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Abstract: The study applies the grey model (GM(1,1)) to the Verhulst differential equation for forecasting the Bitcoin transaction counts. The grey Verhulst model (GVM) is based on the data set of Bitcoin as recorded along 10 years from the 1st August 2010. The model accuracy is checked by the mean absolute percentage error (MAPE), while the model predictability is assessed by analysing a plot of the Verhulst model constructed upon the parameters provided by the GVM. The MAPE criterion suggests the reasonable accuracy of the overall GVM forecasting values and high accuracy by considering the last 400 forecasting values. Furthermore, the Verhulst model plot suggests that the GVM is potential on predictability as the plot is not chaotic. The GVM forecasting values suggest a slight future decline in transacting Bitcoin; this may be due to its competition with the other emerging cryptocurrencies. The GVM suggests a relatively high performance as compared to the usual one-variable forecasting model GM(1,1). PubDate: 2022-05-04
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Abstract: Abstract In this paper, we study a class of markets, among which we can mention agricultural and energy markets, characterized by seasonality, i.e., in which demand and/or supply conditions cyclically alternate with a precise and known periodicity. We propose a new theoretical framework based on a cobweb model with adaptive expectations, accordingly modified to be consistent with market’s seasonality. The model, consisting in a second-order non-autonomous difference equation, is investigated with the aim of understanding how the periodical nature of the market together with the agents’ expectation formation mechanism affects the resulting dynamics. We analytically prove the emergence of dynamical scenarios that are missing in the classic cobweb model for non-seasonal markets, such as quasi-periodic dynamics and an ambiguous role on stability of the expectation weight. Finally, we discuss their economic rationale with the help of numerical simulations. In such a peculiar economic framework, agents’ learning plays a key role to explain the dynamical properties of economic observables. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00335-w
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Abstract: Abstract Two types of boundedly rational monopolists are considered, when they are unable to determine the profit maximizing output levels. In the first case, the monopolist knows the price function and in the second case it can access only past output and price values. In applying gradient dynamics, the marginal profit is either known or approximated by finite differences based on two past profit data. Stability conditions are derived first with discrete time scales, which are also applied in a special case. Two models of continuous-time dynamics are then introduced. The first is a natural modification of the discrete model, and the other includes an inertia coefficient with the derivative. In each case, a delay differential equation is obtained with two delays. Stability conditions are derived and the stability-switching curves are constructed and illustrated. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00342-x
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Abstract: Abstract In this paper, I study the conditions under which a CSR leader, that is a firm which commits to invest in socially responsible activities prior to its competitor, can develop a first-mover advantage. A price-setting duopoly market with horizontally differentiated products is considered, where firms can increase the willingness to pay of the consumers of their products by investing in socially responsible activities. It is shown that if the investment in CSR is perfectly specific to the CSR leader and does not spill over to the CSR follower, the CSR leader achieves higher profits. Hence, a first-mover advantage arises. If however, CSR investment spills over to and hence benefits also the CSR follower by increasing the follower sales, then a second-mover advantage might arise for the follower. A characterization is provided for the influence of the intensity of competition and the level of spillovers on the relative and absolute level of CSR activities and the firms’ incentives to engage in CSR. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00328-9
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Abstract: Abstract In this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00319-w
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Abstract: Abstract We reconsider the multiplier–accelerator model of business cycles, first introduced by Samuelson and then modified by many authors. The original simple model, besides damped oscillations, also leads to divergent oscillations. To avoid this, we introduce two different types of governmental expenditures leading a two-dimensional continuous piecewise linear map that can generate sustained oscillations (attracting cycles). The map is defined by three different linear functions in three different partitions of the phase plane, and this peculiarity influences the overall dynamics of the system. We show that, similar to the classical Samuelson model, there is a unique feasible equilibrium as well as converging oscillations. However, close to the center bifurcation value the attracting equilibrium coexists with attracting cycles of different periods, which lose stability via a center bifurcation simultaneously with the equilibrium. Moreover, we show that attracting cycles of particular type also exist when the equilibrium becomes an unstable focus. For several families of attracting cycles, by introducing the symbolic representation, we obtain boundaries of the related periodicity regions, associated with border collision bifurcations. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00333-y
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Abstract: Abstract Based on the seminal asset-pricing model by Brock and Hommes (J Econ Dyn Control 22:1235–1274, 1998), we analytically show that higher wealth taxes increase the risky asset’s fundamental value, enlarge its local stability domain, may prevent the birth of nonfundamental steady states and, if they exist, reduce the risky asset’s mispricing. We furthermore find that higher wealth taxes may hinder the emergence of endogenous asset price oscillations and, if they exist, dampen their amplitudes. Since oscillatory price dynamics may be associated with lower mispricing than locally stable nonfundamental steady states, policymakers may not always want to suppress them by imposing (too low) wealth taxes. Overall, however, our study suggests that wealth taxes tend to stabilize the dynamics of financial markets. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00340-z
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Abstract: Abstract At the end of the last century, chaos theory principles have helped organizational theorists to analyze several aspects of organizations and to account for their dynamic evolution. However, most of contributions remained relegated as qualitative discussions of organizational phenomena. In this paper, starting from effort allocations of subordinates in supervised work groups which were observed in a human participants interaction, we found evidence of nonlinear relation between the colleagues’ effort. In order to explain the large variability of behavior we observed, we consider those activated by unfavorable social comparison and propose a dynamical model. A theoretical dynamic model based on the empirical results appears to be powerful for modeling repeated interactions in a work group. Research and intervention design should focus on individual intolerance and beliefs about the reciprocal capacities between subordinates, which, according to our study, appear to play a key role in the inefficiency of equilibria observed in supervised work groups. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00331-0
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Abstract: Abstract In this paper, we come up with an original trading strategy on Bitcoins. The methodology we propose is profit-oriented, and it is based on buying or selling the so-called Contracts for Difference, so that the investor’s gain, assessed at a given future time t, is obtained as the difference between the predicted Bitcoin price and an apt threshold. Starting from some empirical findings, and passing through the specification of a suitable theoretical model for the Bitcoin price process, we are able to provide possible investment scenarios, thanks to the use of a Recurrent Neural Network with a Long Short-Term Memory for predicting purposes. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00324-z
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Abstract: Abstract Whereas much research has largely investigated the safe haven, diversifier and hedge proprieties of cryptocurrency, very few papers have analyzed the hedging issue of cryptocurrency with other assets. As such, this paper attempts to investigate the possibility if Bitcoin can be hedged by selected fiat currencies (EUR, JPY and GBP) as Bitcoin prices have experienced high and persistent volatility. To do so, we compute optimal hedge ratios between Bitcoin and fiat currencies over the period 02/02/2012–30/11/2017 based on the VAR-DCC-GARCH model, VAR-ADCC-GARCH model and VAR-component GARCH-DCC model. A rolling window analysis is employed to establish out-of-sample one-step-ahead forecasts of dynamic conditional correlations between different assets. This leads to establish time-varying hedge ratios and thus dynamic cross-hedging Bitcoin/fiat currency markets. The empirical results clearly show the time-varying correlations between Bitcoin and fiat currencies under different specifications, implying a dynamic behavior of the relationship between such assets. For all the proposed models, such dynamic correlations are rather characterized by trending downward over the period under study. The results also display time-varying hedge ratios which lead to an ongoing regular demand for rebalancing the hedged positions under different specifications. As a matter of fact, using various models which take into account different aspects of volatility and correlation structures allows to better implement dynamic hedging strategies. PubDate: 2021-12-01 DOI: 10.1007/s10203-020-00314-7
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Abstract: Abstract Since Bitcoin’s introduction in 2009, interest in cryptocurrencies has soared. One manifestation of this interest has been the explosion of newly created coins and tokens. In this paper, we analyze the dynamics of this burgeoning industry. We consider both cryptocurrency coins and tokens. The paper examines the dynamics of coin and token creation, competition and destruction in the cryptocurrency industry. In order to conduct the analysis, we develop a methodology to identify peaks in prices and trade volume, as well as when coins and tokens are abandoned and subsequently “resurrected”. We also study trading activity. Our data spans more than 4 years: there are 1082 coins and 725 tokens in the data. While there are some similarities between coins and tokens regarding dynamics, there are some striking differences as well. Overall, we find that 44% of publicly-traded coins are abandoned, at least temporarily. 71% of abandoned coins are later resurrected, leaving 18% of coins to fail permanently. Tokens experience abandonment less frequently, with only 7% abandonment and 5% permanent token abandonment at the end of the data. Using linear regressions, we find that market variables such as the bitcoin price are not associated with the rate of introducing new coins, though they are positively associated with issuing new tokens. We find that for both coins and tokens, market variables are positively associated with resurrection. We then examine the effect that the bursting of the Bitcoin bubble in December 2017 had on the dynamics in the industry. Unlike the end of the 2013 bubble, some alternative cryptocurrencies continue to flourish after the bursting of this bubble. PubDate: 2021-12-01 DOI: 10.1007/s10203-021-00329-8
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Abstract: Abstract In recent times of noticeable climate change the consideration of external factors, such as weather and economic key figures, becomes even more crucial for a proper valuation of derivatives written on agricultural commodities. The occurrence of remarkable price changes as a result of severe changes in these factors motivates the introduction of different price states, each describing different dynamics of the price process. In order to include external factors we propose a two-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second, predictions of future price states are then obtained from short-term predictions of the external factors by means of either K-nearest neighbors or random forest classification. We apply our model to real corn futures data and generate price scenarios via a Monte Carlo simulation, which we compare to Sørensen (J Futures Mark 22(5):393–426, 2002). Thereby we obtain a better approximation of the real futures prices by the simulated futures prices regarding the error measures MAE, RMSE and MAPE. From a practical point of view, these simulations can be used to support the assessment of price risks in risk management systems or as decision support regarding trading strategies under different price states. PubDate: 2021-11-18 DOI: 10.1007/s10203-021-00354-7
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Abstract: Abstract The motivation of proposing and editing the Special Issue “Blockchain and cryptocurrencies” came from the inspirational invited and contributed talks at the 43rd annual A.M.A.S.E.S. conference held in Perugia in September 2019. All the papers have gone through the journal regular refereeing process under the same standards set by the journal, and nine contributions were finally accepted for publication. PubDate: 2021-11-13 DOI: 10.1007/s10203-021-00366-3
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Abstract: Abstract Sustainable and responsible finance incorporates Environmental, Social, and Governance (ESG) principles into business decisions and investment strategies. In recent years, investors have rushed to Sustainable and Responsible Investments in response to growing concerns about the risks of climate change. Asset managers look for some assessment of sustainability for guidance and benchmarking, for instance, $30 trillion of assets are invested using some ESG ratings. Several studies argue that good ESG ratings helped to prop up stock returns during the 2008 Global Financial Crisis (Lins et al. J Finance 72(4):1785–1824, 2017). The ESG score represents a benchmark of disclosures on public and private firms, it is based on different characteristics which are not directly related to the financial performance (Harvard Law School Forum on Corporate Governance, ESG reports and ratings:what they are, why they matter. https://corpgov.law.harvard.edu/2017/07/27/esg-reports-and-ratings-what-they-are-why-they-matter/, 2017). The role of ESG ratings and their reliability have been widely discussed (Berg et al. Aggregate confusion: the divergence of ESG ratings, MIT Sloan Research Paper No. 5822-19, 2019). Sustainable investment professionals are unsatisfied with publicly traded companies’ climate-related disclosure. This negative sentiment is particularly strong in the USA, and within asset managers who do not believe that markets are consistently and correctly pricing climate risks into company and sector valuations. We believe that ESG ratings, when available, still affect business and finance strategies and may represent a crucial element in the company’s fundraising process and on shares returns. We aim to assess how structural data as balance sheet items and income statements items for traded companies affect ESG scores. Using the Bloomberg ESG scores, we investigate the role of structural variables adopting a machine learning approach, in particular, the Random Forest algorithm. We use balance sheet data for a sample of the constituents of the Euro Stoxx 600 index, referred to the last decade, and investigate how these explain the ESG Bloomberg ratings. We find that financial statements items represent a powerful tool to explain the ESG score. PubDate: 2021-11-12 DOI: 10.1007/s10203-021-00364-5
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Abstract: Abstract In this paper we propose and solve a real options model for the optimal adoption of an electric vehicle. A policymaker promotes the abeyance of fossil-fueled vehicles through an incentive, and the representative fossil-fueled vehicle’s owner decides the time at which buying an electric vehicle, while minimizing a certain expected cost. This involves a combination of various types of costs: the stochastic opportunity cost of driving one-unit distance with a traditional fossil-fueled vehicle instead of an electric one, the cost associated to traffic bans, and the net purchase cost. After determining the optimal switching time and the minimal cost function for a general diffusive opportunity cost, we specialize to the case of a mean-reverting process. In such a setting, we provide a model calibration on real data from Italy, and we study the dependency of the optimal switching time with respect to the model’s parameters. Moreover, we study the effect of traffic bans and incentive on the expected optimal switching time. We observe that incentive and traffic bans on fossil-fueled transport can be used as effective tools in the hand of the policymaker to encourage the adoption of electric vehicles and hence to reduce air pollution. PubDate: 2021-11-09 DOI: 10.1007/s10203-021-00359-2
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Based on the concept of self-decomposability, we extend some recent multidimensional Lévy models built using multivariate subordination. Our aim is to construct multivariate Lévy processes that can model the propagation of the systematic risk in dependent markets with some stochastic delay instead of affecting all the markets at the same time. To this end, we extend some known approaches keeping their mathematical tractability, study the properties of the new processes, derive closed-form expressions for their characteristic functions and detail how Monte Carlo schemes can be implemented. We illustrate the applicability of our approach in the context of gas, power and emission markets focusing on the calibration and on the pricing of spread options written on different underlying commodities. PubDate: 2021-10-08 DOI: 10.1007/s10203-021-00352-9
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Abstract: Abstract We propose a new model for the pricing of wind power futures written on the wind power production index. Our approach is based on an arithmetic multi-factor pure-jump Ornstein–Uhlenbeck setup with time-dependent coefficients. We express the wind power production index and the corresponding futures price in terms of Fourier integrals and derive the related time dynamics. We conclude the paper by an investigation of the risk premium associated with our wind power model. PubDate: 2021-10-01 DOI: 10.1007/s10203-021-00345-8
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Abstract: Abstract This paper presents a stylized model of interaction among boundedly rational heterogeneous agents in a multi-asset financial market to examine how agents’ impatience, extrapolation, and switching behaviors can affect cross-section market stability. Besides extrapolation and performance based switching between fundamental and extrapolative trading documented in single asset market, we show that a high degree of ‘impatience’ of agents who are ready to switch to more profitable trading strategy in the short run provides a further cross-section destabilizing mechanism. Though the ‘fundamental’ steady-state values, which reflect the standard present-value of the dividends, represent an unbiased equilibrium market outcome in the long run (to a certain extent), the price deviation from the fundamental price in one asset can spill-over to other assets, resulting in cross-section instability. Based on a (Neimark–Sacker) bifurcation analysis, we provide explicit conditions on how agents’ impatience, extrapolation, and switching can destabilize the market and result in a variety of short and long-run patterns for the cross-section asset price dynamics. PubDate: 2021-08-19 DOI: 10.1007/s10203-021-00348-5
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