Decentralized finance (DeFi) is a brand-new disruptive procedure that encourages the use of blockchain technology for developing and distributing a variety of financial goods and services. This study investigates the time-varying and asymmetric interplay between DeFi and CEE stock returns, concentrated around the COVID-19 outbreak and the Russo-Ukrainian conflict. While the associations between other cryptocurrencies and conventional assets have been studied, DeFi assets have not. For this purpose, we employ the multivariate DECO-GARCH model and cross-quantilogram framework. The results reveal a positive equicorrelation between DeFi and CEE stock market returns. Notably, the influence of DeFi on CEE stock markets is greater during the COVID-19 outbreak and the Russo-Ukrainian conflict than in the other periods. Furthermore, the cross-quantilogram estimations uncover that CEE stock markets depend less on the DeFi market at longer lag lengths. This means that the diversification benefits of DeFi against CEE stock market returns are more important for long-run investment horizons. In general, our research offers a new understanding of dependence structures, which might help investors make better investment decisions and direct their trading strategies.
This study examines the interconnectedness between central bank digital currencies (CBDC) index, digital assets and financial stability. First, we use the CBDC index as a measure of financial stability and examine its connectedness with other known measures of financial stability used in the literature. Secondly, we analyse the connectedness of CBDC index with digital assets such as cryptocurrencies and non-fungible tokens and various measures of financial stability. By analysing index returns of CBDC data and applying various connectedness measures to CBDC index, cryptocurrencies, stablecoins and NFTs, we gain insights into the relationships among these assets within a framework. The findings reveal a significant level of connectedness between CBDCs index, digital assets and financial stability. Our analysis shows a weak positive connectedness between CBDCs index and digital assets, indicating that movements in the CBDC index are not closely related to the performance of various digital assets and have a very small contribution to the changes in the returns of digital assets. Furthermore, the study finds bidirectional connectedness between CBDCs and other financial stability measures, suggesting that changes in CBDC performance can influence the overall stability of the financial system, and vice versa. This highlights the importance of carefully considering the design and implementation of CBDCs to ensure they support financial stability objectives.
Kripto para birimleri, 2009 yılında ortaya çıkmalarından bu yana oldukça popüler hale gelmiştir. Özellikle Bitcoin'in 3 Ocak 2009'da piyasaya sürülmesinden sonra, diğer kripto para birimlerinin piyasaya çıkışı hız kazanmıştır. Bu popülerlik artışının ardından, kripto para birimlerinin tahmini önemli bir konu haline gelmiştir. Bu çalışmanın ana amacı, Bitcoin (BTC), Ethereum (ETH) ve Binance (BNB) kripto para getirilerini öngörmek için geleneksel zaman serisi yöntemlerinden olan ARIMA-GARCH ile birlikte LSTM (Long Short-Term Memory) derin öğrenme yaklaşımını kullanarak elde edilen tahmin performanslarını karşılaştırmaktır. Bu çerçevede, çalışma literatüre yeni bir katkı sunmayı amaçlamaktadır. Her bir kripto para birimi için farklı zaman aralıklarında günlük veriler kullanılmış ve bu veriler %90 eğitim ve %10 test verisi olarak bölünmüştür. Çalışmada, yöntemler RMSE ve MSE değerlendirme kriterleri kullanılarak karşılaştırılmıştır. Genel olarak, BTC serisinde ARIMA-GARCH yöntemi eğitim verisinde daha iyi sonuçlar gösterirken, test verisi için LSTM yöntemi daha etkili olmuştur. BNB serisinde ise hem eğitim hem de test verisi için LSTM yöntemi daha üstün performans sergilemiştir. ETH serisinde ise her iki veri seti için ARIMA-GARCH yöntemi daha iyi sonuçlar ortaya koymaktadır. Bu çalışma, finansal veri tahmininde her iki yöntemin de önemli bir performans sergileyebildiğini vurgulamaktadır.
Abstract This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens (NFTs) and conventional currencies using the time-varying parameter vector autoregressions approach. We reveal that the total connectedness between these markets is weak, implying that investors may increase the diversification benefits of their multicurrency portfolios by adding NFTs. We also find that NFTs are net transmitters of both return and volatility spillovers; however, in the case of return spillovers, the influence of NFTs on conventional currencies is more pronounced than that of volatility shock transmissions. The dynamic exercise reveals that the returns and volatility spillovers vary over time, largely increasing during the onset of the Covid-19 crisis, which deeply affected the relationship between NFTs and the conventional currencies markets. Our findings are useful for currency traders and NFT investors seeking to build effective cross-currency and cross-asset hedge strategies during systemic crises.
Although there are currently four retail central bank digital currencies in circulation, no central bank has yet issued the wholesale form of a central bank digital currency. There are good reasons to do so, however, and central banks have already conducted projects in this area. A wholesale central bank digital currency could be issued in different ways. This article presents two "polar" scenarios, with a restrained and an extensive use of the possibilities offered by recourse to distributed ledger technology. Their consequences for monetary policy are discussed, and some precautions for central banks that intend to launch a wholesale central bank digital currency are underlined.
This study examines whether precious metals, industrial metals, energy and agricultural commodities, or cryptocurrencies form trustworthy safe havens against extreme price volatility of major global bank stock indices during black-swan events such as the COVID-19 pandemic and the Russia-Ukraine conflict. Using daily data and applying Quantile-VAR dynamic pairwise and extended joint connectedness methodologies, we investigate dynamic connectedness between major financial assets and major bank indices during exceptional crises. Findings provide evidence that crude oil and both Ethereum and Bitcoin present evidence of propagating significant shocks towards bank stock indices during crises, but other large-cap cryptocurrencies present no evidence of any specific influence. Further, gold, natural gas, and wheat are identified as the main absorbers of spillovers from banking indices during analysed crises, with more pronounced effects identified during exceptional phases of volatility. Such findings suggest that risk in the banking sector can be efficiently hedged by traditional safe havens such as gold and counterbalanced by highly outperforming assets such as natural gas and wheat. The study significantly contributes to understanding the interplay between banking sectors and various financial assets during crises and the subsequent strategies available for managing systemic risks, providing valuable insights for policymakers, regulators, and investors alike.
Abstract This study uses the Structural Factor Augmented VAR in exogenous variables (SFAVARx) approach to analyse the impact of cryptocurrency transactions on India’s major macroeconomic variables. Monthly data from May 2013 to October 2021 are sourced from the Reserve Bank of India and statista.com. The current form of cryptocurrency did not have a significant impact on inflation, production, the money supply, or major interest rates. However, given the increasing marginal participation in the crypto market, these important macroeconomic variables can be adversely affected in the future. The Central Bank Digital Currency (CBDC) with features related to India is being proposed as a proactive measure.
Kiana Kia, Bo Liu, Qian Li, Victor Song · 5 authors
ABSTRACT In this study, we explore price discovery across the following three Bitcoin markets: spot, futures, and exchange‐traded funds (ETFs). Employing the fractionally cointegrated vector autoregressive (FCVAR) model, we estimate price discovery in each market using minute‐level price data from October 19, 2021, the launch date of the first US Bitcoin futures‐based Bitcoin ETF, to December 30, 2022. The trivariate FCVAR analysis reveals that the three markets are pairwise cointegrated. In the spot‐futures pair, the spot market emerges as the dominant force in price discovery, while in the spot–ETF pair, the ETF market assumes a leading role. Our paper is the first to show the importance of the newly introduced Bitcoin ETF market in the price discovery process. Extending the analysis to the more recent period, we find that the approval of spot‐based Bitcoin ETFs has weakened the price discovery contribution of the futures‐based ETF and Bitcoin spot market has since become the dominant venue for price discovery.
Over recent decades, especially since the 2007–08 global financial crisis, the world has experienced a rapid shift toward the adoption of digital payment methods. This trend has been driven by the rise of cryptocurrencies and introduction of central bank digital currencies (CBDCs), which are accelerating the move to a cashless society. This article explores the socio-cultural and geopolitical values of cash, often overlooked in the transition to digital currencies such as bitcoin and CBDCs. Using a historical lens, we analyze the role of cash in shaping culture, history, and geopolitics and propose policy measures to integrate these values into the design of digital currencies.
Min-Bin Lin, Cathy Yi‐Hsuan Chen, Wolfgang Karl Härdle
This study investigates cryptocurrency volatility dynamics, particularly focusing on Ethereum (ETH). We dissect long- and short-term volatility components to gain deeper insights into its evolution. This approach allows studying the impact of ETH’s Merge upgrade, replacing Proof-of-Work with Proof-of-Stake on September 15, 2022. Employing 29 empirical factors related to blockchain functionality and crypto market characteristics, we explore their long-term equilibrium connection with price volatility. Our findings reveal that scalability factors and wealth dis- tribution significantly influence volatility persistence, ultimately highlighting the stability-enhancing impact of Ethereum’s Merge upgrade.
Spot bitcoin ETFs have been recently approved in the U.S., increasing retail and insti tutional investors’ 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.’s 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 bitcoin’s inflationary hedging property is sensitive to the price index – it only holds for CPI shocks – and to the period of analysis — the hedging property stems primarily from sample periods before the increasing institutional adoption of BTC (“early days”). 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.
This paper explores the impact of sentiment on return spillovers among seven major Non-Fungible Tokens (NFTs). Using daily sentiment data from Thomson Reuters MarketPysch Indices and controlling for uncertainty factors and NFT sales, we examine the relationship between media sentiment and NFTs return spillovers using a TVP-VAR model. Our findings show that individual NFTs sentiment is important for spillover dynamics and the effect of sentiment changes based on market uncertainty. The study highlights the need for NFTs investors to focus on market sentiment themes rather than overall sentiment
Nghiên cứu này sử dụng các mô hình GARCH, bao gồm EGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1) và APARCH(1,1) để khảo sát sự bất đối xứng trong biến động tỷ suất sinh lợi của các loại tiền điện tử như Bitcoin, Ethereum, Ripple (XRP), Binance Coin (BNB) và DigiByte (DGB) trong khoảng thời gian từ ngày 01 tháng 01 năm 2018 đến ngày 31 tháng 5 năm 2023. Kết quả cho thấy mô hình EGARCH(1,1) là mô hình tốt nhất để mô tả hiệu ứng bất đối xứng trong biến động tỷ suất sinh lợi của các chuỗi tiền điện tử. Sự biến động tăng nhiều hơn trong phản ứng với cú sốc tích cực hơn là cú sốc tiêu cực, hàm ý một hiệu ứng bất đối xứng khác với hiệu ứng thường thấy trên thị trường chứng khoán. Kết quả nghiên cứu giúp nhà đầu tư và nhà quản lý rủi ro trong thị trường tiền điện tử hiểu rõ hơn về sự biến động giá, nhận biết, đánh giá rủi ro một cách chính xác hơn và đưa ra các chiến lược đầu tư phù hợp.
Stylianos Asimakopoulos, Marco Lorusso, Francesco Ravazzolo
We develop and estimate a DSGE model to evaluate the economic repercussions of cryptocurrency. In our model, cryptocurrency offers an alternative currency option to government currency, with endogenous supply and demand. We uncover a substitution effect between the real balances of government currency and cryptocurrency in response to technology, preferences and monetary policy shocks. We find that an increase in cryptocurrency productivity induces a rise in the relative price of government currency with respect to cryptocurrency. Since cryptocurrency and government currency are highly substitutable, the demand for the former increases whereas it drops for the latter. Our historical decomposition analysis shows that fluctuations in the cryptocurrency price are mainly driven by shocks in cryptocurrency demand, whereas changes in the real balances for government currency are mainly attributed to government currency and cryptocurrency demand shocks.
Bu çalışmanın amacı COVID-19 pandemisi döneminde yatırımcı kararlarında meydana gelen değişimleri pandemi öncesi ve sonrası dönemler şeklinde ortaya koyarak finansal sistem içerisinde yer alan ve etkilenen tarafların kararlarında yol gösterici veriler ortaya koymak ve literatüre katkıda bulunmaktır. Çalışma Türkiye örneği üzerinden COVID-19 pandemisi öncesi ve sonrasını içerecek şekilde ve en son güncel değerlerle 01/01/2018-24/02/2023 dönemini kapsamaktadır. Analizler Toda-Yamamoto prosedürünü Fourier fonksiyonu (FTY) ile zenginleştiren bir nedensellik testi kullanılarak yapılmıştır. Çalışma yapılan dönem Chow yapısal kırılma testi ile dört alt döneme ayrılmıştır. Çalışmada USD, Altın (AU) ve Bitcoin değişkenleri ile BIST 100 endeksi arasındaki nedensellik ilişkisi analiz edilmiştir. Yapılan analiz sonuçları pandemi öncesi ve sonrası dönemin birbirinden oldukça farklı nedensellik ilişkileri ortaya koyduğunu, pandeminin ilk şok dalgasında altının güvenli liman özelliğinin ortaya çıktığını, devam eden pandemi sürecinde ise altının bu özelliğini kaybettiği ve ele alınan tüm değişkenler arasındaki nedenselliklerin belirginleştiği görülmüştür. Pandemi sonrası dönemde ise pandemi öncesi döneme kıyasla sadece altının aynı şekilde tek taraflı nedensellik ilişkisine sahip olduğu diğer değişkenler olan USD ve Bitcoin’in BIST100 değişkeniyle nedensellik ilişkisinin tamamen kaybolduğu görülmüştür. Çalışma kriz dönemlerinin her bir aşamasında yatırımcı davranışlarının analiz edilmesi açısından literatüre önemli bir katkı sunmaktadır.
Abstract Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.