Analyzes Indian commercial dynamism using time series data.
problem Understanding commercial dynamism in India.
method Time series analysis of various economic indicators.
result Detailed insights into growth rate, trade balance, etc.
On-device research index
arXiv research
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.
169,051 papers · 148 categories
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48 results for “Dynamic Data Rates”
Method calibrates local volatility and stochastic short rate models for equity-rate dynamics.
problem Joint calibration of local volatility and stochastic short rate models.
method Iterative approach using semimartingale optimal transport.
result Demonstrated performance on market data using European SPX options and cap interest rate options.
Transfer learning improves chaotic dynamics predictions with less data.
problem Efficiently predicting chaotic dynamics with limited data.
method Transfer learning for nonlinear dynamics, optimizing transfer rate and leveraging small-scale turbulence universality.
result Significantly more accurate inference of chaotic dynamics achieved.
Model proposes neural network for continuous time dynamics with inductive biases.
problem Training neural networks for small datasets with nonlinear dynamics.
method Inductive biases on decay rates and frequencies using Koopman operator theory.
result Higher forecasting performance with single short training sequence.
Adaptive algorithm improves convergence rate of Langevin dynamics.
problem Improving convergence rate of Langevin dynamics.
method Adaptive non-reversible stochastic gradient Langevin dynamics algorithm.
result Improved convergence rate of the algorithm.
Adaptive learning rate improves model training flexibility.
problem Limited flexibility of hand-designed learning rate schedules.
method Reinforcement learning to automatically learn adaptive learning rates.
result Auto-learned learning rate controller achieves better test results.
Paper tackles dynamic pricing in a geometrically decaying environment, achieving better occupancy with lower rates.
problem Minimizing expected loss in a dynamically changing environment with decisions dependent on the data distribution.
method Introduces algorithms for information and loss function settings, using repeated decision deployment to allow mixing of the environment.
result Iteration complexity matches first and zero order stochastic gradient methods up to logarithmic factors.
A new method predicts student skill success rates in real-time.
problem Accurate and explainable prediction of student skill success rates in real-time.
method Performance Distribution Tracing (PDT) using a Dynamic Bayesian Network with continuous random variables.
result PDT provides both explainability and accuracy in real-time predictions of student skill success rates.
We present a continuous-time maximum likelihood estimation methodology for credit rating transition probabilities, taking into account the presence of censored data. We perform rolling estimates of the transition matrices with exponential time weighting with varying horizons and discuss the underlying dynamics of trans…
Extends BBSM model to incorporate ESG ratings and path dynamics.
problem Price stock options considering historical market index dynamics and ESG ratings.
method Develops discrete, binary tree option pricing model under BBSM with ESG valuation.
result Model accurately fits stock price changes and European call option prices.
The paper offers error bounds for quantized dynamical models.
problem Accuracy of dynamical models from dependent data sequences.
method Developed uniform error bounds for quantized models and imperfect optimization algorithms.
result Unified bounds for slow and fast rates, scaling with model encoding bits.
Study examines USD exchange rate dynamics using Kramers-Moyal expansion.
problem Understanding and predicting exchange rate instability.
method Kramers-Moyal expansion and Fokker-Planck formalism applied to log-return data.
result Identifies a stabilizing linear drift and nonlinear diffusion term in exchange rate fluctuations.
The paper models SOFR and EFFR dynamics, reconciling diffusive and piecewise paths.
problem Updating interest rate models for SOFR, which is becoming a key benchmark.
method Calibrates a model to SOFR and EFFR futures prices, reconciling diffusive and piecewise paths.
result The model reflects key empirical features of SOFR dynamics and reconciles diffusive and piecewise paths.
High-dimensional learning dynamics of multi-pass Stochastic Gradient Descent in multi-index modelsstat.ML
Study SGD dynamics in high-dimensional models, revealing consistent behavior across different batch sizes and learning rates.
problem Understanding SGD dynamics in high-dimensional multi-index models.
method Asymptotic analysis of SGD, developing mean-field equations and Gaussian diffusion approximations.
result Consistent SGD dynamics across different batch sizes and learning rates, distinct from gradient flow and online SGD.
Modeling exchange rates and options using entropic dynamics.
problem Modeling the dynamics of exchange rates and European options.
method Entropic Dynamics, entropic inference, scale invariance, logarithm of exchange rate.
result Derives the Geometric Brownian Motion and the Garman-Kohlhagen model for European options.
New framework analyzes SGD dynamics in large samples and dimensions.
problem Analyzing stochastic gradient descent in large-scale settings.
method Inspired by random matrix theory, new framework for fixed stepsize and finite sum settings.
result SGD dynamics become deterministic in the large sample and dimensional limit, governed by a Volterra integral equation.
DGNN predicts financial margin calls under stress tests.
problem Forecasting margin calls in dynamic financial networks.
method Dynamic Graph Neural Network (DGNN) architecture.
result DGNN produces accurate forecasts up to 21 days.
Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an ex…
Model for dynamic relational data with regime changes.
problem Handling abrupt changes in dynamic relational data.
method Factorized fusion shrinkage model with global-local shrinkage priors.
result Posterior distribution attains minimax optimal rate up to logarithmic factors.
Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings. This paradigm h…
The paper models exchange rate risk premium using mean-reverting dynamics.
problem Empirical failure of uncovered interest parity (UIP).
method Modeling risk premium using Ornstein-Uhlenbeck (OU) process embedded in stochastic differential equation for exchange rate.
result The model shows strong predictive performance at short and long horizons, but underperforms at intermediate horizons.
Entropy rate of sequential data-streams naturally quantifies the complexity of the generative process. Thus entropy rate fluctuations could be used as a tool to recognize dynamical perturbations in signal sources, and could potentially be carried out without explicit background noise characterization. However, state of…
Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007--2008 credit crisisphysics.soc-ph
We study the cluster dynamics of multichannel (multivariate) time series by representing their correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of indiv…
This paper demonstrates dynamic hyper-parameter setting, for deep neural network training, using Mutual Information (MI). The specific hyper-parameter studied in this paper is the learning rate. MI between the output layer and true outcomes is used to dynamically set the learning rate of the network through the trainin…
The analytical tractability of affine (short rate) models, such as the Vasicek and the Cox-Ingersoll-Ross models, has made them a popular choice for modelling the dynamics of interest rates. However, in order to account properly for the dynamics of real data, these models need to exhibit time-dependent or even stochast…
Rate-In dynamically adjusts dropout rates during inference to improve uncertainty estimation in neural networks.
problem Static dropout rates lead to suboptimal uncertainty estimates in neural networks.
method Rate-In dynamically adjusts dropout rates using information-theoretic principles.
result Rate-In improves calibration and sharpens uncertainty estimates compared to fixed or heuristic dropout rates.
ISOKANN learns collective variables and effective dynamics for metastable transitions.
problem Understanding metastable transitions in complex molecular systems.
method Integrates Koopman operators with neural networks to extract CVs and effective dynamics.
result Reconstructs coarse-grained kinetics and reproduces transition times across barriers.
New dynamics for SGD in small learning rate regime.
problem Improving stochastic gradient descent in small learning rate regime.
method Introducing stochastic modified flows and distribution dependent stochastic modified flows.
result Captures fluctuating dynamics of SGD in small learning rate - infinite width scaling regime.
Deep RL tackles mmWave backhaul resource allocation challenges.
problem Limited backhaul capacity and dynamic data rates in mmWave systems.
method Deep reinforcement learning (DRL) to predict and allocate backhaul resources.
result Efficient utilization of backhaul resources through DRL.
Bayesian optimisation for dynamically adjusting learning rates in machine learning models.
problem Dynamic adjustment of learning rates schedules in machine learning models.
method Probabilistic model based on latent Gaussian processes and auto-/regressive formulation.
result Flexibly adjusts learning rates schedules to abrupt changes of behaviours.
Solomon and Golo [1] have recently proposed an autocatalytic (self-reinforcing) feedback model which couples a macroscopic system parameter (the interest rate), a microscopic parameter that measures the distribution of the states of the individual agents (the number of firms in financial difficulty) and a peer-to-peer …
New model explains deep learning performance at large learning rates.
problem Understanding deep learning performance at different learning rates.
method Developed neural networks with solvable training dynamics.
result Large learning rates lead to convergence to flatter minima.
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneous…
Proposes DHEB model for predicting online ad performance.
problem Sparse data at individual unit level in online advertising.
method Dynamic Hierarchical Empirical Bayesian (DHEB) model with data-driven hierarchy and shrinkage-based estimations.
result Proposed method outperforms other models in accuracy and efficiency.
On Capital Dependent Dynamics of Knowledgephysics.soc-ph
We investigate the dynamics of growth models in terms of dynamical system theory. We analyse some forms of knowledge and its influence on economic growth. We assume that the rate of change of knowledge depends on both the rate of change of physical and human capital. First, we study model with constant savings. The mod…
The paper explores how dynamic preconditioning affects the CLT in online averaging.
problem When does dynamic preconditioning preserve the Polyak-Ruppert CLT?
method The authors decompose the averaged error and identify a stabilization-rate threshold for the CLT to hold.
result The CLT holds if the dynamic remainder vanishes in and the stabilization rate exceeds a threshold.
GOALS improves learning rate selection for dynamic MBSS in deep learning.
problem Challenges in selecting learning rates for dynamic MBSS in deep learning.
method Gradient-only approximation line search (GOALS) for dynamic MBSS loss functions.
result GOALS reduces model errors in multimodal cases.
Dynamical Volatilities for Yen-Dollar Exchange Ratescond-mat.other
We study the continuous time random walk theory from financial tick data of the yen-dollar exchange rate transacted at the Japanese financial market. The dynamical behavior of returns and volatilities in this case is particularly treated at the long-time limit. We find that the volatility for prices shows a power-law w…
We use techniques from network science to study correlations in the foreign exchange (FX) market over the period 1991--2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clus…
MOSAIC detects change points in dynamic networks with low-rank and sparse changes.
problem Detecting change points in dynamic networks with specific structural properties.
method Eigen-decomposition-based test with screened signals and residual-based adjustment.
result MOSAIC achieves minimax-optimal detection and testing rates.
On the Dynamics of Inference and Learningcond-mat.dis-nn
Bayesian updating is modeled as a dynamical system, revealing learning rate laws.
problem Modeling Bayesian inference as a dynamical system.
method Formulated Bayesian updating as a continuous dynamical system, solving for trajectories in information geometry.
result Learning rate is governed by a power-law when the Cramér-Rao bound is saturated.
Global Convergence of the ODE Limit for Online Actor-Critic Algorithms in Reinforcement Learningcs.LG
Actor-critic algorithms converge to an ODE as data samples change dynamically.
problem Challenging to mathematically analyze due to non-i.i.d. data samples.
method Proved convergence to an ODE using time rescaling and geometric ergodicity.
result Convergence to the ODE limit and its properties proven.
An Empirical Study on Arrival Rates of Limit Orders and Order Cancellation Rates in Borsa Istanbulq-fin.MF
Study models arrival rates and cancellation rates of limit orders in Borsa Istanbul.
problem Understanding order dynamics in Borsa Istanbul's stock market.
method Used limit order book data from Garanti Bank. Tested three discrete probability distributions and two theoretical models for arrival rates. Examined cancellation rates using L1 norms.
result Modelled daily, weekly, and monthly arrival rates of limit orders in the first fifteen bid and ask price levels.
Investigates consistency of FX rate dynamics under inversion.
problem Consistency of jump-diffusion dynamics for FX rates under inversion.
method Calibrated Heston and SABR models, analyzed jumps in domestic and foreign measures.
result Determines conditions for consistency in FX rate dynamics under inversion.
Develops a bi-variate stochastic framework to model mortality and interest rates with long-range dependence.
problem Captures long-range dependence and instantaneous correlation in mortality and interest rates.
method Mixed fractional Brownian motions, analytical solutions, risk-neutral measure, sequential parameter estimation.
result Explicit pricing of zero-coupon bonds and extreme mortality bonds, practical implications for pricing and risk management.
The paper uses filtering techniques to predict rating transitions.
problem Analyzing the effect of business cycles on rating transitions.
method Point process filtering framework to infer latent factor states.
result Efficient estimation of latent factor parameters for real-time detection of economic changes.
The paper develops methods to estimate optimal treatment sequences under policy constraints.
problem Estimating the best sequence of treatments over multiple stages for individuals.
method Empirical welfare maximization approach, solving treatment assignment sequentially or simultaneously.
result Established convergence rates and upper bounds for estimation methods.
We discover scaling laws for kernel regression loss under various learning rate schedules.
problem Understanding loss dynamics and learning rate schedules in kernel regression.
method Theoretical analysis of stochastic gradient descent on a power-law kernel regression model.
result Established a Functional Scaling Law (FSL) capturing the full loss trajectory under arbitrary learning rate schedules.