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Forecasting the Real Price of Oil Using Online Search Data

Fantazzini, Dean - 2014

New models to forecast the real price of oil on the basis of macroeconomic indicators and Google search data are proposed. A large-scale out-of-sample forecasting analysis comparing the different models is performed. It is found that models including both Google data and macroeconomic aggregates...

Persistent link: https://www.econbiz.de/10013055642

Forecasting Oil Prices With Penalized Regressions, Variance Risk Premia and Google Data

Lycheva, Maria; Mironenkov, Alexey; Kurbatskii, Alexey; … - 2023

This paper investigates whether augmenting models with the variance risk premium (VRP) and Google search data improves the quality of the forecasts for real oil prices. We considered a time sample of monthly data from 2007 to 2019 that includes several episodes of high volatility in the oil...

Persistent link: https://www.econbiz.de/10014349277

Long run returns predictability and volatility with moving averages

Chang, Chia-Lin; Ilomäki, Jukka; Laurila, Hannu; … - In: Risks : open access journal 6 (2018) 4, pp. 1-18

This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The...

Persistent link: https://www.econbiz.de/10011906234

On the economic determinants of optimal stock-bond portfolios : international evidence

Conrad, Christian; Stürmer, Karin - 2017 - This version: May 31, 2017

Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple … new DCC-MIDAS model, we construct stock-bond hedge portfolios and show that these portfolios outperform various benchmark …

Persistent link: https://www.econbiz.de/10011745369

Modelling Foreign Exchange Realized Volatility Using High Frequency Data : Long Memory Versus Structural Breaks

Maatoug, Abderazak Ben - 2019

In this study, we model realized volatility constructed from intraday high-frequency data. We explore the possibility of confusing long memory and structural breaks in the realized volatility of the following spot exchange rates: EUR/USD, EUR/JPY, EUR/CHF, EUR/GBP, and EUR/AUD. The results show...

Persistent link: https://www.econbiz.de/10012900291

Augmented Real-Time GARCH : A Joint Model for Returns, Volatility and Volatility of Volatility

Ding, Yashuang (Dexter) - 2021

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...

Persistent link: https://www.econbiz.de/10013234440

Asymptotic Inference about Predictive Accuracy Using High Frequency Data

Li, Jia - 2013

This paper provides a general framework that enables many existing inference methods for predictive accuracy to be used in applications that involve forecasts of latent target variables. Such applications include the forecasting of volatility, correlation, beta, quadratic variation, jump...

Persistent link: https://www.econbiz.de/10013079416

Package CovRegpy : Regularised Covariance Regression and Forecasting in Python

van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023

CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py …. These seven scripts contain the Dynamic Conditional Correlation (DCC) framework, Instantaneous Frequency Forecasting (IFF … Decomposition (EMD) and the development of the AdvEMDpy package. The CovRegpy_DCC.py, CovRegpy_RCR.py, and CovRegpy_RPP.py scripts …

Persistent link: https://www.econbiz.de/10014253907

A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

Mehtab, Sidra; Sen, Jaydip - 2020

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy...

Persistent link: https://www.econbiz.de/10014094821

Forecasting Half-Hourly Electricity Prices using a Mixed-Frequency Structural VAR Framework

Kapoor, Gaurav; Wichitaksorn, Nuttanan; Li, Mengheng; … - 2023

Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this...

Persistent link: https://www.econbiz.de/10014354157

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