1,060 research outputs found
Some Essays on models in the Bond and Energy Markets
No full textThe term structure of interest rates plays a fundamental role as an indicator of economy and market trends, as well as a supporting tool for macroeconomic strategies, investment choices or hedging practices. Therefore, the availability of proper techniques to model and predict its dynamics is of crucial importance for players in the financial markets. Along this path, the dissertation initially examined the reliability of parametric and neural network models to fit and predict the term structure of interest rates in emerging markets, focusing on the Brazilian, Russian, Indian, Chines and South African (BRICS) bond markets. The focus on the BRICS is straightforward: the dynamics of their term structures make tricky the application of consolidated yield curve models. In this respect, BRICS yield curve act as stress testers. The study then examined how to apply the above cited models to energy derivatives, focusing the attention on the Natural Gas and Electricity futures, motivated by the existence of similarity. The research was carried out using ad hoc routines, such as the R package "DeRezende.Ferreira", developed by the candidate and now freely downloadable at the Comprehensive R Archive Network (CRAN) repository*, as well as by means of code written in MatLab 2021a - 2022a and Python (3.10.10) using the open-source Keras (2.4.3) library with TensorFlow (2.4.0) as backend. The dissertation consists of four chapters based on published and/or under submission materials. Chapter 1 is an excerpt of the paper • Castello, O.; Resta, M. Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques. Risks 2022
The work firstly offers a comprehensive analysis of the BRICS bond market and then investigates and compares the abilities of the parametric Five–Factor De Rezende–Ferreira model and Feed–Forward Neural Networks to fit the yield curves. Chapter 2 is again focused on the BRICS market but investigates a methodology to identify optimal time–varying parameters for parametric yield curve models. The work then investigates the ability of this method both for in–sample fitting and out–of–sample prediction. Various forecasting methods are examined: the Univariate Autoregressive process AR(1), the TBATS and the Autoregressive Integrated Moving Average (ARIMA) combined to Nonlinear Autoregressive Neural Networks (NAR–NN). Chapter 3 studies the term structure dynamics in the Natural Gas futures market. This chapter represents an extension of the paper • Castello, O., Resta, M. (2022). Modeling and Forecasting Natural Gas Futures Prices Dynamics: An Integrated Approach. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022.
After showing that the natural gas and bond markets share similar stylized facts, we exploit these findings to examine whether techniques conventionally employed on the bonds market can be effectively used also for accurate in–sample fitting and out–of–sample forecast. We worked at first in–sample and we compared the performance of three models: the Four–Factor Dynamic Nelson–Siegel–Svensson (4F-DNSS), the Five–Factor Dynamic De Rezende–Ferreira (5F–DRF) and the B–Spline. Then, we turned the attention on forecasting, and explored the effectiveness of a hybrid methodology relying on the joint use of 4F–DNSS, 5F–DRF and B–Splines with Nonlinear Autoregressive Neural Networks (NAR–NNs). Empirical study was carried on using the Dutch Title Transfer Facility (TTF) daily futures prices in the period from January 2011 to June 2022 which included also recent market turmoil to validate the overall effectiveness of the framework.
Chapter 4 analyzes the predictability of the electricity futures prices term structure with Artificial Neural Networks. Prices time series and futures curves are characterized by high volatility which is a direct consequence of an inelastic demand and of the non–storable nature of the underlying commodity. We analyzed the forecasting power of several neural network models, including Nonlinear Autoregressive (NAR–NNs), NAR with Exogenous Inputs (NARX–NNs), Long Short–Term Memory (LSTM–NNs) and Encoder–Decoder Long Short–Term Memory Neural Networks (ED–LSTM–NNs). We carried out an extensive study of the models predictive capabilities using both the univariate and multivariate setting. Additionally, we explored whether incorporating various exogenous components such as Carbon Emission Certificates (CO2) spot prices,
as well as Natural Gas and Coal futures prices can lead to improvements of the models performances. The data of the European Energy Exchange (EEX) power market were adopted to test the models. Chapter 4 concludes.
____________________________ * https://cran.r-project.org/web/packages/DeRezende.Ferreira/index.htm
A comparative study of Machine Learning methods for Power Futures Curves prediction
No full textAccurate electricity price forecasting is a critical issue for policy makers and market players, especially during periods of market turmoil. Nevertheless, the development of robust forecasting methodologies still represents a challenging issue due to high volatility and irregular cycles in price dynamics that characterize international power markets. This work studies a Machine Learning (ML) approach to predict the term structure of power futures prices. In detail, four ML models, i.e. Generalized Regression (GR-NNs), Nonlinear Autoregressive (NAR–NNs), NeuralProphet (NP-NN) and Long Short-Term Memory Neural Networks (LSTM-NN) are examined for the 1 and 5 days-ahead pointwise forecast. To investigate the models predictive performances, the study has been run using daily settlement prices of electricity futures contracts traded on the European Energy Exchange (EEX) in the period spanning from March 2017 to October 2023
A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling
No full textThis work studies the term structure dynamics in the natural gas futures market, focusing on the Dutch Title Transfer Facility (TTF) daily futures prices. At first, using the whole dataset, we compared the in-sample fitting performance of three models: the four-factor dynamic Nelson–Siegel–Svensson (4F-DNSS) model, the five-factor dynamic De Rezende–Ferreira (5F-DRF) model, and the B-spline model. Our findings suggest that B-spline is the method that achieves the best in-line fitting results. Then, we turned our attention to forecasting, using data from 20 January 2011 to 13 May 2022 as the training set and the remaining data, from 16 May to 13 June 2022, for day-ahead predictions. In this second part of the work we combined the above mentioned models (4F-DNSS, 5F-DRF and B-spline) with a Nonlinear Autoregressive Neural Network (NAR-NN), asking the NAR-NN to provide parameter tuning. All the models provided accurate out-of-sample prediction; nevertheless, based on extensive statistical tests, we conclude that, as in the previous case, B-spline (combined with an NAR-NN) ensured the best out-of-sample prediction
Univariate and multivariate forecasting of the electricity futures curve using Dynamic Recurrent Neural Networks
No full textIn recent years international power markets have witnessed high uncertainty and extraordinary volatility which, given the inherent complexity of the market, has made the Electricity Price Forecasting (EPF) process increasingly difficult. Therefore the development of a proper forecasting framework suitable for both stable and volatile periods has assumed an increasing importance for market players and policymakers in both strategic planning and risk management. At present, the majority of the studies on electricity price forecasting focused on the analysis of spot markets, neglecting the importance of derivative price modeling to mitigate the risks induced by market downturns and turmoil. Our study nests within this research stream and analyzes the potential of a set of state-of-the-art Machine Learning (ML) models for the prediction of the term structure of electricity futures prices. The objective is to define an ML-based framework capable of ensuring high predictive performance of the term structure during both stable and extremely turbulent conditions. In this regard we examined the predictive capabilities of a variety of Dynamic Recurrent Neural Networks (DRNNs) including: Nonlinear Autoregressive Neural Networks (NAR-NNs), NAR with Exogenous Inputs (NARX-NNs), Long Short-Term Memory (LSTM-NNs), Stacked Long Short-Term Memory (ST-LSTM-NNs), Bidirectional Long Short-Term Memory (BI-LSTM-NNs) and Encoder–Decoder Long Short-Term Memory Neural Networks (ED-LSTM-NNs). The models were applied to both low fluctuating and volatile sets of daily futures prices of the European Energy Exchange (EEX) for univariate as well as multivariate forecasting. Additionally, we compared this set of networks to baseline models commonly used in the EPF literature, including classical statistical and ML methods. Empirical results highlighted that DRNN models predictions are consistent with futures prices trends observed under different market regimes and outperform the competitors’ performance. Overall, main outcomes of the study may be summarized as follows: LSTM-based models seem to have the highest predictive power, with robust performance under various conditions. In detail the Multivariate BI-LSTM-NN performs better under quiet market conditions ensuring an accuracy level of 98.11 %, while the Univariate ED-LSTM-NN ensures superior predictive performance in presence of turmoil, achieving a 95.33 % accuracy
Optimal Time Varying Parameters in Yield Curve Modeling and Forecasting: A Simulation Study on BRICS Countries
No full textThe term structure of interest rates is a fundamental decision–making tool for various economic activities. Despite the huge number of contributions in the field, the development of a reliable framework for both fitting and forecasting under various market conditions (either stable or very volatile) still remains a topical issue. Motivated by this problem, this study introduces a methodology relying on optimal time–varying parameters for three and five factor models in the Nelson–Siegel class that can be employed for an effective in-sample fitting and out–of–sample forecasting of the term structure. In detail, for the in–sample fitting we discussed a two–step estimation procedure leading to optimal models parameters and evaluated the performances of this approach in terms of flexibility and fitting accuracy gains. For what it concerns the forecasting, we suggest an approach overcoming the well–known issue between the stability of factor models’ parameters and the optimal dynamic decay terms. To such aim, we use either autoregressive or machine learning techniques as local data generating processes based on the optimal parameters time series derived in the in–line fitting step. The so–obtained values are then employed to get day–ahead predictions of the yield curve. We assessed the proposed framework on daily spot rates of the BRICS (Brazil, Russia, India, China and South Africa) bond market. The experimental analysis illustrated that (i) time–varying parameters ensure a significant boost in the models fitting power and a more faithful representation of the yield curves dynamics; (ii) the proposed approach provides also stable and accurate predictions
Modeling and Forecasting Natural Gas Futures Prices Dynamics: An Integrated Approach
No full textWe explore and test the capabilities of B-Splines and Dynamic De Rezende-Ferreira five–factor model to replicate the main dynamics and stylized facts of futures curves in the Natural Gas Futures market. Furthermore, we discuss the joint use of these models with a Nonlinear Autoregressive Neural Network for parameters fine–tuning to
forecast futures curves. The simulation study highlighted the effectiveness of the proposed framework; empirical results show that the joint use of B–Splines and neural networks provides highest overall performances on the Natural Gas futures market
A Swap-Based Framework for Managing Energy Transition Risks
No full textThe sustainable energy transition represents a transformative shift in how energy is produced, distributed, and consumed-moving away from fossil fuels toward renewable energy sources. This shift is essential for achieving global decarbonization goals but presents significant challenges for businesses. As firms are compelled to adapt to this evolving regulatory and economic landscape, they become increasingly exposed to transition-related financial risks and uncertainties. This exposure is particularly pronounced for small and medium-sized enterprises (SMEs), which often lack the financial and strategic capacity to manage such transformation effectively, placing their valuations and long-term competitiveness at risk. To facilitate SMEs' transition process, we propose a novel financial tool, termed Transition Risk Impact Swap (TRIS), that leverages the conceptual framework of equity swaps, the use of conventional and sustainability-linked indices as proxies for corporate greenness, and flexible customisation, to enable the hedging of climate transition costs and uncertainty based on SMEs' transition performance. Proposed under both floating-for-floating and fixed-for-floating structures, TRIS allows for positive upfront payments and mitigating basis risk through linkage to observable market indicators, while also offering the flexibility to achieve a zero initial value through appropriately defined spreads, enhancing its marketability. The economic viability and risk-mitigating potential of the TRIS are quantitatively assessed through Historical Bootstrap and Monte Carlo simulations using European market data from STOXX and MSCI index series. The proposed financial instrument offers a scalable mechanism to strategically support firms in initiating or accelerating their climate transition by enhancing financial flexibility, reducing reliance on traditional funding channels, and mitigating the risk of long-term marginalisation or market exclusion
Mazurkevych Oleksandr Romanovych
No full textУ статті відображено життєвий і творчий шлях Мазуркевича Олександра Романовича - учителя, педагога, фахівця з методики літератури. Автором статті визначено, що науково-педагогічна спадщина вченого багатогранна, вагома й потребує глибокого неупередженого наукового дослідження та оцінки.The article deals with the life and creative way of Oleksandr Romanovych Mazurkevych - a teacher, a specialist in the methodology of literature. The author of the article determines the scientific and pedagogical heritage of the scientist is multifaceted, significant and requires a deep and impartial scientific research and evaluation
On history of ethnographic research of the Crimea: Oleksandr Muzychenko
No full textUsing documents of Chernihiv oblast State Archives Department in Nizhyn and Myzychenko’s poorly investigated activity in the field of the Crimean studies the author of the article opens unknown pages of outstanding Ukrainian pedagogue’s biography which relate to studying ethnography of the Crimean Bulgarians. The author reconstituted all repertoire of Oleksandr Muzychenko’s ethnographic bibliography that relates to studying folklore of the Crimean Bulgarians
- …
