamed Fakhfekh, Azza BĂŠjaoui, Aurelio F. Bariviera, Ahmed Jeribi
This paper investigates the connectedness among eighteen cryptocurrency assets including NFT, DeFi, gold-backed cryptocurrencies, and traditional cryptocurrencies. We also compute the Optimal hedge ratio for each pair of (gold-backed) cryptocurrency-NFT/DeFi and assess their hedge effectiveness. To this end, we use a combination of econometric methods. Our sample period goes from 01/11/2021 to 21/02/2023, making the empirical analysis insightful and interesting as it includes the Covid-19 health crisis and the RussiaâUkraine war. Our empirical findings highlight the dissimilarities between different cryptocurrencies in terms of connectedness with NFT/DeFi assets. They also reflect the diversification benefits generated by the inclusion of gold-backed cryptocurrencies into NFT/DeFi portfolios, in particular in times of unprecedented events. These findings could be useful for crypto-investors who search to diversify their portfolios.
ABSTRACT The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses highâfrequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers.
Abstract The cryptocurrency market is a complex and rapidly evolving financial landscape in which understanding the inter- and intra-asset dependencies among key financial variables, such as return and liquidity, is crucial. In this study, we analyze daily return and liquidity data for six major cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Binance Coin, Litecoin, and Dogecoin, spanning the period from June 3, 2020, to November 30, 2022. Liquidity is estimated using three low-frequency proxies: the Amihud ratio and the Abdi and Ranaldo (AR) and Corwin and Schultz (CS) estimators. To account for autoregressive and persistent effects, we apply the autoregressive integrated moving average-generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model and subsequently utilize the copula method to examine the interdependent relationships between the return on and liquidity of the six cryptocurrencies. Our analysis reveals strong cross-asset lower-tail dependence in return and significant cross-asset upper-tail dependence in illiquidity measures, with more pronounced dependence observed in specific cryptocurrency pairs, primarily involving Bitcoin, Ethereum, and Litecoin. We also observe that returns tend to be higher when liquidity is lower in the cryptocurrency market. Our findings have significant implications for portfolio diversification, asset allocation, risk management, and trading strategy development for investors and traders, as well as regulatory policy-making for regulators. This study contributes to a deeper understanding of the cryptocurrency marketplace and can help inform investment decision making and regulatory policies in this emerging financial domain.
The efficient market hypothesis encounters scrutiny from behavioral finance insights, highlighting the pronounced influence of investor emotions on market dynamics, a phenomenon especially evident in the tumultuous cryptocurrency markets. This investigation utilizes the autoregressive distributed lag (ARDL) model and the error correction model (ECM) to examine the impact of the Bitcoin Sentiment Index (BSI), also known as the Crypto Fear & Greed Index (CFGI), on Bitcoin returns, leveraging monthly data spanning from 2016 to 2021. The ARDL analysis identifies a positive and statistically significant correlation between BSI and Bitcoin returns, indicating that strong sentiment may beneficially affect Bitcoinâs long-term returns. Concurrently, the ECM analysis reveals that fluctuations in the BSI positively influence the changes in Bitcoin returns in the short term. The error correction term demonstrates a significantly negative value, signifying an expedient adjustment toward long-term equilibrium following transient disturbances. These findings remain robust upon the integration of additional macroeconomic control variables. Unlike prior studies centered on singular sentiment indicators or limited temporal analyses, this research employs an extensive sentiment measure over an extended duration. The integrated application of ARDL and ECM methodologies facilitates a thorough and rigorous examination of short-term fluctuations alongside long-term equilibrium dynamics.
Abstract This study examines how global geopolitical risks , threats , and acts impact the daily returns of 10 major cryptocurrencies (BTC, ETH, USDT, XRP, BNB, USDC, BCH, DOGE, LTC, and ADA). The statistically significant results that are robust to the consideration of alternative model specifications and control variables suggest that there is strong evidence for (i) ETH, XRP, BNB and BCH responding negatively to the shocks of geopolitical risks , (ii) BTC, ETH, BNB, BCH, LTC and ADA responding negatively to the shocks of geopolitical threats , and (iii) all 10 cryptocurrencies not responding to the shocks of geopolitical acts . As these 10 cryptocurrencies do not respond positively to any of the three shocks in a robust and statistically significant way either, it is implied that none of them offer a reliable hedge against geopolitical risks.