Articles de journaux |
Marie Lambert AND Federico Platania The macroeconomic drivers in hedge fund beta management Article de journal Economic Modelling, 91 , p. 65-80, 2020. Résumé | Liens | BibTeX @article{PlataniaEM2020,
title = {The macroeconomic drivers in hedge fund beta management},
author = {Marie Lambert AND Federico Platania},
doi = {https://doi.org/10.1016/j.econmod.2020.04.016},
year = {2020},
date = {2020-09-01},
journal = {Economic Modelling},
volume = {91},
pages = {65-80},
abstract = {We investigate how macroeconomic indicators alter the dynamic risk exposure of different hedge fund style strategies. We implement a multifactor model to estimate the unobservable time-varying risk exposure conditional on macroeconomic information and a VAR to measure the impact of macroeconomic predictors on different time horizons. Using monthly returns on a cross-section of 10 different style indices from February 1997 to August 2019, we find that, on average, macroeconomic indicators explain approximately 30%, 55%, and 75% of the variability of betas at 1-, 6-, and 36-month horizons, respectively. Although macroeconomic predictors play a critical role at every horizon, at 1 month, the dominating effect comes from idiosyncratic shocks, which indicates that in the short run, hedge fund managers rely mostly on their own reallocation signals. Moreover, consistent with the fundamental drivers of the smart beta factors, we find that the interest rate level and GDP growth similarly impact hedge fund exposures across styles.},
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We investigate how macroeconomic indicators alter the dynamic risk exposure of different hedge fund style strategies. We implement a multifactor model to estimate the unobservable time-varying risk exposure conditional on macroeconomic information and a VAR to measure the impact of macroeconomic predictors on different time horizons. Using monthly returns on a cross-section of 10 different style indices from February 1997 to August 2019, we find that, on average, macroeconomic indicators explain approximately 30%, 55%, and 75% of the variability of betas at 1-, 6-, and 36-month horizons, respectively. Although macroeconomic predictors play a critical role at every horizon, at 1 month, the dominating effect comes from idiosyncratic shocks, which indicates that in the short run, hedge fund managers rely mostly on their own reallocation signals. Moreover, consistent with the fundamental drivers of the smart beta factors, we find that the interest rate level and GDP growth similarly impact hedge fund exposures across styles. |
Francesco Paolo Appio AND Daniele Leone AND Federico Platania AND Francesco Schiavone Why are rewards not delivered on time in rewards-based crowdfunding campaigns? An empirical exploration Article de journal Technological Forecasting & Social Change, 157 , 2020. Résumé | Liens | BibTeX @article{FPlat2020,
title = {Why are rewards not delivered on time in rewards-based crowdfunding campaigns? An empirical exploration},
author = {Francesco Paolo Appio AND Daniele Leone AND Federico Platania AND Francesco Schiavone},
doi = {https://doi.org/10.1016/j.techfore.2020.120069},
year = {2020},
date = {2020-08-01},
journal = {Technological Forecasting & Social Change},
volume = {157},
abstract = {Crowdfunding is an alternative way to seek capital for new projects. However, it can also be a danger for entrepreneurs facing the post-campaign phase delays in the delivery of the promised rewards. Crowdfunding campaigns require months of preparation and meeting delivery deadlines seems to be a real problem. With this study, we try to explain why this is the case. By drawing on a dataset of 1,567 successfully funded new technological projects in the period 2009–2017, and by means of a text mining routine, this study presents a comprehensive description of the causes of delay in rewards delivery in crowdfunding campaigns. Our findings reveal that perceived incompetence, fraud, and funding cancellation, are the main causes of delay in rewards delivery. Furthermore, controlling for the presence of serial project creators, project appeal (% of new backers), project complexity (number of FAQs, days of funding, number of updates, number of comments), project financial size (% funded, amount raised, financial goal, average pledge per backer), as well as time, mitigate but does not eliminate the problem. Results provide a thorough contribution and implications for both the agents of the crowdfunding industry (e.g., creators, backers), platform managers, and the academic community.},
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Crowdfunding is an alternative way to seek capital for new projects. However, it can also be a danger for entrepreneurs facing the post-campaign phase delays in the delivery of the promised rewards. Crowdfunding campaigns require months of preparation and meeting delivery deadlines seems to be a real problem. With this study, we try to explain why this is the case. By drawing on a dataset of 1,567 successfully funded new technological projects in the period 2009–2017, and by means of a text mining routine, this study presents a comprehensive description of the causes of delay in rewards delivery in crowdfunding campaigns. Our findings reveal that perceived incompetence, fraud, and funding cancellation, are the main causes of delay in rewards delivery. Furthermore, controlling for the presence of serial project creators, project appeal (% of new backers), project complexity (number of FAQs, days of funding, number of updates, number of comments), project financial size (% funded, amount raised, financial goal, average pledge per backer), as well as time, mitigate but does not eliminate the problem. Results provide a thorough contribution and implications for both the agents of the crowdfunding industry (e.g., creators, backers), platform managers, and the academic community. |
Manuel Moreno; Alfonso Novales; 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.},
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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.},
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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.},
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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.},
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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.},
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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 |
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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