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on Forecasting |
| By: | Sonnleitner, Benedikt; Stapf, Jelena; Wulff, Kai |
| Abstract: | Among the most important tasks of central banks is to ensure the availability of cash to credit institutions and retailers. Forecasting the demand for cash on a granular level is crucial in the process to keep logistics costs low, while being resilient to demand or supply shocks. Whereas to date, cash forecasts with central banks mostly comprise structural models to define banknote production for the coming years, our contribution is to combine features of macro level forecasting with more granular and short term regional forecasts methods. We show in an inventory simulation, that elaborate forecasting methods on granular level can substantially improve inventory performance for this use-case. To guide the implementation of a forecasting process at the Bundesbank, we benchmark statistical and machine learning methods on demand and supply of cash, using anonymized data on transactions of six regional branches of Deutsche Bundesbank. We use a pseudo out of sample predictive performance framework to evaluate the accuracy of our forecasts and perform an inventory cost simulation. We find that (i) DeepAR outperforms the other benchmarks substantially on all data sets. (ii) ETS, ARIMA, and DeepAR clearly outperform the naive benchmark in terms of accuracy across all data sets, and inventory performance. |
| Keywords: | Global learning, Forecasting, Machine Learning |
| JEL: | E31 G21 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:305276 |
| By: | Alex Li |
| Abstract: | Predicting volatility in financial markets, including stocks, index ETFs, foreign exchange, and cryptocurrencies, remains a challenging task due to the inherent complexity and non-linear dynamics of these time series. In this study, I apply TimeMixer, a state-of-the-art time series forecasting model, to predict the volatility of global financial assets. TimeMixer utilizes a multiscale-mixing approach that effectively captures both short-term and long-term temporal patterns by analyzing data across different scales. My empirical results reveal that while TimeMixer performs exceptionally well in short-term volatility forecasting, its accuracy diminishes for longer-term predictions, particularly in highly volatile markets. These findings highlight TimeMixer's strength in capturing short-term volatility, making it highly suitable for practical applications in financial risk management, where precise short-term forecasts are critical. However, the model's limitations in long-term forecasting point to potential areas for further refinement. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.09062 |
| By: | Heinisch, Katja |
| Abstract: | The European Commission's growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts' quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries. |
| Keywords: | consensus forecasts, data revision, forecast evaluation, forecast horizon, forecasting, nowcasting, professional forecasters |
| JEL: | C32 C52 C53 E37 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iwhdps:304456 |
| By: | Rotem Zelingher |
| Abstract: | Ensuring food security is a critical global challenge, particularly for low-income countries where food prices impact the access to nutritious food. The volatility of global agricultural commodity (AC) prices exacerbates food insecurity, with international trade restrictions and market disruptions further complicating the situation. Despite online platforms for monitoring food prices, there is a significant gap in providing detailed explanations and forecasts accessible to non-specialists. To address this, we propose the Agricultural Commodity Analysis and Forecasts (AGRICAF) methodology, integrating explainable machine learning (XML) and econometric techniques to analyse and forecast global agricultural commodity prices up to one year ahead, dynamically adapting to different forecast horizons. This innovative integration allows us to model complex interactions and dynamics while providing clear, interpretable results. This paper demonstrates how AGRICAF can be used, applying it to three major agricultural commodities - maize, soybean, and wheat - and explaining how different factors impact prices across various months and forecast horizons. By facilitating access to accurate and interpretable medium-term forecasts of AC prices, AGRICAF can contribute to developing a fair and sustainable food system. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.20363 |