arXiv research

New algorithms reduce dynamic regret in online MDPs with changing losses.

Study shows changes in information sharing between Bitcoin markets during 2017 crash.

Study analyzes stock market dynamics using recurrence measures and transitions.

Paper introduces EEMs for pricing contingent claim returns.

Paper proposes algorithms to minimize both dynamic and adaptive regret simultaneously.

From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks. After the change, a subset of anomalous nodes which compares dissimilarly with the normal nodes. We study a sim…

Dynamic Influence Tracker measures changing sample importance during model training.

Framework LiLY recovers latent causal variables from time-series data under distribution shifts.

Risk measures applied to dynamic Markov processes with varying risk aversion.

The paper prices weather contracts using a complex temperature model.

New method reduces dynamic regret for non-stationary bandits.

Enhances resilience evaluation by using dynamic convex risk measures.

Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.

The paper addresses dynamic capital structure models with defaultable debt, proving existence and uniqueness.

New algorithm reduces dynamic regret without prior function change knowledge.

The paper shows measures equidistribute on affine submanifolds with a rate.

New algorithm reduces control error in systems with changing dynamics.

KNF uses Koopman theory to forecast time series with changing dynamics.

Paper introduces a novel error measure for neural networks integrating statistical and information theory.

Proposes a deep learning method for modeling dynamic individual-level latent trajectories with changing parameters.

The monitoring of large dynamic networks is a major chal- lenge for a wide range of application. The complexity stems from properties of the underlying graphs, in which slight local changes can lead to sizable variations of global prop- erties, e.g., under certain conditions, a single link cut that may be overlooked du…

Rate change calculations in the literature involve deterministic methods that measure the change in premium for a given policy. The definition of rate change as a statistical parameter is proposed to address the stochastic nature of the premium charged for a policy. It promotes the idea that rate change is a property o…

New algorithm reduces decision switching in dynamic environments.

In electricity markets, it is sensible to use a two-factor model with mean reversion for spot prices. One of the factors is an Ornstein-Uhlenbeck (OU) process driven by a Brownian motion and accounts for the small variations. The other factor is an OU process driven by a pure jump Lévy process and models the characteri…

Review of correlation-based financial networks and entropy measures.

Improved algorithm for adaptive dueling bandits with near-optimal regret bound.

Whilst there are many approaches to detecting changes in mean for a univariate time-series, the problem of detecting multiple changes in slope has comparatively been ignored. Part of the reason for this is that detecting changes in slope is much more challenging. For example, simple binary segmentation procedures do no…

The article provides representations of exchange option prices under SVJD dynamics.

Algorithm learns from changing zero-sum games with no regret.

We consider a multivariate default system where random environmental information is available. We study the dynamics of the system in a general setting and adopt the point of view of change of probability measures. We also make a link with the density approach in the credit risk modelling. In the particular case where …

Study uses neural networks to improve option pricing accuracy.

For a commodity spot price dynamics given by an Ornstein-Uhlenbeck process with Barndorff-Nielsen and Shephard stochastic volatility, we price forwards using a class of pricing measures that simultaneously allow for change of level and speed in the mean reversion of both the price and the volatility. The risk premium i…

The study analyzes the differences between physical and risk-neutral correlation estimates for equity baskets.

We follow the main stocks belonging to the New York Stock Exchange and to Nasdaq from 2003 to 2012, through years of normality and of crisis, and study the dynamics of networks built on two measures expressing relations between those stocks: correlation, which is symmetric and measures how similar two stocks behave, an…

The paper values reinsurance contracts for dynamic catastrophe claims without arbitrage.

This paper explores the dependence modeling of financial assets in a dynamic way and its critical role in measuring risk. Two new methods, called Accelerated Moving Window method and Bottom-up method are proposed to detect the change of copula. The performance of these two methods together with Binary Segmentation \cit…

Stable topological summary captures evolving dependency structure in dynamic Bayesian networks.

We propose a simple approach to a problem introduced by Galatolo and Pollicott, consisting in perturbing a dynamical system in order for its absolutely continuous invariant measure to change in a prescribed way. Instead of using transfer operators, we observe that restricting to an infinitesimal conjugacy already yield…

Innovative inequalities for divergences with applications in PAC-Bayesian bounds and Monte Carlo.

Unified framework detects changes in complex system models.

We present a notion of super Ricci flow for time-dependent finite weighted graphs. A challenging feature is that these flows typically encounter singularities where the underlying graph structure changes. Our notion is robust enough to allow the flow to continue past these singularities. As a crucial tool for this purp…

New model detects gradual changes in processes more accurately.

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 dev…

Within many real-world networks the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal …

Deep learning dynamics and NTK evolution studied through diverse measures.

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…

DiwE uses regional distribution changes to create diverse ensemble classifiers for concept drift.

SimCD simultaneously clusters cells and identifies differential gene expression in scRNA-seq data.