Modeling Volatility and Dependence of European Carbon and Energy Prices
- URL: http://arxiv.org/abs/2208.14311v1
- Date: Tue, 30 Aug 2022 14:50:25 GMT
- Title: Modeling Volatility and Dependence of European Carbon and Energy Prices
- Authors: Jonathan Berrisch, Sven Pappert, Florian Ziel, Antonia Arsova
- Abstract summary: We study the prices of European Emission Allowances (EUA)
We propose a probabilistic conditional time series model that exploits key characteristics of the data.
We discuss our findings focusing on volatility spillovers and time-varying correlations, also in view of the Russian invasion of Ukraine.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study the prices of European Emission Allowances (EUA), whereby we analyze
their uncertainty and dependencies on related energy markets. We propose a
probabilistic multivariate conditional time series model that exploits key
characteristics of the data. The forecasting performance of the proposed model
and various competing models is evaluated in an extensive rolling window
forecasting study, covering almost two years out-of-sample. Thereby, we
forecast 30-steps ahead. The accuracy of the multivariate probabilistic
forecasts is assessed by the energy score. We discuss our findings focusing on
volatility spillovers and time-varying correlations, also in view of the
Russian invasion of Ukraine.
Related papers
- Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks [1.03590082373586]
We introduce an ensemble model to capture Brent oil price volatility and enhance the multi-step prediction.
Our methodology employs a two-pronged approach. First, we assess popular deep-learning models and the impact of various external factors on forecasting accuracy.
Our approach generates accurate forecasts by employing ensemble techniques across multiple forecasting scenarios using three BI-GRU networks.
arXiv Detail & Related papers (2024-07-15T22:21:17Z) - Analyzing Currency Fluctuations: A Comparative Study of GARCH, EWMA, and
IV Models for GBP/USD and EUR/GBP Pairs [0.0]
We examine the fluctuation in the value of the Great Britain Pound (GBP)
We apply various mathematical models to assess their effectiveness in predicting the 20-day variation in the pairs' daily returns.
Our experiments reveal that for the GBP/USD pair, the most accurate volatility forecasts stem from the utilization of GARCH models.
arXiv Detail & Related papers (2024-02-12T06:29:57Z) - A probabilistic forecast methodology for volatile electricity prices in
the Australian National Electricity Market [0.36832029288386137]
The South Australia region of the Australian National Electricity Market displays some of the highest levels of price volatility observed in modern electricity markets.
This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps.
arXiv Detail & Related papers (2023-11-13T12:33:33Z) - Probabilistic Forecasting of Day-Ahead Electricity Prices and their
Volatility with LSTMs [0.0]
We present a Long Short-Term Memory (LSTM) model for the German-Luxembourg day-ahead electricity prices.
The recurrent structure of the LSTM allows the model to adapt to trends, while the joint prediction of both mean and standard deviation enables a probabilistic prediction.
Using a physics-inspired approach - superstatistics - to derive an explanation for the statistics of prices, we show that the LSTM model faithfully reproduces both prices and their volatility.
arXiv Detail & Related papers (2023-10-05T06:47:28Z) - Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch.
In this paper, we collected large-scale load datasets and released a new renewable energy dataset.
We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
arXiv Detail & Related papers (2023-07-14T06:50:02Z) - The Capacity and Robustness Trade-off: Revisiting the Channel
Independent Strategy for Multivariate Time Series Forecasting [50.48888534815361]
We show that models trained with the Channel Independent (CI) strategy outperform those trained with the Channel Dependent (CD) strategy.
Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series.
We propose a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy.
arXiv Detail & Related papers (2023-04-11T13:15:33Z) - Back2Future: Leveraging Backfill Dynamics for Improving Real-time
Predictions in Future [73.03458424369657]
In real-time forecasting in public health, data collection is a non-trivial and demanding task.
'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature.
We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
arXiv Detail & Related papers (2021-06-08T14:48:20Z) - When in Doubt: Neural Non-Parametric Uncertainty Quantification for
Epidemic Forecasting [70.54920804222031]
Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions.
Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations.
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
arXiv Detail & Related papers (2021-06-07T18:31:47Z) - Learning Interpretable Deep State Space Model for Probabilistic Time
Series Forecasting [98.57851612518758]
Probabilistic time series forecasting involves estimating the distribution of future based on its history.
We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks.
We show in experiments that our model produces accurate and sharp probabilistic forecasts.
arXiv Detail & Related papers (2021-01-31T06:49:33Z) - A generative adversarial network approach to (ensemble) weather
prediction [91.3755431537592]
We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe.
The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
arXiv Detail & Related papers (2020-06-13T20:53:17Z) - Ensemble Forecasting for Intraday Electricity Prices: Simulating
Trajectories [0.0]
Recent studies have shown that the hourly German Intraday Continuous Market is weak-form efficient.
A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window.
The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets, especially in Europe.
arXiv Detail & Related papers (2020-05-04T10:21:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.