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Modelling Asset Price Volatility Over Time Using LSTM
Datta, Suman - 2022
This note explores machine learning based modelling approach over classical quantitative methods with a focus on modelling realized volatility of asset price over time. Initially, a few modelling assumptions of classical quantitative finance are examined using recent market data. Daily stock...
Persistent link: https://www.econbiz.de/10014238231
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Does GINI respond to income volatility in an asymmetric manner? : evidence from 41 countries
Bahmani-Oskooee, Mohsen; Motavallizadeh-Ardakani, Amid - In: Economic systems 44 (2020) 2, pp. 1-17
Persistent link: https://www.econbiz.de/10012593400
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Méthodes de décomposition de la volatilité d'un portefeuille : une nouvelle approche d'estimation des risques par l'indice de Gini
Mussard, Stéphane; Terraza, Virginie - In: Revue d'économie politique 114 (2004) 4, pp. 557-571
Persistent link: https://www.econbiz.de/10002233811
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Support vector regression based GARCH model with application to forecasting volatility of financial returns
Chen, Shiyi (contributor); Jeong, Kiho (contributor);  … - 2008
In recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving...
Persistent link: https://www.econbiz.de/10003636113
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Forecasting Volatility in Financial Markets! By Introducing a GA-Assisted SVR-Garch Model
Habibnia, Ali - 2012
In recent years, support vector regressions (SVRs), a novel artificial neural network (ANN) technique, has been successfully used as a nonparametric tool for regression estimation and forecasting time series data. In this thesis, we deal with the application of SVRs in financial markets...
Persistent link: https://www.econbiz.de/10013100878
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High-dimensional statistical learning techniques for time-varying limit order book networks
Chen, Shi; Härdle, Wolfgang; Schienle, Melanie - 2021
This paper provides statistical learning techniques for determining the full own-price market impact and the relevance and effect of cross-price and cross-asset spillover channels from intraday transactions data. The novel tools allow extracting comprehensive information contained in the limit...
Persistent link: https://www.econbiz.de/10012614016
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Volatility Model Calibration with Neural Networks a Comparison between Direct and Indirect Methods
Röder, Dirk - 2020
In a recent paper "Deep Learning Volatility" a fast 2-step deep calibration algorithm for rough volatility models was proposed: in the first step the time consuming mapping from the model parameter to the implied volatilities is learned by a neural network and in the second step standard solver...
Persistent link: https://www.econbiz.de/10012828944
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Factor Models, Machine Learning, and Asset Pricing
Giglio, Stefano; Kelly, Bryan T.; Xiu, Dacheng - 2022
We survey recent methodological contributions in asset pricing using factor models and machine learning. We organize these results based on their primary objectives: estimating expected returns, factors, risk exposures, risk premia, and the stochastic discount factor, as well as model comparison...
Persistent link: https://www.econbiz.de/10013322001
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Topological tail dependence : evidence from forecasting realized volatility
Souto, Hugo Gobato - In: The Journal of finance and data science : JFDS 9 (2023), pp. 1-26
This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory … behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure … of this study provide evidence that the predictions drawn from the Topological Tail Dependence Theory are correct and …
Persistent link: https://www.econbiz.de/10014514075
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Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns
Chen, Shiyi - 2017
In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...
Persistent link: https://www.econbiz.de/10012966267