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.
In the Decentralized Finance (DeFi) setting, we present a new parametrized family of Constant Function Market Makers (CFMMs) which we call the Generalized Mean Market Makers (G3Ms), based on the generalized means. The G3Ms are intermediate between the Arithmetic Mean and Geometric Mean CFMM models, which G3Ms incorporate as special cases. We also present an extension of the G3Ms, based on the so-called Generalized f-Means, called Generalized f-Mean Market Makers (Gf3Ms). We show in addition that the G3Ms possess certain properties preferable to those exhibited by either the Arithmetic Mean CFMM or the Geometric Mean CFMM alone.
Optimal simple rules for the monetary policy of the first stochastically dominant crypto-currency are derived in a Dynamic Stochastic General Equilibrium (DSGE) model, in order to provide optimal responses to changes in inflation, output, and other sources of uncertainty. The optimal monetary policy stochastically dominates all the previous crypto-currencies, thus the efficient portfolio is to go long on the stochastically dominant crypto-currency: a strategy-proof arbitrage featuring a higher Omega ratio with higher expected returns, inducing an investment-efficient Nash equilibrium over the crypto-market. Zero-knowledge proofs of the monetary policy are committed on the blockchain: an implementation is provided.
The aim of this study is to investigate the volatility spillover connectedness between NFTs attention and financial markets. This paper firstly proposes a new direct proxy for the public’s attention in the NFT market: the non-fungible tokens attention index (NFTsAI), based on 590m news stories from the LexisNexis News & Business database and applies the historical decomposition to assess the historical variations of the NFTsAI. Then the empirical analysis is performed via a TVP-VAR volatility spillover connectedness model. The empirical results show that NFTsAI indicates NFT markets are dominated by cryptocurrency, DeFi, equity, bond, commodity, F.X. and gold markets. And NFT markets are volatility spillover receivers. In addition, NFT assets could impede financial contagion and have significant diversification benefits. Employing a panel pooled OLS regression model as a supplementary analysis and a GARCH-MIDAS model as a robustness test. This study reveals that NFTsAI has sufficient power to explain the return of NFT assets from a fixed effect perspective, and NFTsAI contains useful forecasting information for both short and long-term volatility of NFT markets, separately. The new NFTsAI and the empirical findings contain useful insights for risk-averse investors, portfolio managers, institutional investors, academics and financial policy regulators.
Bitcoin has grown in popularity and has now attracted the attention of individual and institutional investors. Accurate Bitcoin price direction forecasts are important for determining the trend in Bitcoin prices and asset allocation. This paper addresses several unanswered questions. How important are business cycle variables like interest rates, inflation, and market volatility for forecasting Bitcoin prices? Does the importance of these variables change across time? Are the most important macroeconomic variables for forecasting Bitcoin prices the same as those for gold prices? To answer these questions, we utilize tree-based machine learning classifiers, along with traditional logit econometric models. The analysis reveals several important findings. First, random forests predict Bitcoin and gold price directions with a higher degree of accuracy than logit models. Prediction accuracy for bagging and random forests is between 75% and 80% for a five-day prediction. For 10-day to 20-day forecasts bagging and random forests record accuracies greater than 85%. Second, technical indicators are the most important features for predicting Bitcoin and gold price direction, suggesting some degree of market inefficiency. Third, oil price volatility is important for predicting Bitcoin and gold prices indicating that Bitcoin is a substitute for gold in diversifying this type of volatility. By comparison, gold prices are more influenced by inflation than Bitcoin prices, indicating that gold can be used as a hedge or diversification asset against inflation.
Bu çalışmanın amacı, en fazla ilgi gören kripto para birimleri arasında yer alan Bitcoin ile gelişmekte olan piyasalar arasında önde gelen Borsa İstanbul (BİST) endekslerinden BİST 100 (XU100), BİST Mali (XUMAL) ve BİST Teknoloji (XUTEK) endeksleri arasındaki ilişkilerin incelenmesi olarak belirlenmiştir. Bu amaçla çalışma kapsamında Borsa İstanbul 100 fiyat endeksi, Borsa İstanbul Mali fiyat endeksi ve Borsa İstanbul Teknoloji fiyat endeks ile Bitcoin arasındaki kısa ve uzun dönemli ilişki ARDL sınır testi yaklaşımı ile incelenmiştir. Elde edilen bulgular, Bitcoin fiyatı ile Borsa İstanbul Mali Endeksi arasında uzun dönem bir ilişkinin var olduğunu göstermiştir. Ancak, Bitcoin fiyatı ile diğer endeks fiyatları arasında uzun dönemli bir ilişkinin varlığına yönelik herhangi bir bulguya ulaşılamamıştır. Elde edilen kısa dönem bulgular ise Bitcoin fiyatı ile Borsa İstanbul Mali fiyat endeksi arasında herhangi anlamlı bir ilişkinin bulunmadığını göstermektedir.
The paper is devoted to studying the bitcoin blockchain as a new global phenomenon in monetary economics, which requires comprehending from the economic theory view - a self-regulating system of decentralized emission without participation of a central monetary authority. Mathematical modelling is the instrument of this studying. The author has analyzed the Bitcoin system parameters that determine dynamics of a self-regulating emission mechanism. This mechanism operates in a peer-to-peer computer network and provides a smooth increasing of the "money supply" with a gradually decreasing rate of growth. The limit of this growth is determined by maximal volume 21 million BTC. Self-regulation is implemented through negative feedback between changes of control parameters (the target interval for the hash function values and the Bitcoin Difficulty level) and the speed of mining process. Control parameters depend on the real speed deviations from the target value. This mechanism provides a stable mining speed and determines annual rate of emission. The author suggests a spline-function for describing the annual rate of the cryptocurrency emission in accordance with the Proof-of-Work protocol in the Bitcoin blockchain algorithm. This spline-function gives possibility to find a monetary rule for annual rate of emission. The author in the paper proposes to call this monetary rule by the name of the Bitcoin system inventor - Nakamoto Monetary Rule. The Nakamoto Monetary Rule could be seen as the first example of a programmable monetary rule of the decentralized emission algorithm on the basis of blockchain technology. Central banks could use a similar approach, with the necessary modifications, to develop their programmable monetary rules for Central Bank Digital Currencies (CBDCs) emission based on DLT or blockchain technology
The purpose of this article has been the Bitcoin rates modeling, as the most important digital currency, by depending on 1932 daily observations. As a result , the Bitcoin rates follow the ARIMA(1,1,2) model while the residuals pursue GARCH(1.1) model . In the second semester of 2017, a structural change was noticed, at that moment, the Bitcoin has reached the highest level, and overcame the rate of 16560 Euro. The Bitcoin leap is due to several factors, the most important ones are that it has been accredited as a legal currency by many great world governments , benefits of the tax exemption for its users , has been considered as an entertainment tool , and a short term hedging tool as many researchers have declared .
French title: Modelisation de pieces Bitcoin utilisant le modele autoregressif heteroscedasticite conditionnelle
Cet article vise a modeliser les valeurs de bitcoin comme la monnaie numerique la plus importante a travers les vues quotidiennes de 1932. Il a ete constate que les valeurs de bitcoin suivent le modele ARIMA (1,1,2) tandis que les autres suivent le modele GARCH(1,1), en plus de surveiller les changements structurels dans la serie au deuxieme semestre 2017, au cours de cette periode, le bitcoin a atteint un record, depassant 16590 euros. Le boom du bitcoin est du a plusieurs facteurs, dont le plus important est son acceptation dans de nombreux grands pays comme monnaie legale, l'exoneration fiscale de son detenteur, en plus d'etre consideree comme une methode de luxe, en particulier avec ses avantages, car de nombreux chercheurs ont souligne qu'il s'agissait d'un outil de couverture a court terme.
This paper conducts a review on theoretical and empirical findings on the increasingly popular measure of trade policy uncertainty (TPU) in economics and finance. Moreover, an empirical investigation takes place in order to find the impact that TPU exerts on Bitcoin market values by employing a spectrum of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specifications. Existing studies support that trade policy uncertainty leads to lower-quality and more expensive products and weak participation in international trade. Moreover, it contributes to lower democratic sentiment, hesitant internal migration and lesser socio-economic mobility and higher fluctuations in profitable assets. Moreover, our econometric findings reveal that TPU positively affects Bitcoin prices while crude oil values negatively influence this major cryptocurrency. Thereby, higher trade policy uncertainty is found to increase demand and favorite investments into risky assets in order to ameliorate the risk-return trade-off in investors’ portfolios. This study provides a compass for investing during turmoil due to trade wars and tariffs.
In only a few years, central bank digital currencies (CBDC) have gone from a fringe idea promoted by cryptocurrency bloggers to an idea being seriously explored by 80% of the world’s major central banks, including the People’s Bank of China and the United States Federal Reserve. This paper gives an overview of the drive at the world’s central banks to evaluate CBDCs and examines the reasons behind the world’s two leading economies’ stark divergence in central bank digital currency development. China committed much earlier to launching a CBDC, doing so in early 2016, and has since taken more concrete steps towards piloting and issuing a CBDC than the Fed, which has yet to commit to ever issuing one.
The Editorial Board of Industrial and Corporate Change is pleased to announce its newest venture as part of ICC’s ongoing commitment to highlighting important issues in management, economics, and business. With this Special Issue ICC launches an annual Special Issue devoted to the theme of Macro Economics and Development. As important economists early recognized, the crisis of 2008 fully exposed the inability of macroeconomic theory to account for the possibility of macroeconomic crises as well as deep and long-lasting downturns. This theoretical crisis rests at the very foundations of mainstream economics with its strict commitment to the equilibrium and far-sighted “rationality” of a fictitious representative agent. Unfortunately, a good deal of the intellectual efforts of macroeconomists since the crisis has gone into adding “epicycles” to an already baroque Ptolemaic construction, namely DSGE (Dynamic Stochastic General Equilibrium modeling), by means of a new wave of frictions and rigidities and, in few cases, homeopathic doses of heterogeneity and bounded rationality. In the view of the Editors of this ICC Special Issue such patchwork is far from sufficient. We need a radical departure with radically different foundations. And we need some prestigious venues, where likeminded scholars, especially younger ones, can publish and debate without the tyranny of the “top five” journals, whose conservatism prevents new ideas and approaches from blossoming.1 This overriding purpose drives the launching of this first annual ICC Special Issue.It is proposed by a group of scholars who strongly agree on some propositions (and do not disagree too much on a few others).2 A non-exhaustive list of such propositions would include the following: The economy has to be analyzed as a complex evolving system. Evolution is driven by (partly endogenous) technological, organization, and institutional change. Complexity stems already from the interactions among multiple agents. Heterogeneities are ubiquitous in terms of access to information, capabilities, “models of the world,” and decision processes. More is different. There is no isomorphism between micro behaviors and more aggregate dynamics. The latter is not simply the sum of micro entities but the outcomes of their interactions, most often out-of-equilibrium ones. (Varying degrees of) aggregate order is likely to be an emergent property, stemming from out-of-equilibrium interactions. Dynamics ought to be taken very seriously. Any analysis of the properties of any equilibrium ought to be ideally accompanied by the answers to the question: “how did one get there?”. All this does not rule out the usefulness of simpler, equilibrium models allowing full rationality but taking seriously heterogeneity in information access and capabilities. However, the imperative is that simple models must have even more so properties: if any result applies to simple equilibrium and rationality set-ups, it has to apply even more so to environments where such assumptions cannot apply. In a complex evolving system, it is simply unfeasible to ever achieve an environment of complete markets, which implies that there is no way to ensure that economic plans that involve debt and credit will always be consistent. Thus, it is possible that the system falls into a state of macroeconomic disequilibria associated with intertemporal coordination failures—the failure of a decentralized market economy to deliver a solution that satisfies all equilibrium conditions in a dynamic environment, a theme that macroeconomic theories that intend to shed light on the phenomenon of macroeconomic and debt crises must tackle without assuming the problem away by construction—as in DSGE models. The nature of learning, capabilities accumulation, and innovation is central to the analysis of growth and development. Together, so are the properties of industrial dynamics, structural change, and inter-sectoral interactions. In that setting, supply and demand dynamics interact both in the short- and long-term. Non-linearities are widespread. Learning as such is intrinsically associated with dynamic increasing returns. Those in turn normally involve multiple equilibria and trajectories which are inevitably path dependent. History counts. Such system dynamics are likely to display self-organized criticalities, hysteresis, tipping points, and irreversibilities. The search of proximate laws of motions ought to be encouraged—on, for example, multipliers, accelerators, “learning curves,” as in the cases of Lotka-Volterra processes and Kaldor-Okun laws. Uncertainty in complex evolving environments is endogenous and radical in a Knightian sense: there is no hope of reducing it to probabilizable risk. In such a framework, bounded rationality should not be considered an imperfection, a departure from the fully rational equilibrium case, but rather as the way that agentsbehave, adapt, and learn in a complex environment. In such a framework, often less ismore: in the presence of radical uncertainty, which is quite ubiquitous, one needs to relyon heuristics or other simple rules. The dynamics of socioeconomic systems is nested in a rich thread of institutions and is influenced by a large ensemble of policies which also shape the patterns of interactions and the behaviors of the agents. Inequality is an endemic property of the system and it affects its short- and long-term performance. Its drivers and the policies to curb it ought to be under the spotlight. Finance is not just a “veil” but interacts and influences real dynamics and income distributions. Markets do not work in a vacuum. The functioning of a market economy depends on the laws, rules, norms, and institutional structures under which it operates, all determined by power relations; and the outcomes it produces affect the distribution of power itself. The co-evolution between climate warming and economy should be carefully studied from a complexity perspective, considering the implications for the very survival of humankind and the possible pathways to achieve sustainable growth. Macroeconomics is not an island: we ought to learn from other disciplines, and interdisciplinarity should be encouraged. All these points carry strong policy implications, and our Marco Economics and Development annual Special Issue will encourage their exploration. In support of its aims, the ICC Special Issue will target, among others, the following topics for papers: All macro models focusing on the consequences of heterogenous information and coordination failures. ABM (macro but also at lower levels of aggregation). Models of endogenous macroeconomic and financial instability. Models of macroeconomic and financial crises emergence and resolution. Evolutionary models of growth and development. Networks (with general macro implications). Empirical and theoretical analyses of labor markets and labor relations. Income distribution and inequalities. Trade and development. Post-Keynesian macro analyses. Any empirical papers (such as those by, e.g., G. Katona and G. Gigerenzer) and experimental papers that provide evidence about the behavior of agents, markets, and institutions. Any works that improve agent-based validation, estimation, sensitivity analysis. Applied macroeconometrics addressing complexity, such as, e.g., nonlinearity, heterogeneity. Empirical and theoretical works studying the economic impact of climate change and coevolution between climate and economic dynamics. Political economy written large with special attention to socioeconomic phenomena. Interdisciplinary papers with a macro flavor. Economic history papers related to crises, growth, development, but also to the working of institutions and markets. Analyses of policies, including the reflections of actual policy makers. General topics, including: macroeconomics, debt, development, institutions, climate change, political economy, socioeconomic, and political history. By launching this first of annual special issues on Macro Economics and Development, the ICC Editors are confident that they will be offering original and pathbreaking contributions at this pivotal time of reassessing and restructuring theory and policy into the foreseeable future. e-mail: jes322@columbia.edu
This study aims to determine which auxiliary asset – S&P500, SHCOMP, the U.S. 10Y bond, gold, Brent or corn, in combination with Bitcoin has the best downside risk-minimizing performances. Six portfolios are constructed via an optimal DCC-GARCH model, while for downside risk measures, we use parametric and semiparametric Value-at-Risk and Conditional Value-at-Risk. All selected auxiliary assets have very low dynamic correlation with Bitcoin, which classifies them as good diversifiers. According to parametric results, S&P500 has the best downside risk-minimizing output, while SHCOMP and gold take second and third place. However, when higher moments of portfolios are taken into account, the results change significantly. Due to very high kurtosis and negative skewness, portfolio with S&P500 has among the worst semiparametric downside risk results. On the other hand, SHCOMP index and gold have relatively favourable third and fourth moments’ characteristics, which pushes them to the first and second place of the best auxiliary assets when modified downside risk measures are at stake. We also calculate Sharpe ratio, which suggests that portfolio with gold has by far the best return/risk characteristics.