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Found 17 papers, 4 papers with code
Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups
To estimate IFAM under the multi-level factor model, we introduce a factor-adjusted GLASSO estimator to address the prevalent global factor effect in the inverse covariance matrix.
Cubic-based Prediction Approach for Large Volatility Matrix using High-Frequency Financial Data
In this paper, we develop a novel method for predicting future large volatility matrices based on high-dimensional factor-based It\^o processes.
Robust Reinforcement Learning under Diffusion Models for Data with Jumps
Reinforcement Learning (RL) has proven effective in solving complex decision-making tasks across various domains, but challenges remain in continuous-time settings, particularly when state dynamics are governed by stochastic differential equations (SDEs) with jump components.
GECKO: Generative Language Model for English, Code and Korean
GECKO is pretrained on the balanced, high-quality corpus of Korean and English employing LLaMA architecture.
Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector
In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Ito semimartingale models using high-frequency financial data.
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels
In the evolving landscape of federated learning (FL), addressing label noise presents unique challenges due to the decentralized and diverse nature of data collection across clients.
Large Global Volatility Matrix Analysis Based on Observation Structural Information
In this paper, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets.
Large Volatility Matrix Analysis Using Global and National Factor Models
Several large volatility matrix inference procedures have been developed, based on the latent factor model.
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction
Historically, this challenge has been tackled using numerical weather prediction (NWP) models, grounded on physics-based simulations.
Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective
From the empirical study, we find evidence of financial contagion from the U. S. to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation.
Exponential GARCH-Ito Volatility Models
This paper introduces a novel Ito diffusion process to model high-frequency financial data, which can accommodate low-frequency volatility dynamics by embedding the discrete-time non-linear exponential GARCH structure with log-integrated volatility in a continuous instantaneous volatility process.
Self-supervised Text-to-SQL Learning with Header Alignment Training
Since we can leverage a large amount of unlabeled data without any human supervision to train a model and transfer the knowledge to target tasks, self-supervised learning is a de-facto component for the recent success of deep learning in various fields.
Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data
This paper proposes a dynamic process of portfolio risk measurement to address potential information loss.
Overnight GARCH-Itô Volatility Models
Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics.
Domain-agnostic Question-Answering with Adversarial Training
Adapting models to new domain without finetuning is a challenging problem in deep learning.
FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud
We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation.
Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-observed Low Rank Matrix with Noise
In practice, the singular vectors and singular values of the low rank matrix play a pivotal role for statistical analyses and inferences.
Methodology
