Lukasz Sliwinski,Tanut Treetanthiploet,David Siska et al.
Lukasz Sliwinski et al.
The use of learning algorithms for automatic price adjustments in markets is on the rise. However, these algorithms often assume that reward distributions for actions are uncorrelated and stationary, a condition that does not hold in compet...
Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria [0.03%]
深度强化学习寻找纳什均衡的计算性能
Christoph Graf,Viktor Zobernig,Johannes Schmidt et al.
Christoph Graf et al.
We test the performance of deep deterministic policy gradient-a deep reinforcement learning algorithm, able to handle continuous state and action spaces-to find Nash equilibria in a setting where firms compete in offer prices through a unif...
On the Optimal Size and Composition of Customs Unions: An Evolutionary Approach [0.03%]
关于关税同盟的最优规模和构成的一种进化方法
Takfarinas Saber,Dominik Naeher,Philippe De Lombaerde
Takfarinas Saber
Customs unions enable countries to freely access each other's markets, which is thought to increase intra-regional trade and economic growth. However, accession to a customs union also comes with the condition that all members need to conse...
Aiche Avishay,Cohen Gil,Griskin Vladimir
Aiche Avishay
This research studies different gap opening price strategies using artificial intelligence and big data analysis to learn how fast new information is absorbed into the stock's price. Our system is designed to optimize trading results of dif...
Erniel B Barrios,Paolo Victor T Redondo
Erniel B Barrios
Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on s...
The Rise and Fall of Financial Flows in EU 15: New Evidence Using Dynamic Panels with Common Correlated Effects [0.03%]
欧盟15国金融流量的兴衰：基于动态面板公共相关效应的新证据
Mariam Camarero,Alejandro Muñoz,Cecilio Tamarit
Mariam Camarero
This paper assesses capital mobility for a panel of 15 European countries for the period 1970-2019 using dynamic common correlated effects modeling as proposed in Chudik and Pesaran (J Econ 188(2):393-420, 2015). In particular, we account f...
GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks [0.03%]
基于神经网络的GARCH模型在风险价值预测中的应用
Mateusz Buczynski,Marcin Chlebus
Mateusz Buczynski
This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility c...
LSTM-GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios [0.03%]
基于LSTM-GARCH混合模型的加密货币组合波动率预测方法研究
Andrés García-Medina,Ester Aguayo-Moreno
Andrés García-Medina
In the present work, the volatility of the leading cryptocurrencies is predicted through generalised autoregressive conditional heteroskedasticity (GARCH) models, multilayer perceptron (MLP), long short-term memory (LSTM), and hybrid models...
Impact of Climate Variables Change on the Yield of Wheat and Rice Crops in Iran (Application of Stochastic Model Based on Monte Carlo Simulation) [0.03%]
基于Monte Carlo模拟的随机模型在伊朗气候变量变化对小麦和水稻产量影响中的应用研究
Akram Javadi,Mohammad Ghahremanzadeh,Maria Sassi et al.
Akram Javadi et al.
This study aims to predict the yield of two strategic crops in Iran; wheat and rice, under climate scenarios that indicate probable changes in climate variables. It implemented by a stochastic model based on the Monte Carlo method. This mod...
Gumsong Jo,Hyokil Kim,Hoyong Kim et al.
Gumsong Jo et al.
Here we have proposed fuzzy portfolio selection model using stochastic correlation (FPSMSC) to overcome limitations both in fuzzy and stochastic world. The newly proposed model not only gets harmonious efficient frontier, but also considers...