Evolutionary correlation, regime switching, spectral dynamics and optimal trading strategies for cryptocurrencies and equities
This paper uses new and recently established methodologies to study the evolutionary dynamics of the cryptocurrency market, and compares the findings with that of the equity market. We begin by applying random matrix theory and principal components analysis (PCA) to correlation matrices of both collections, highlighting clear differences in the eigenspectra exhibited. We then explore the heterogeneity of both asset classes, studying the time-varying dynamics of underlying sector behaviours, and determine the collective similarity within each collection. We then turn to a study of structural break dynamics and evolutionary power spectra, where we quantify the collective affinity in structural breaks and evolutionary behaviours of underlying sector time series. Finally, we implement two algorithms simulating `portfolio choice' dynamics to compare the effectiveness of stock selection and sector allocation in cryptocurrency portfolios. There, we highlight the importance of both endeavours and comment on noteworthy implications for cryptocurrency portfolio management.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Regime-Arrival Uncertainty in Generalization Bounds under Distribution Shift
The standard generalization bounds assume that the training and deployment distributions are the same, or are static, and don't consider regime switching environments where the ratio of calm vs crisis states is different…
Modeling the Complex Dynamics and Changing Correlations of Epileptic Events
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied q…
EEGElectroencephalogram (EEG)Regime-Switching Langevin Monte Carlo Algorithms
Langevin Monte Carlo (LMC) algorithms are popular Markov Chain Monte Carlo (MCMC) methods to sample a target probability distribution, which arises in many applications in machine learning. Inspired by regime-switching s…
Eco-Evolutionary Dynamics of a Population with Randomly Switching Carrying Capacity
Environmental variability greatly influences the eco-evolutionary dynamics of a population, i.e. it affects how its size and composition evolve. Here, we study a well-mixed population of finite and fluctuating size whose…
Improving S&P 500 Volatility Forecasting through Regime-Switching Methods
Accurate prediction of financial market volatility is critical for risk management, derivatives pricing, and investment strategy. In this study, we propose a multitude of regime-switching methods to improve the predictio…