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.
Study sharp convergence rates of empirical UOT for spatio-temporal point processes.
Personal income distributions in Japan are analyzed empirically and a simple stochastic model of the income process is proposed. Based on empirical facts, we propose a minimal two-factor model. Our model of personal income consists of an asset accumulation process and a wage process. We show that these simple processes…
Developed accurate empirical potentials for Si:H nanowires using multi-fidelity Gaussian process.
We develop a scale-invariant truncated Lévy (STL) process to describe physical systems characterized by correlated stochastic variables. The STL process exhibits Lévy stability for the probability density, and hence shows scaling properties (as observed in empirical data); it has the advantage that all moments are fini…
Improved analysis of UCRL2 with empirical Bernstein inequality reduces exploration-exploitation regret.
Empirical Gaussian Processes learn flexible priors from data.
New empirical process bounds reveal trade-off between dependence and complexity in nonparametric learning.
Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…
The paper bounds the expectation of empirical processes indexed by Hölder classes.
Time reversal invariance can be summarized as follows: no difference can be measured if a sequence of events is run forward or backward in time. Because price time series are dominated by a randomness that hides possible structures and orders, the existence of time reversal invariance requires care to be investigated. …
The paper studies empirical processes from nearest neighbors in regression.
We prove a new and general concentration inequality for the excess risk in least-squares regression with random design and heteroscedastic noise. No specific structure is required on the model, except the existence of a suitable function that controls the local suprema of the empirical process. So far, only the case of…
The paper examines the short-time implied volatility of additive processes and finds key parameters.
We analyze the probability density function (PDF) of waiting times between financial loss exceedances. The empirical PDFs are fitted with the self-excited Hawkes conditional Poisson process with a long power law memory kernel. The Hawkes process is the simplest extension of the Poisson process that takes into account h…
The paper proves concentration inequalities for two-sample rank processes and applies them to ranking performance criteria.
We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S\&P 500 stocks from the New York Stock Exchange. After establishing an empirical overview we compare the quantile-based correlation function to…
The paper provides a new uniform tail bound for empirical processes.
Transformers can learn Markov processes with constant depth, surprising results.
Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy. By applying posterior sampling in model-free RL under the hypothesis of GP, we propose Gaussian Process Posterior Sampling Reinforcement Learning(GPPSTD) algorithm in continuous state space, giving theoretical justif…
We propose the point process model as the Poissonian-like stochastic sequence with slowly diffusing mean rate and adjust the parameters of the model to the empirical data of trading activity for 26 stocks traded on NYSE. The proposed scaled stochastic differential equation provides the universal description of the trad…
We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order direc…
In this paper we propose an overview of the recent academic literature devoted to the applications of Hawkes processes in finance. Hawkes processes constitute a particular class of multivariate point processes that has become very popular in empirical high frequency finance this last decade. After a reminder of the mai…
Study finds short-term instability in financial ARCH models.
The paper analyzes optimal execution strategies for traders with inventory processes influenced by Brownian motion.
The paper offers efficient algorithms for combinatorial and linear bandits using empirical process theory.
RL approach for continuous-time mean-variance portfolio selection with empirical validation.
Paper forecasts financial trading durations using a new point process model.
Develops robust MDPs for unknown disturbances with performance guarantees.
Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between random, wide, fully connected, feedforward networks with more than one hidden layer and Gaussian processes with a recursive ker…
This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an e…
The non-gaussianity of processes observed in financial markets and relatively good performance of gaussian models can be reconciled by replacing the Brownian motion with Levy processes whose Levy densities decay as exp(-lambda|x|) or faster, where lambda>0 is large. This leads to asymptotic pricing models. The leading …
EnKF's update is shown to be similar to Matheron's method in Gaussian process regression.
Study improves ERM for heavy-tailed data with dependent inputs.
Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms. One approach is to unfold finite number of iterations of conventional optimization algorithms and to learn parameters in the algorithms. However, these are forward methods and are indeed neither iterative nor convergent…
A Bayesian agent learns about the structure of a stationary process from ob- serving past outcomes. We prove that his predictions about the near future become ap- proximately those he would have made if he knew the long run empirical frequencies of the process.
We compare our results on empirical analysis of financial data with simulations of two stochastic models of the dynamics of stock market prices. The two models are (i) the truncated Lévy flight recently introduced by us and (ii) the ARCH(1) and GARCH(1,1) processes. We find that the TLF well describes the scaling and i…
Paper speeds up Gaussian process inference using Matérn kernels.
This paper investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by \emph{general}, we mean that many stationary stochastic processes can be included. We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a u…
Model predicts bid and ask price dynamics with spread-dependent intensities.
A novel Hawkes Process model captures order sizes in LOBs, improving fit quality and market impact studies.
Augmented bridge matching preserves coupling information between distributions.
Enhancing the Black-Scholes model with Lévy processes and Malliavin calculus
A novel version of the Continuous-Time Random Walk (CTRW) model with memory is developed. This memory means the dependence between arbitrary number of successive jumps of the process, while waiting times between jumps are considered as i.i.d. random variables. The dependence was found by analysis of empirical histogram…
New method for spatiotemporal data regression using Gaussian processes.
Unified technique for sequential estimation of convex divergences.
A new method uses multifidelity Gaussian process regression to solve nonlinear PDEs.
The paper explains how neural networks learn less salient frequency components during training.
The data processing inequality doesn't always hold in practice, showing benefits in low-level tasks.