Papers by Lasko Basnarkov
RePEc: Research Papers in Economics, 2019
Lead-lag relationships among assets represent a useful tool for analyzing high frequency financia... more Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market indexes, whereas foreign exchange data have been less explored. To provide a valuable insight on the nature of the lead-lag relationships in foreign exchange markets here we perform a detailed study for the one-minute log returns on exchange rates through three different approaches: i) lagged correlations, ii) lagged partial correlations and iii) Granger causality. In all studies, we find that even though for most pairs of exchange rates lagged effects are absent, there are many pairs which pass statistical significance tests. Out of the statistically significant relationships, we construct directed networks and investigate the influence of individual exchange rates through the PageRank algorithm. The algorithm, in general, ranks stock market indexes quoted in their respective currencies, as most influential. In contrast to the claims of the efficient market hypothesis, these findings suggest that all market information does not spread instantaneously.
Physica D: Nonlinear Phenomena, Jul 1, 2019
The value of an asset in a financial market is given in terms of another asset known as numeraire... more The value of an asset in a financial market is given in terms of another asset known as numeraire. The dynamics of the value is non-stationary and hence, to quantify the relationships between different assets, one requires convenient measures such as the means and covariances of the respective log returns. Here, we develop transformation equations for these means and covariances when one changes the numeraire. The results are verified by a thorough empirical analysis capturing the dynamics of numerous assets in a foreign exchange market. We show that the partial correlations between pairs of assets are invariant under the change of the numeraire. This observable quantifies the relationship between two assets, while the influence of the rest is removed. As such the partial correlations uncover intriguing observations which may not be easily noticed in the ordinary correlation analysis.
arXiv (Cornell University), Mar 4, 2015
The Football World Cup as world's favorite sporting event is a source of both entertainment and o... more The Football World Cup as world's favorite sporting event is a source of both entertainment and overwhelming amount of data about the games played. In this paper we analyse the available data on football world championships since 1930 until today. Our goal is to rank the national teams based on all matches during the championships. For this purpose, we apply the PageRank with restarts algorithm to a graph built from the games played during the tournaments. Several statistics such as matches won and goals scored are combined in different metrics that assign weights to the links in the graph. Finally, our results indicate that the Random walk approach with the use of right metrics can indeed produce relevant rankings comparable to the FIFA official all-time ranking board.
arXiv (Cornell University), Nov 7, 2021
In the light of several major epidemic events that emerged in the past two decades, and emphasize... more In the light of several major epidemic events that emerged in the past two decades, and emphasized by the COVID-19 pandemics, the non-Markovian spreading models occurring on complex networks gained significant attention from the scientific community. Following this interest, in this article, we explore the relations that exist between the non-Markovian SEIS (Susceptible-Exposed-Infectious-Susceptible) and the classical Markov SIS, as basic re-occurring virus spreading models in complex networks. We investigate the similarities and seek for equivalences both for the discretetime and the continuous-time forms. First, we formally introduce the continuous-time non-Markovian SEIS model, and derive the epidemic threshold in a strict mathematical procedure. Then we present the main result of the paper that, providing certain relations between process parameters hold, the stationary-state solutions of the status probabilities in the non-Markovian SEIS may be found from the stationary state probabilities of the Markov SIS model. This result has a twofold significance. First, it simplifies the computational complexity of the non-Markovian model in practical applications, where only the stationary distribution of the state probabilities is required. Next, it defines the epidemic threshold of the non-Markovian SEIS model, without the necessity of a thrall mathematical analysis. We present this result both in analytical form, and confirm the result trough numerical simulations. Furthermore, as of secondary importance, in an analytical procedure we show that each Markov SIS may be represented as non-Markovian SEIS model.
Evolution of cooperation in networked heterogeneous fluctuating environments
Physica D: Nonlinear Phenomena, Jun 1, 2021
In contrast to game-theoretic interaction models with additive payoffs, the temporal evolution of... more In contrast to game-theoretic interaction models with additive payoffs, the temporal evolution of natural and artificial systems in often governed by a multiplicative process. In evolutionary biology, these systems are studied within the general framework of fluctuating environments, where the individual entities are subject to spatio-temporal fluctuations in the environmental conditions. In these environments, evolutionary behavior may emerge that essentially differs from the one observed in standard models. The study of the evolution of cooperation in fluctuating environments typically assumes a homogeneous, well mixed population, whose constituents choose between two behavioral strategies. In this paper, we generalize these results by developing a systematic study of the cooperation dynamics in fluctuating environments under the consideration of structured, heterogeneous populations with individual entities subjected to general behavioral rules. We find that, in the presence of environmental fluctuations, the cooperation dynamics yields the creation of network components with distinct evolutionary properties. We utilize this result to examine the applicability of a generalized reciprocity behavioral rule in a variety of settings. We thereby show that the introduced rule leads to steady state cooperative behavior that is always greater than or equal to the one predicted by the evolutionary stability analysis of unconditional cooperation. As a consequence, the implementation of our results may go beyond explaining the evolution of cooperation. In particular, they can be directly applied in domains that deal with the development of artificial systems able to adequately mimic reality, such as reinforcement learning.
Physica D: Nonlinear Phenomena, Feb 1, 2020
Lead-lag relationships among assets represent a useful tool for analyzing high frequency financia... more Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market indexes, whereas foreign exchange data have been less explored. To provide a valuable insight on the nature of the lead-lag relationships in foreign exchange markets here we perform a detailed study for the one-minute log returns on exchange rates through three different approaches: i) lagged correlations, ii) lagged partial correlations and iii) Granger causality. In all studies, we find that even though for most pairs of exchange rates lagged effects are absent, there are many pairs which pass statistical significance tests. Out of the statistically significant relationships, we construct directed networks and investigate the influence of individual exchange rates through the PageRank algorithm. The algorithm, in general, ranks stock market indexes quoted in their respective currencies, as most influential. In contrast to the claims of the efficient market hypothesis, these findings suggest that all market information does not spread instantaneously.
SEAIR Epidemic spreading model of COVID-19
Chaos Solitons & Fractals, 2021
We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model o... more We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model of COVID-19. It captures two important characteristics of the infectiousness of COVID-19: delayed start and its appearance before onset of symptoms, or even with total absence of them. The model is theoretically analyzed in continuous-time compartmental version and discrete-time version on random regular graphs and complex networks. We show analytically that there are relationships between the epidemic thresholds and the equations for the susceptible populations at the endemic equilibrium in all three versions, which hold when the epidemic is weak. We provide theoretical arguments that eigenvector centrality of a node approximately determines its risk to become infected.
arXiv (Cornell University), Jul 4, 2018
A growing body of literature suggests that heavy tailed distributions represent an adequate model... more A growing body of literature suggests that heavy tailed distributions represent an adequate model for the observations of log returns of stocks. Motivated by these findings, here we develop a discrete time framework for pricing of European options. Probability density functions of log returns for different periods are conveniently taken to be convolutions of the Student's t-distribution with three degrees of freedom. The supports of these distributions are truncated in order to obtain finite values for the options. Within this framework, options with different strikes and maturities for one stock rely on a single parameter-the standard deviation of the Student's t-distribution for unit period. We provide a study which shows that the distribution support width has weak influence on the option prices for certain range of values of the width. It is furthermore shown that such family of truncated distributions approximately satisfies the no-arbitrage principle and the put-call parity. The relevance of the pricing procedure is empirically verified by obtaining remarkably good match of the numerically computed values by our scheme to real market data.
Progress of theoretical physics. Supplement, 2003
Numerical study of the motion of a classical particle in a homogeneous gravitational field bounci... more Numerical study of the motion of a classical particle in a homogeneous gravitational field bouncing off elastically from a piecewise linear periodic boundary shows that it is characterized by diffusion constant, but nonlinear scaling of the mean-square displacement is also observed. It is shown that periodic by modulus trajectories cannot have a segment with vertical flight.
Persistent Random Search on Complex Networks
Communications in computer and information science, 2017
Searching of target based on random movements in space is an interesting topic of research releva... more Searching of target based on random movements in space is an interesting topic of research relevant in different fields. For searching in complex networks besides the classical random walk, various biasing procedures have been applied for reducing the searching time. We propose one such biasing algorithm that favors movements towards more distant nodes, while penalizing going backward. Using Monte Carlo numerical simulations we demonstrate that the proposed algorithm provides lower Mean First Passage Time for several types of generic and real complex networks.
Physical review, May 14, 2018
We introduce a framework for studying social dilemmas in networked societies where individuals fo... more We introduce a framework for studying social dilemmas in networked societies where individuals follow a simple state-based behavioral mechanism based on generalized reciprocity, which is rooted in the principle "help anyone if helped by someone". Within this general framework, which applies to a wide range of social dilemmas including, among others, public goods, donation and snowdrift games, we study the cooperation dynamics on a variety of complex network examples. By interpreting the studied model through the lenses of nonlinear dynamical systems, we show that cooperation through generalized reciprocity always emerges as the unique attractor in which the overall level of cooperation is maximized, while simultaneously exploitation of the participating individuals is prevented. The analysis elucidates the role of the network structure, here captured by a local centrality measure which uniquely quantifies the propensity of the network structure to cooperation by dictating the degree of cooperation displayed both at microscopic and macroscopic level. We demonstrate the applicability of the analysis on a practical example by considering an interaction structure that couples a donation process with a public goods game.
Scientific Reports, Mar 17, 2017
Ensemble generation is a natural and convenient way of achieving better generalization performanc... more Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemblebased learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model's constituent learners at various levels. This novel stabilityguided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination. Machine learning has been transforming the world by improving our understanding of artificial intelligence 1-3 and by providing solutions for some outstanding problems such as multi-modal parcellation of human cerebral cortex 4 and materials discovery 5. A learning algorithm generalizes if, given access to some training set, it returns a hypothesis whose empirical error is close to its true error 6. There are three main approaches to institute generalization guarantees: (1) by providing bounds of various notions of functional space capacity-most notably, using the VC-dimension 7 ; (2) by establishing connections between the stability of a learning algorithm and its ability to generalize 8-10 , and (3) by considering the compression-scheme method 11. Here we describe an effective way to fuse boosting and bagging ensembles in which algorithmic stability directs a novel process of collaboration between the resulting ensemble's weak/strong components that outperforms best-case boosting/bagging for a broad range of applications and under a variety of scenarios. The algorithms were assessed on various realistic datasets, showing improved performance in all cases, on average of slightly below 40%, compared to the best-case boosting/bagging counterparts. Furthermore, in a medical setting for protein detection in texture analysis of gel electrophoresis images 12 , our approach exhibits surpassing performance of approximately 0.9773 area under the ROC curve (AUROC), compared to three machine-learning feature selection approaches: Multiple Kernel Learning, Recursive Feature Elimination with different classifiers and a Genetic Algorithm-based approach with Support Vector Machines (SVMs) as decision functions, having 0.9574 or less AUROCs. Moreover, when collaboration is effectuated with weak components, our algorithm runs up to more than five times faster than the underlying boosting algorithm. We anticipate our approach to be a starting point for more sophisticated models for generating stability-guided collaborative learning approaches, not necessarily limited to boosting. Ensemble techniques 13-15 show improved accuracy of predictive analytics and data mining applications. In a typical ensemble method, the base inducers and diversity generators are responsible for generating diverse classifiers which represent the generalized relationship between the input and the target attributes. A strong classifier
Critical exponents of the transition from incoherence to partial oscillation death in the Winfree model
Journal of Statistical Mechanics: Theory and Experiment, Oct 21, 2009
ABSTRACT We consider an analytically solvable version of the Winfree model of synchronization of ... more ABSTRACT We consider an analytically solvable version of the Winfree model of synchronization of phase oscillators (proposed by Ariaratnam and Strogatz 2001 Phys. Rev. Lett. 86 4278). It is obtained that the transition from incoherence to a partial death state is characterized by third-order or higher phase transitions according to the Ehrenfest classification. The order of the transition depends on the shape of the distribution function for natural frequencies of oscillators in the vicinity of their lowest frequency. The corresponding critical exponents are found analytically and verified with numerical simulations of equations of motion. We also consider the generalized Winfree model with the interaction strength proportional to a power of the Kuramoto order parameter and find the domain where the critical exponent remains unchanged by this modification.
arXiv (Cornell University), May 24, 2020
We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model o... more We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model of COVID-19. It captures two important characteristics of the infectiousness of COVID-19: delayed start and its appearance before onset of symptoms, or even with total absence of them. The model is theoretically analyzed in continuous-time compartmental version and discrete-time version on random regular graphs and complex networks. We show analytically that there are relationships between the epidemic thresholds and the equations for the susceptible populations at the endemic equilibrium in all three versions, which hold when the epidemic is weak. We provide theoretical arguments that eigenvector centrality of a node approximately determines its risk to become infected.
arXiv (Cornell University), Jul 18, 2019
We study random walk on complex networks with transition probabilities which depend on the curren... more We study random walk on complex networks with transition probabilities which depend on the current and previously visited nodes. By using an absorbing Markov chain we derive an exact expression for the mean first passage time between pairs of nodes, for a random walk with a memory of one step. We have analyzed one particular model of random walk, where the transition probabilities depend on the number of paths to the second neighbors. The numerical experiments on paradigmatic complex networks verify the validity of the theoretical expressions, and also indicate that the flattening of the stationary occupation probability accompanies a nearly optimal random search.
arXiv (Cornell University), May 16, 2014
We develop a game-theoretic framework to investigate the effect of cooperation on the energy effi... more We develop a game-theoretic framework to investigate the effect of cooperation on the energy efficiency in wireless networks. We address two examples of network architectures, resembling ad-hoc network and network with central infrastructure node. Most present approaches address the issue of energy efficiency in communication networks by using complex algorithms to enforce cooperation in the network, followed by extensive signal processing at the network nodes. Instead, we address cooperative communication scenarios which are governed by simple, evolutionary-like, local rules, and do not require strategic complexity of the network nodes. The approach is motivated by recent results in evolutionary biology which suggest that cooperation can emerge in Nature by evolution, i. e. can be favoured by natural selection, if certain mechanism is at work. As result, we are able to show by experiments that cooperative behavior can indeed emerge and persist in wireless networks, even if the behavior of the individual nodes is driven by selfish decision making. The results from this work indicate that uncomplicated local rules, followed by simple fitness evaluation, can promote cooperation and generate network behavior which yields global energy efficiency in certain wireless networks.
arXiv (Cornell University), Jun 22, 2015
Plug-in electrical vehicles (PEV) are capable of both grid-to-vehicle (G2V) and vehicle-to-grid (... more Plug-in electrical vehicles (PEV) are capable of both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) power transfer. The advantages of developing V2G include an additional revenue stream for cleaner vehicles, increased stability and reliability of the electric grid, lower electric system costs, and eventually, inexpensive storage and backup for renewable electricity. Here we show how smart control of PEVs can improve the stability of power grids using only local frequency measurements. We evaluate the proposed control strategy on the IEEE Case 3 and the IEEE New England power systems. The results show that V2G leads to improved steady-state stability, larger region of stability, reduced frequency and voltage fluctuations during transients and longer critical clearing times.
arXiv (Cornell University), Dec 19, 2019
Fluctuating environments are situations where the spatio-temporal stochasticity plays a significa... more Fluctuating environments are situations where the spatio-temporal stochasticity plays a significant role in the evolutionary dynamics. The study of the evolution of cooperation in these environments typically assumes a homogeneous, well mixed population, whose constituents are endowed with identical capabilities. In this paper, we generalize these results by developing a systematic study for the cooperation dynamics in fluctuating environments under the consideration of structured, heterogeneous populations with individual entities subjected to general behavioral rules. Considering complex network topologies, and a behavioral rule based on generalized reciprocity, we perform a detailed analysis of the effect of the underlying interaction structure on the evolutionary stability of cooperation. We find that, in the presence of environmental fluctuations, the cooperation dynamics can lead to the creation of multiple network components, each with distinct evolutionary properties. This is paralleled to the freezing state in the Random Energy Model. We utilize this result to examine the applicability of our generalized reciprocity behavioral rule in a variety of settings. We thereby show that the introduced rule leads to steady state cooperative behavior that is always greater than or equal to the one predicted by the evolutionary stability analysis of unconditional cooperation. As a consequence, the implementation of our results may go beyond explaining the evolution of cooperation. In particular, they can be directly applied in domains that deal with the development of artificial systems able to adequately mimic reality, such as reinforcement learning.
Energy efficiency is gaining importance in wireless communication networks which have nodes with ... more Energy efficiency is gaining importance in wireless communication networks which have nodes with limited energy supply and signal processing capabilities. We present a numerical study of cooperative communication scenarios based on simple local rules. This is in contrast to most of the approaches in the literature which enforce cooperation by using complex algorithms and require strategic complexity of the network nodes. The approach is motivated by recent results in evolutionary biology which suggest that, if certain mechanism is at work, cooperation can be favored by natural selection, i. e. even selfish actions of the individual nodes can lead to emergence of cooperative behavior in the network. The results of the simulations in the context of wireless communication networks verify these observations and indicate that uncomplicated local rules, followed by simple fitness evaluation, can generate network behavior which yields global energy efficiency.
arXiv (Cornell University), Jul 15, 2021
We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the CO... more We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the COVID-19, by considering discrete-and continuous-time versions. The incubation period, delayed infectiousness and the distribution of the recovery period are modeled with general functions. By taking corresponding choice of these functions, it is shown that the model reduces to the classical Markovian case. The epidemic threshold is analytically determined for arbitrary functions of infectivity and recovery and verified numerically. The relevance of the model is shown by modeling the first wave of the epidemic in Italy, in the spring, 2020.
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Papers by Lasko Basnarkov