Paper addresses stability in multi-asset American option pricing.
problem Stability in multi-asset American option pricing problems.
method Semi-discretization approach followed by full discretization.
result Stability conditions found for numerical solution.
The study compares differencing methods for financial data and finds fractional differencing improves model performance.
problem Improving financial time series forecasting models using appropriate data transformation techniques.
method Comparative analysis of traditional logarithmic returns and fractional differencing methods, including tempered extensions.
result Fractional differencing methods improve model forecasting performance and trading strategy effectiveness.
The study proves fractional-order differences and equations are key to modeling long and short memory in economics.
problem Modeling long and short memory in economic processes with discrete fractional differencing and integration.
method Proved discrete fractional differencing and integration are Grunwald-Letnikov fractional differences of non-integer order d. ARIMA and ARFIMA models are fractional-order difference equations. Proved exact fractional-order differences are needed for power law memory.
result Fractional differential equations are necessary for modeling continuous time long and short memory with power law.
This paper re-evaluates TD in deep RL, finding MC can be a viable alternative.
problem Understanding the role of temporal differencing (TD) in deep reinforcement learning.
method Designed environments to control for factors affecting performance in deep RL, comparing TD with infinite-horizon Monte Carlo (MC).
result Finite-horizon Monte Carlo is not inferior to TD, even with sparse or delayed rewards.
This study examines chaos in FIGARCH processes using various metrics.
problem Analyzing chaos in FIGARCH processes for financial time series.
method Computed mutual information, correlation dimensions, FNNs, Lyapunov exponents for FIGARCH (p,d,q) processes and financial time series.
result Maximal Lyapunov exponents are negative, suggesting FIGARCH (p,d,q) is not deterministic chaotic.
New method combines long-memory reservoirs for accurate dengue forecasting from short data.
problem Accurate dengue forecasting from short, noisy, non-stationary, and nonlinear data.
method Fractional ESN and Wavelet ESN frameworks integrating long-term memory.
result fESN and wESN outperform baselines in multiple dengue datasets and forecasting horizons.
Online learning rbfnet improves multi-horizon returns forecasts for financial time series.
problem Nonstationarity and concept drift in financial time series.
method Combines feature representation transfer with sequential optimisation.
result Online learning rbfnet outperforms random-walk and batch learners.
This study uses moving average cluster entropy to analyze financial market dynamics.
problem Understanding long-range dependence in financial markets.
method Moving average cluster entropy approach applied to ARFIMA and FBM processes.
result Long-range positive correlation in financial markets is linked to the cluster entropy behavior.
A new method solves American put options with high accuracy and speed.
problem Solving American put options with high accuracy and speed.
method Adaptive fourth-order Runge-Kutta-Fehlberg method coupled with a fourth-order compact scheme.
result The method provides a more accurate solution and better performance in terms of computational speed.
New method for active subspace analysis reduces gradient evaluations needed.
problem Efficiently perform subspace sensitivity analysis on expensive or noisy functions.
method Develops acquisition functions for sequential learning of active subspaces using Gaussian process surrogate models.
result ASM estimator can be computed in closed form for Gaussian process surrogates, reducing need for finite differencing.
ABBA creates a new symbolic time series representation based on Brownian bridge.
problem Representing time series data in a compact, symbolic form.
method Adaptive polygonal chain approximation followed by mean-based clustering.
result ABBA outperforms other representations in preserving time series shape information.
In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…
Deep model forecasts correlated multivariate time series.
problem Forecasting correlated multivariate time series.
method Deep learning structural model using CNN-LSTM architecture.
result Model outperforms state-of-the-art methods in various time series data sets.
Enhanced LSTM predicts equity trends, outperforming traditional methods.
problem Nonstationary and nonlinear market regimes challenge trend forecasting.
method LSTM-based framework for forecasting equity trend differences.
result LSTM framework outperforms traditional methods in terms of overall PNL.
We develop methods to approximate derivatives for causal inference problems using data.
problem Estimating causal effects from data when distributions are not known.
method Constructive algorithm approximating Gateaux derivatives via finite differencing.
result Derives conditions for finite-difference approximations to preserve statistical benefits.
Study forecasts U.S. bond index using deep learning, finding persistence is key.
problem Forecasting U.S. aggregate bond index with deep learning methods.
method Constructed a stationary but maximally persistent representation of the bond index, evaluated using MLPs and CNNs.
result Deep learning models outperform traditional methods in short-horizon forecasting of bond indices.
NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.
problem Sparse and nonstationary movement speed data from urban roads.
method Nonstationary Temporal Matrix Factorization (NoTMF) model.
result NoTMF outperforms baseline models in forecasting urban road movement speeds.
New method constructs multilayer networks from financial data, capturing dependencies across different risk factors.
problem Difficult construction of multilayer networks, neglecting time delays and interdependencies.
method Tucker tensor autoregression for direct multilayer network construction.
result Captures within and between connections, identifies strong interconnections between volumes and prices layers.
The paper calculates prices for special options using mixed-exponential jumps.
problem Pricing special options in a mixed-exponential jump-diffusion model.
method Derive joint distributions of a mixed-exponential jump-diffusion process and its occupation times.
result Various joint distributions derived for pricing options.
We study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. While at shorter microscopic time lags the distribution has power-law tails, for mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an expon…
MIM networks predict non-stationary spatiotemporal dynamics using differential signals.
problem Predicting non-stationary spatiotemporal processes with high-order variations.
method Memory In Memory (MIM) networks with cascaded memory modules.
result Achieved state-of-the-art results on four spatiotemporal prediction tasks.
We study singularity formation in spherically symmetric solutions of the charge-one and charge-two sector of the (2+1)-dimensional S^2 sigma-model and the (4+1)-dimensional Yang-Mills model, near the adiabatic limit. These equations are non-integrable, and so studies are performed numerically on rotationally symmetric …
Proposes a new model using exponential smoothing cells for robust time series analysis.
problem Challenges of traditional exponential smoothing in noisy data and changing series.
method Flexible model using exponential smoothing cells for overlapping time windows, solving a structured convex optimization problem.
result Can detect and remove outliers, denoise data, fill in missing observations, and provide meaningful forecasts.
New model captures time-varying volatility with stochastic exponential tails.
problem Capturing time-varying volatility and stochastic skewness in financial markets.
method Normal Tempered Stable distribution with time-varying parameter.
result Model better explains market option prices with stochastic exponential tails.
Method improves treatment effect prediction robust to unknown covariate shifts.
problem Estimating heterogeneous treatment effects for different populations.
method Post-processing CATE T-learners with multi-accurate predictors to handle unknown covariate shifts.
result Improves bias and mean squared error in simulations with covariate shifts.
Ancient solutions of heat equation with exponential growth are analytic in time.
problem Analyticity of solutions to the heat equation in time.
method Proving analyticity for ancient solutions with exponential growth.
result Ancient solutions with exponential growth are analytic in time.
A fast, accurate method for pricing American options with free boundaries.
problem Pricing American options with free boundaries efficiently and accurately.
method A sixth-order compact finite difference scheme with a dynamic staggered boundary scheme and 3(2) R-K Bogacki-Shampine time stepping.
result An efficient sixth-order compact scheme for pricing American options with free boundaries.
We develop a new Monte Carlo variance reduction method to estimate the expectation of two commonly encountered path-dependent functionals: first-passage times and occupation times of sets. The method is based on a recursive approximation of the first-passage time probability and expected occupation time of sets of a Le…
Exponentially smoothed RNNs improve industrial forecasting.
problem Complexity and non-stationarity in industrial time series data.
method Exponential smoothed recurrent neural networks (RNNs) for modeling non-linear dynamics.
result Exponentially smoothed RNNs outperform traditional models in multi-step forecasting.
Develops a new exponential map for time-varying vector fields.
problem Lack of global flows for general time-varying vector fields.
method Categorical development of spaces of vector fields and flows, allowing for systematic localisation.
result Derives the homeomorphism of the exponential map for vector fields with measurable time-dependence.
Develops SQR models for multivariate exponential families allowing positive dependencies.
problem Lack of positive dependencies in multivariate graphical models for exponential and Poisson distributions.
method Introduces Square Root Graphical Models (SQR) derived from univariate exponential distributions, with methods for parameter estimation and likelihood approximation.
result Allows for arbitrary positive and negative dependencies in multivariate distributions without constraints on parameter values.
New Thompson sampling algorithm reduces regret for exponential family bandits.
problem Minimizing regret in multi-armed bandit problems with exponential family rewards.
method Proposes ExpTS and ExpTS+ algorithms using novel sampling distributions. result Minimizes both finite-time and asymptotic regret for exponential family rewards.
A new method for exponentially weighted moving models using approximations.
problem Efficiently updating moving averages for time series data.
method Approximates EWMM using a fixed window and quadratic term, solving non-growing problems.
result Approximation produces estimates similar to exact EWMM.
Paper calculates the distribution of time spent below zero in risk models.
problem Analyzing time spent below zero in risk models.
method Analytical expressions for the distribution of occupation times.
result Improved understanding of risk processes by providing distribution formulas.
CDEFs reduce model complexity and uncover time correlations.
problem Model complexity and data efficiency in probabilistic modeling.
method Builds on deep exponential families, ties weights for reduced parameters.
result CDEFs uncover time correlations with fewer parameters.
We analyze waiting times for price changes in a foreign currency exchange rate. Recent empirical studies of high frequency financial data support that trades in financial markets do not follow a Poisson process and the waiting times between trades are not exponentially distributed. Here we show that our data is well ap…
This paper extends exponential smoothing to distributional time series using Wasserstein distance.
problem Forecasting distributional time series with exponential smoothing.
method Generalized exponential smoothing in Wasserstein space, with consistent parameter estimation.
result Wasserstein exponential smoothing outperforms traditional methods in high-frequency financial and electricity demand data.
Classical knot recognition problem solved in NP with exponential time algorithm.
problem Determining if a virtual knot is classical.
method Proved NP membership and provided an exponential time algorithm.
result Classical knot recognition problem is in NP.
Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.
New theory extends LQ control to non-exponential discount scenarios.
problem Time-inconsistent deterministic LQ control problems.
method Extended equivalent relationship to non-exponential discount functions, studied Riccati equation solvability.
result Existence and uniqueness of linear equilibrium for time-inconsistent LQ problem.
It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the return intervals of extreme values of the fluctuations of resistance and defect-fraction displa…
Study optimal strategy for maximizing exponential utility in financial market with linear price impact.
problem Maximizing exponential utility in financial market with linear price impact.
method Purely probabilistic approach using duality.
result Computed optimal portfolio strategy and value for Ornstein-Uhlenbeck process.
This paper proposes a technique for the unsupervised detection and tracking of arbitrary objects in videos. It is intended to reduce the need for detection and localization methods tailored to specific object types and serve as a general framework applicable to videos with varied objects, backgrounds, and image qualiti…
Consider power utility maximization of terminal wealth in a 1-dimensional continuous-time exponential Levy model with finite time horizon. We discretize the model by restricting portfolio adjustments to an equidistant discrete time grid. Under minimal assumptions we prove convergence of the optimal discrete-time strate…
Study shows Bitcoin security tied to mining rewards and prices.
problem Understanding Bitcoin security's dependency on market outcomes.
method Used ARDL approach with daily blockchain and Bitcoin data from 2014-2019.
result Bitcoin security outcomes linked to Bitcoin price and mining rewards.
We provide a complete characterization of the class of one-dimensional time-homogeneous diffusions consistent with a given law at an exponentially distributed time using classical results in diffusion theory. To illustrate we characterize the class of diffusions with the same distribution as Brownian motion at an expon…
Auto-regressive models improve smoothing efficiency with exponentially tapered windows.
problem Improving time-series smoothing efficiency.
method An auto-regressive formulation for time-series smoothing.
result Auto-regressive models result in moving means with exponentially tapered windows.
Study optimal stopping in non-exponential discounting, finding unique equilibrium.
problem Optimal stopping under non-exponential discounting.
method Iterative approach to find subgame perfect Nash equilibria.
result Existence and uniqueness of optimal equilibrium with higher value.