Articles de journaux
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Manuel Moreno AND Alfonso Novales AND Federico Platania Long-term swings and seasonality in energy markets Article de journal European Journal of Operational Research, 279 (3), p. 1011-1023, 2019. Résumé | Liens | BibTeX @article{Platania2019b,
title = {Long-term swings and seasonality in energy markets},
author = {Manuel Moreno AND Alfonso Novales AND Federico Platania},
url = {https://doi.org/10.1016/j.ejor.2019.05.042},
doi = {10.1016/j.ejor.2019.05.042},
year = {2019},
date = {2019-06-07},
journal = {European Journal of Operational Research},
volume = {279},
number = {3},
pages = {1011-1023},
abstract = {This paper introduces a two-factor continuous-time model for commodity pricing under the assumption that prices revert to a stochastic mean level, which shows smooth, periodic fluctuations over long periods of time. We represent the mean reversion price by a Fourier series with a stochastic component. We also consider a seasonal component in the price level, an essential characteristic of many commodity prices, which we represent again by a Fourier series. We obtain analytical pricing expressions for futures contracts. Using futures price data on Natural Gas, we provide evidence on the presence of long-term fluctuations and show how to estimate the long-term component simultaneously with a seasonal component using the Kalman filter. We analyse the in-sample and out-of-sample empirical performance of our pricing model with and without a seasonal component and compare it with Schwartz and Smith (2000) model. Our findings show the in-sample and out-of-sample superiority of our model with seasonal fluctuations, thereby providing a simple and powerful tool for portfolio management, risk management, and derivative pricing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a two-factor continuous-time model for commodity pricing under the assumption that prices revert to a stochastic mean level, which shows smooth, periodic fluctuations over long periods of time. We represent the mean reversion price by a Fourier series with a stochastic component. We also consider a seasonal component in the price level, an essential characteristic of many commodity prices, which we represent again by a Fourier series. We obtain analytical pricing expressions for futures contracts. Using futures price data on Natural Gas, we provide evidence on the presence of long-term fluctuations and show how to estimate the long-term component simultaneously with a seasonal component using the Kalman filter. We analyse the in-sample and out-of-sample empirical performance of our pricing model with and without a seasonal component and compare it with Schwartz and Smith (2000) model. Our findings show the in-sample and out-of-sample superiority of our model with seasonal fluctuations, thereby providing a simple and powerful tool for portfolio management, risk management, and derivative pricing. |
Gabriela Contreras AND Federico Platania Economic and policy uncertainty in climate change mitigation: The London Smart City case scenario Article de journal Technological Forecasting & Social Change, 142 , p. 384-393, 2019. Résumé | Liens | BibTeX @article{Platania2018c,
title = {Economic and policy uncertainty in climate change mitigation: The London Smart City case scenario},
author = {Gabriela Contreras AND Federico Platania},
url = {https://doi.org/10.1016/j.techfore.2018.07.018},
doi = {10.1016/j.techfore.2018.07.018},
year = {2019},
date = {2019-05-01},
journal = {Technological Forecasting & Social Change},
volume = {142},
pages = {384-393},
abstract = {Despite the overwhelming consensus within the scientific community concerning the causes and effects of climate change, decision-making processes often do not point out in the same direction. In order to effectively and satisfactorily tackle climate change, a legally and politically binding long-term policy architecture is needed. In practice, however, central governments and international policymakers have been unable to provide a successful policy architecture. Yet, city-level initiatives within the Smart City framework are a promising way to tackle climate change. An example of such a Smart City framework is the London Environment Strategy (LES). In this paper, we propose a zero mean reverting model for greenhouse gas emissions to quantitatively analyze its consistency with the 2050 Zero Carbon objectives. We consider different policy scenarios proposed in the LES and the forward-looking policy uncertainty embedded in different economic sectors, primarily domestic, industrial and commercial and transport. We find that, on average, only transport improves the historical greenhouse gas emissions trend, and most of this reduction comes from Smart Mobility and/or Smart Regulation programs focusing on the environment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Despite the overwhelming consensus within the scientific community concerning the causes and effects of climate change, decision-making processes often do not point out in the same direction. In order to effectively and satisfactorily tackle climate change, a legally and politically binding long-term policy architecture is needed. In practice, however, central governments and international policymakers have been unable to provide a successful policy architecture. Yet, city-level initiatives within the Smart City framework are a promising way to tackle climate change. An example of such a Smart City framework is the London Environment Strategy (LES). In this paper, we propose a zero mean reverting model for greenhouse gas emissions to quantitatively analyze its consistency with the 2050 Zero Carbon objectives. We consider different policy scenarios proposed in the LES and the forward-looking policy uncertainty embedded in different economic sectors, primarily domestic, industrial and commercial and transport. We find that, on average, only transport improves the historical greenhouse gas emissions trend, and most of this reduction comes from Smart Mobility and/or Smart Regulation programs focusing on the environment. |
Federico Platania AND Pedro Serrano AND Mikel Tapia Modelling the shape of the limit order book Article de journal Quantitative Finance, 18 (9), p. 1575-1597, 2018. Résumé | Liens | BibTeX @article{Tapia2018,
title = {Modelling the shape of the limit order book},
author = {Federico Platania AND Pedro Serrano AND Mikel Tapia},
doi = {10.1080/14697688.2018.1433312},
year = {2018},
date = {2018-02-23},
journal = {Quantitative Finance},
volume = {18},
number = {9},
pages = {1575-1597},
abstract = {This article develops a parsimonious way to use the shape of the limit order book to produce an estimate of the asset price. The posited model captures and describes the evolution of the distribution of limit orders on the bid and ask sides of the LOB during the trading session and provides estimates of the execution asset price over time. The performance of the model is evaluated against some existing standards from the market microstructure literature during the trading session. Empirical evidence on listed companies confirm a strong contribution of our methodology to the innovation in asset prices, according to the information share coefficients. We also document a significant improvement relative to the Hasbrouck [J. Finance, 1991, 46, 179–207] model when our model estimates are included as regressors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article develops a parsimonious way to use the shape of the limit order book to produce an estimate of the asset price. The posited model captures and describes the evolution of the distribution of limit orders on the bid and ask sides of the LOB during the trading session and provides estimates of the execution asset price over time. The performance of the model is evaluated against some existing standards from the market microstructure literature during the trading session. Empirical evidence on listed companies confirm a strong contribution of our methodology to the innovation in asset prices, according to the information share coefficients. We also document a significant improvement relative to the Hasbrouck [J. Finance, 1991, 46, 179–207] model when our model estimates are included as regressors. |
Manuel Moreno AND Alfonso Novales AND Federico Platania A term structure model under cyclical fluctuations in interest rates Article de journal Economic Modelling, 72 , p. 140-150, 2018. Résumé | Liens | BibTeX @article{Platania2018b,
title = {A term structure model under cyclical fluctuations in interest rates},
author = {Manuel Moreno AND Alfonso Novales AND Federico Platania},
doi = { https://doi.org/10.1016/j.econmod.2018.01.015},
year = {2018},
date = {2018-02-07},
journal = {Economic Modelling},
volume = {72},
pages = {140-150},
abstract = {We propose a flexible yet tractable model of the term structure of interest rates (TSIR). Term structure models attempt to explain how interest rates depend on their maturities at a given point in time, characterizing the relationship between short-term and long-term rates. Our model can reproduce and fit a variety of TSIR shapes by capturing cyclical fluctuations of interest rates, different monetary policy reactions as witnessed pre- and post-crisis as well as the effect of the business cycle or exogenous shocks. Our modelling approach also provides a characterization of long-term fluctuations in the mean level of interest rates unveiling the effects of monetary policy interventions in interest rates. Furthermore, using daily US data, we compare the empirical ability of our model to both fit and forecast the TSIR under different economic scenarios. We show that our model improves pricing and risk management by fitting and predicting interest rates more accurately and precisely than do existing TSIR models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We propose a flexible yet tractable model of the term structure of interest rates (TSIR). Term structure models attempt to explain how interest rates depend on their maturities at a given point in time, characterizing the relationship between short-term and long-term rates. Our model can reproduce and fit a variety of TSIR shapes by capturing cyclical fluctuations of interest rates, different monetary policy reactions as witnessed pre- and post-crisis as well as the effect of the business cycle or exogenous shocks. Our modelling approach also provides a characterization of long-term fluctuations in the mean level of interest rates unveiling the effects of monetary policy interventions in interest rates. Furthermore, using daily US data, we compare the empirical ability of our model to both fit and forecast the TSIR under different economic scenarios. We show that our model improves pricing and risk management by fitting and predicting interest rates more accurately and precisely than do existing TSIR models. |
Manuel Moreno AND Federico Platania A cyclical square-root model for the term structure of interest rates Article de journal European Journal of Operational Research, 241 (1), p. 109–121, 2015. Résumé | Liens | BibTeX @article{Platania2014,
title = {A cyclical square-root model for the term structure of interest rates},
author = {Manuel Moreno AND Federico Platania},
url = {https://doi.org/10.1016/j.ejor.2014.08.010},
doi = {10.1016/j.ejor.2014.08.010},
year = {2015},
date = {2015-02-16},
journal = {European Journal of Operational Research},
volume = {241},
number = {1},
pages = {109–121},
abstract = {This paper presents a cyclical square-root model for the term structure of interest rates assuming that the spot rate converges to a certain time-dependent long-term level. This model incorporates the fact that the interest rate volatility depends on the interest rate level and specifies the mean reversion level and the interest rate volatility using harmonic oscillators. In this way, we incorporate a good deal of flexibility and provide a high analytical tractability. Under these assumptions, we compute closed-form expressions for the values of different fixed income and interest rate derivatives. Finally, we analyze the empirical performance of the cyclical model versus that proposed in Cox et al. (1985) and show that it outperforms this benchmark, providing a better fitting to market data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents a cyclical square-root model for the term structure of interest rates assuming that the spot rate converges to a certain time-dependent long-term level. This model incorporates the fact that the interest rate volatility depends on the interest rate level and specifies the mean reversion level and the interest rate volatility using harmonic oscillators. In this way, we incorporate a good deal of flexibility and provide a high analytical tractability. Under these assumptions, we compute closed-form expressions for the values of different fixed income and interest rate derivatives. Finally, we analyze the empirical performance of the cyclical model versus that proposed in Cox et al. (1985) and show that it outperforms this benchmark, providing a better fitting to market data. |
Conférences
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Federico Platania Long-term swings and seasonality in energy markets 5500 - 5599 Conférence 5th International Symposium on Environment and Energy Finance Issues (ISEFI-2017), Paris, 22-23 mai , 2017. BibTeX @conference{platania2017,
title = {Long-term swings and seasonality in energy markets},
author = {Federico Platania},
year = {2017},
date = {2017-05-15},
booktitle = {5th International Symposium on Environment and Energy Finance Issues (ISEFI-2017), Paris, 22-23 mai },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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Moreno M.; Novales A.; Platania, F. Long-term swings and and seasonality in energy markets 5500 - 5599 Conférence 4th Paris Financial Management Conferen, hosted by IPAG Business School., 2016. BibTeX @conference{Platania2016,
title = {Long-term swings and and seasonality in energy markets},
author = {Moreno M. and Novales A. and Platania, F. },
year = {2016},
date = {2016-12-12},
booktitle = {4th Paris Financial Management Conferen, hosted by IPAG Business School.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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