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Financial Econometrics - R Tutorial Guidance
Wang, Yizhi; Vigne, Samuel - 2021
Persistent link: https://www.econbiz.de/10013223934
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Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities
Rudy, Jozef - 2013
The motivation for this paper is to apply a statistical arbitrage technique of pairs trading to high-frequency equity data and compare its profit potential to the standard sampling frequency of daily closing prices. We use a simple trading strategy to evaluate the profit potential of the data...
Persistent link: https://www.econbiz.de/10013081228
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The One-Trading-Day-Ahead Forecast Errors of Intra-Day Realized Volatility
Degiannakis, Stavros Antonios - 2018
Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the...
Persistent link: https://www.econbiz.de/10012910114
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Deep Signature models for Financial Equity Time Series prediction
Alonso, Miquel Noguer i; Agrawal, Himanshu; Srivastava, … - 2022
We explore in this paper the use of deep signature models to predict equity financial time series returns. First, we use signature transformations to model the underlying shape of the input equity returns; further assuming the underlying shape remains the same, we predict future values based on...
Persistent link: https://www.econbiz.de/10013289206
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The Shape of Performance Curve in Financial Time Series
Alonso, Miquel Noguer i; Srivastava, Sonam - 2021
in some specific domains.We discuss some of the recent discoveries in the mathematical theory of machine learning that … reduce the gap between theory and practice. We conduct experiments in the financial time series domain using deep neural … financial time series domain. This is consistent with the finance practitioner's theory that backtesting ( training data …
Persistent link: https://www.econbiz.de/10013310497
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Generative Models for Time Series in Finance
Noguer i Alonso, Miquel; Pacheco Aznar, David - 2023
A generative model is a statistical model of the joint probability distribution. We built a generative model for univariate time series in finance using a Variational Autoencoder (VAE) neural network architecture. We test the model in SP500 and the Heston Model widely used for option pricing and...
Persistent link: https://www.econbiz.de/10014255820
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Combining robust dynamic neural networks with traditional technical indicators for generating mechanic trading signals
Giribone, Pier Giuseppe; Ligato, Simone; Penone, Francesco - In: International journal of financial engineering 5 (2018) 4, pp. 1-44
Persistent link: https://www.econbiz.de/10012028821
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Evaluating the effectiveness of modern forecasting models in predicting commodity futures prices in volatile economic times
Vancsura, László; Tatay, Tibor; Bareith, Tibor - In: Risks : open access journal 11 (2023) 2, pp. 1-16
The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21-...
Persistent link: https://www.econbiz.de/10014233184
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Forecasting exchange rates with neural networks : time variation, nonstationarity, and causal models
Reikard, Gordon - In: International economic journal 37 (2023) 2, pp. 202-219
Persistent link: https://www.econbiz.de/10014295003
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Time-Series and Cross-Sectional Stock Return Forecasting : New Machine Learning Methods
Rapach, David - 2019
This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series context. The methods use the elastic net to refine the simple combination return forecast from Rapach et al. (2010). In a time-series application focused...
Persistent link: https://www.econbiz.de/10012865775