A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Develops a flexible model for regime transitions in time series data.
TMTF improves time series visualization by separating dynamic regimes.
The article detects market regimes from covariance matrices using VLSTAR and clustering models.
The paper extends MS models with TVTP to U.S. Treasury yields, finding reliable regime dynamics but challenging TVTP identification.
Study on how initialization scale affects neural network training regimes.
A recent line of work studies overparametrized neural networks in the "kernel regime," i.e. when the network behaves during training as a kernelized linear predictor, and thus training with gradient descent has the effect of finding the minimum RKHS norm solution. This stands in contrast to other studies which demonstr…
We argue that in fully-connected networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. Under some general conditions, we show that this transition is sharp for the hinge loss. In the whole over-parametrized regime, poor minima of the loss are not encounte…
Machine learning detects regime shifts in online game-experiments with high accuracy.
Paper introduces TtT, market-implied transition time, from greenium term structure.
A model of open economics composed of producers and speculators is investigated by numerical simulations. The capital flows from the environment to the producers and from them to the speculators. The price fluctuations are suppressed by the speculators. When the aggressivity of the speculators grows, there is a transit…
In an observed generalized semi-Markov regime, estimation of transition rate of regime switching leads towards calculation of locally risk minimizing option price. Despite the uniform convergence of estimated step function of transition rate, to meet the existence of classical solution of the modified price equation, t…
Unified framework detects change-points and estimates parameters in nonlinear systems with regime switching.
Optimal data-driven formulations are found for learning and decision-making with historical data.
Develops new Markov processes with switching rates and past dependence.
This paper proposes a multi-scale Markov-Switching GARCH model for EUR/USD volatility.
Develops methods to simulate rare transitions in molecular systems.
Entropy tracking reveals class commitment transitions in diffusion models.
The paper explores robustness in linear regression models under adversarial attacks.
Modeling regime shifts in co-evolving time series with interactions and time-dependency.
We establish minimax optimal rates of convergence for estimation in a high dimensional additive model assuming that it is approximately sparse. Our results reveal an interesting phase transition behavior universal to this class of high dimensional problems. In the {\it sparse regime} when the components are sufficientl…
We derive the exact solution of a one-dimensional Markov functional model with log-normally distributed interest rates in discrete time. The model is shown to have two distinct limiting states, corresponding to small and asymptotically large volatilities, respectively. These volatility regimes are separated by a phase …
Recently, it was shown that there is a phase transition in the community detection problem. This transition was first computed using the cavity method, and has been proved rigorously in the case of groups. However, analytic calculations using the cavity method are challenging since they require us to understand p…
Markovian RNN adapts to nonstationary data using HMM for better time series prediction.
New approach to analyze matrix denoising using gradient flow and fixed point equations.
In this paper analytic formulas for electricity derivatives are calculated. To this end, we assume that electricity spot prices follow a 3-regime Markov regime-switching model with independent spikes and drops and periodic transition matrix. Since the classical derivatives pricing methodology cannot be used in case of …
Superstatistics is a widely employed tool of non-equilibrium statistical physics which plays an important role in analysis of hierarchical complex dynamical systems. Yet, its "canonical" formulation in terms of a single nuisance parameter is often too restrictive when applied to complex empirical data. Here we show tha…
We consider an interest rate model with log-normally distributed rates in the terminal measure in discrete time. Such models are used in financial practice as parametric versions of the Markov functional model, or as approximations to the log-normal Libor market model. We show that the model has two distinct regimes, a…
We study a phenomenological model for the continuous double auction, equivalent to two independent queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior …
RFMs transition from linear to nonlinear under specific input-label correlation.
Study shows pre-event L2 liquidity state predicts crypto futures liquidity better than event labels.
RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.
New algorithm achieves data-dependent regret bounds in MDPs with unknown transitions.
Analyzes bias-variance in overparameterized linear models using random features.
This paper considers systems subject to nonholonomic constraints which are not uniform on the whole configuration manifold. When the constraints change, the system undergoes a transition in order to comply with the new imposed conditions. Building on previous work on the Hamiltonian theory of impact, we tackle the prob…
Sharp feature transitions revealed in extensive-width networks.
The study reveals a transition in neural network performance from infinite-width to variance-limited behavior as dataset size increases.
We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 - beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -- -means clustering -- in order to automatical…
We study the phase transition of dynamical herd behaviors for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution of returns satisfies the power-law behavior with three different values of the scaling exponent 3.11 (one time lag = 1 minute), 2.81 (30 minut…
In this paper, we consider a discrete time economy where we assume that the short term interest rate follows a quadratic term structure of a regime switching asset process. The possible non-linear structure and the fact that the interest rate can have different economic or financial trends justify the interest of Regim…
The study provides precise asymptotic theory for in-context learning by Transformers.
Slow feature analysis (SFA) is a method for extracting slowly varying driving forces from quickly varying nonstationary time series. We show here that it is possible for SFA to detect a component which is even slower than the driving force itself (e.g. the envelope of a modulated sine wave). It is shown that it depends…
Neural networks parameterize time-varying Markov dynamics in financial time series.
New findings on -NN algorithm's robustness under random data corruption.
We study the problem of approximate ranking from observations of pairwise interactions. The goal is to estimate the underlying ranks of objects from data through interactions of comparison or collaboration. Under a general framework of approximate ranking models, we characterize the exact optimal statistical error …
Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently-discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology…
Bootstrap method for Markov chains in reinforcement learning.
We study hedging and pricing of unattainable contingent claims in a non-Markovian regime-switching financial model. Our financial market consists of a bank account and a risky asset whose dynamics are driven by a Brownian motion and a multivariate counting process with stochastic intensities. The interest rate, drift, …
New findings support a new community recovery threshold for Stochastic Block Model with many communities.