Zobrazeno 1 - 10
of 521
pro vyhledávání: '"Ghysels, Eric"'
Autor:
Ghysels, Eric, Morgan, Jack
We formulate quantum computing solutions to a large class of dynamic nonlinear asset pricing models using algorithms, in theory exponentially more efficient than classical ones, which leverage the quantum properties of superposition and entanglement.
Externí odkaz:
http://arxiv.org/abs/2405.01479
We present a classical enhancement to improve the accuracy of the Hybrid variant (Hybrid HHL) of the quantum algorithm for solving linear systems of equations proposed by Harrow, Hassidim, and Lloyd (HHL). We achieve this by using higher precision qu
Externí odkaz:
http://arxiv.org/abs/2404.10103
Quantum computers are not yet up to the task of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper we introduce a class of stochastic processes that are both realistic in te
Externí odkaz:
http://arxiv.org/abs/2311.00825
This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validatio
Externí odkaz:
http://arxiv.org/abs/2308.10993
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconom
Externí odkaz:
http://arxiv.org/abs/2307.02673
In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for p
Externí odkaz:
http://arxiv.org/abs/2212.12981
Autor:
Brown, Gregory W1 (AUTHOR), Ghysels, Eric2 (AUTHOR), Gredil, Oleg R3 (AUTHOR) ogredil@tulane.edu
Publikováno v:
Review of Financial Studies. Mar2023, Vol. 36 Issue 3, p945-986. 42p.
