arXiv subjects

Antonia Arsova

Publications and source records attributed to Antonia Arsova.

3 recordsLinked to original sources

arXiv2023

Modeling Volatility and Dependence of European Carbon and Energy Prices

We study the prices of European Emission Allowances (EUA), whereby we analyze their uncertainty and dependencies on related energy prices (natural gas, coal, and oil). We propose a probabilistic multivariate conditional time series model with a VECM-Copula-GARCH structure which exploits key characteristics of the data. Data are normalized with respect to inflation and carbon emissions to allow for proper cross-series evaluation. The forecasting performance is evaluated in an extensive rolling-window forecasting study, covering eight years out-of-sample. We discuss our findings for both levels- and log-transformed data, focusing on time-varying correlations, and in view of the Russian invasion of Ukraine.

q-fin.ST↗

arXiv2023

Forecasting Natural Gas Prices with Spatio-Temporal Copula-based Time Series Models

Commodity price time series possess interesting features, such as heavy-tailedness, skewness, heteroskedasticity, and non-linear dependence structures. These features pose challenges for modeling and forecasting. In this work, we explore how spatio-temporal copula-based time series models can be effectively employed for these purposes. We focus on price series for fossil fuels and carbon emissions. Further, we illustrate how the t-copula may be used in conditional heteroskedasticity modeling. The possible emergence of non-elliptical probabilistic forecasts in this context is examined and visualized. The problem of finding an appropriate point forecast given a non-elliptical probabilistic forecast is discussed. We propose a solution where the forecast is augmented with an artificial neural network (ANN). The ANN predicts the best (in MSE sense) quantile to use as point forecast. In a forecasting study, we find that the copula-based models are competitive.

stat.AP↗

arXiv2020

On cointegration for modeling and forecasting wind power production

This study evaluates the performance of cointegrated vector autoregressive (VAR) models for very short- and short-term wind power forecasting. Preliminary results for a German data set comprising six wind power production time series indicate that taking into account potential cointegrating relations between the individual series can improve forecasts at short-term time horizons.

stat.AP↗