Francesco Lisi - Profile on Academia.edu (original) (raw)

Papers by Francesco Lisi

Combining Forecasts for Nonlinear Testing

Forecasting financial time series with dynamic skewness and kurtosis

S Co 2007, Dec 6, 2007

SSRN Electronic Journal, 2000

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch ge... more Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.

Contributions to Statistics, 2013

The use of computational methods in statistics to face complex problems and highly dimensional da... more The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This volume presents the revised version of a selection of the papers given at S.Co. 2011, the 7th Conference on Statistical Computation and Complex Systems, held in Padua, Italy, September 19-21, 2011. The S.Co. conference is a forum for the discussion of new developments and applications of statistical methods and computational techniques for complex and high-dimensional datasets. Although the topics covered in this volume are diverse, the same themes recur, as research is mostly fueled by the need to analyse complicated data sets, for which traditional methods do not provide viable solutions. Among the topics presented we have estimation of traffic matrices in a communications network, in the presence of long-range dependence; nonparametric mixed-effects models for epidemiology; advanced methods for neuroimaging; efficient computations and inference in environmental studies; hierarchical and nonparametric Bayesian methods with applications in genomic studies; Markov switching models to explain regime changes in the evolution of realized volatility for financial returns; joint modelling of financial returns and multiple daily realized measures; classification of multivariate linear-circular data, with applications to marine monitoring networks; forecasting of electricity supply functions, using principal component analysis and reduced rank regression; clustering based on nonparametric density estimation; surface estimation and spatial smoothing, with applications to the estimation of the blood-flow velocity field. Whilst not exhaustive, this list should give a feel of the range of issues discussed at the conference. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods. v vi Preface We wish to thank all contributors who made this volume possible. Finally, thanks must go to the reviewers, who responded rapidly when put under pressure and helped improve the papers with their valuable comments and suggestions.

Predictive dimension: an alternative definition to embedding dimension

COMPSTAT, 2000

Abstract. In this paper we propose an alternative definition to the embedding dimension that we c... more Abstract. In this paper we propose an alternative definition to the embedding dimension that we call predictive dimension. This dimension does not refer to the number of delayed variables needed to characterize the system but to the best predictions that can be ...

Statistical Methods and Applications, 2010

Distributional theory for Quasi-Maximum Likelihood estimators in long memory conditional heterosk... more Distributional theory for Quasi-Maximum Likelihood estimators in long memory conditional heteroskedastic models is not formally defined, even asympotically. Because of that, this paper analyses the performance of the Likelihood Ratio and the Lagrange Multiplier misspecification tests for Periodic Long Memory GARCH models. The real size and power of these tests are studied by means of Monte Carlo simulations with respect to the class of Generalized Long Memory GARCH models. An application to the U SD/JP Y exchange rate is also provided.

Journal of the Italian Statistical Society, 1996

Singular spectrum analysis has been proposed in the field of nonlinear dynamical systems as filte... more Singular spectrum analysis has been proposed in the field of nonlinear dynamical systems as filtering method. In this paper a criterion to choose the number of components which leads to the best filtering is proposed. The selection is made by minimizing the prediction elTor.

Quantitative Finance, 2012

In the mutual funds industry the rating process is very important, and Morningstar is surely the ... more In the mutual funds industry the rating process is very important, and Morningstar is surely the most influential international rating agency. In this work we consider the problem of evaluating if the risk component is adequately accounted for in the Morningstar rating. To face this problem we compare the ratings produced giving different weights to the risk component. The focus of the analysis is on testing the hypothesis that two similar rating procedures with different risk parameters (or, in statistical terms, two raters) are equivalent. To that end, first the notion of β−equivalence is introduced and then a Monte Carlo test for the hypothesis of β−equivalence is described. Finally, to answer the question on the role of risk in the Morningstar rating, we analyze 1763 monthly return time series of US mutual funds. Results show that the current Morningstar classification, based on a risk-adjusted measure, only marginally accounts for risk and that if we want that risk really matters, the risk parameter should be increased.

Neural Processing Letters, 1995

In this paper, we propose a combination of an adaptive noise-reduction algorithm based on Singula... more In this paper, we propose a combination of an adaptive noise-reduction algorithm based on Singular-Spectrum Analysis (SSA) and a standard feedforward neural prediction model. We test the tbrecast skill of our method on some short real-world and computergenerated time series with different amounts of additive noise. The results show that our combined technique has better performances than those offered by the same network directly applied to raw data, and therefore is well suited to forecast short and noisy time series with an underlying deterministic data generating process (DGP).

A Survey on the Four Families of Performance Measures

Journal of Economic Surveys, 2013

ABSTRACT Performance measurement is one of the most studied subjects in financial literature. Sin... more ABSTRACT Performance measurement is one of the most studied subjects in financial literature. Since the introduction of the Sharpe ratio in 1966, a large variety of new measures has appeared constantly in scientific journals as well as in practitioners' publications. The most complete and significant studies of performance measures, so far, have been written by Aftalion and Poncet, Le Sourd, Bacon, and Cogneau and H übner. A review of the most recent literature led us to collect several dozen measures that we classify into four families. We first present the class of relative measures, starting with the Sharpe ratio. Secondly, we analyse absolute measures, beginning with the most famous one - the Jensen alpha. Thirdly, we study general measures based on specific features of the return distribution, where the pioneering contributions are those of Bernardo and Ledoit, and Keating and Shadwick. Finally, the fourth set concerns a few measures that explicitly take into account the investor's utility functions.

Metodi Quantitativi Per La Gestione Automatica DI Fondi Comuni

labeconomia.unisa.it

ABSTRACT: In questa nota si presentano i risultati di uno studio relativo alle performance di una... more ABSTRACT: In questa nota si presentano i risultati di uno studio relativo alle performance di una strategia per la gestione automatica di un fondo comune. Tale strategiae articolata in due fasi e prende le mosse dalla prassi seguita da una societa di consulenza finanziaria ...

The interbanking liquidity market: Short-time prediction and the central bank reserve management

Decisions in Economics and Finance, 1997

Downloads: (external link) http://hdl.handle.net/10.1007/BF02688989 (text/html) Access to full te... more Downloads: (external link) http://hdl.handle.net/10.1007/BF02688989 (text/html) Access to full text is restricted to subscribers. ... Related works: This item may be available elsewhere in EconPapers: Search for items with the same title. ... This site is part of RePEc and all the ...

One-Step Prediction of Chaotic Time Series by Multivariate Reconstruction

Working Papers, 1997

... EconPapers has moved to http://EconPapers.repec.org! Please update your bookmarks. One-Step P... more ... EconPapers has moved to http://EconPapers.repec.org! Please update your bookmarks. One-Step Prediction of Chaotic Time Series by Multivariate Reconstruction. Francesco Lisi (). ...

Interval prediction for chaotic time series

Metron, 2001

Prediction of chaotic dynamical systems has been extensively studied and a wide variety of predic... more Prediction of chaotic dynamical systems has been extensively studied and a wide variety of predictive methods has been proposed (ie Tong, 1995). Most of this work, however, concerns just point prediction, that is prediction based on a single number with no ...

Energy Economics, 2013

This paper considers how well the approach of combining forecasts extends to the context of elect... more This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk management.

Predictive accuracy for chaotic economic models

Economics Letters, 2001

In this work we present a technique to obtain prediction intervals for chaotic data. Using neares... more In this work we present a technique to obtain prediction intervals for chaotic data. Using nearest neighbors method we give estimates of local variance and percentiles of the prediction error distribution. This allows to define an interval containing a future value with ...

Econometric Reviews, 2008

A distinguishing feature of the intra-day time-varying volatility of financial time series is giv... more A distinguishing feature of the intra-day time-varying volatility of financial time series is given by the presence of long-range dependence of periodic type due mainly to time-of-the-day phenomena. In this work we introduce a model able to describe the empirical evidence given by this periodic longmemory behaviour. The model, named PLM-GARCH (Periodic Long Memory GARCH), represents a natural extension of the FIGARCH model proposed for modelling long-range persistence of the volatility of financial time series. Periodic long memory versions of EGARCH (PLM-EGARCH) models are also considered. Some properties and characteristics of the models are given and an estimation procedure based on quasi maximum likelihood is established. Further possible extensions of the model to take into account multiple sources of periodic long-memory behaviour are suggested. Some empirical applications on intra-day financial time series are also provided.

Il mercto interbancario dei depositi: Previsione a breve e gestione della riserva obbligatoria

Decisions in Economics and Finance, 1997

La capacità di fare previsioni sul mercato monetario è un punto cruciale nella gestione ottima di... more La capacità di fare previsioni sul mercato monetario è un punto cruciale nella gestione ottima di tesoreria di una banca, anche in virtú della possibilità di mobilizzare parte della riserva obbligatoria presso la Banca d’Italia. In questo studio viene mostrato come l’uso congiuuto di tecniche di previsione per i tassi a breve e la costruzione di un modello decisionale per

Computational Statistics & Data Analysis, 1999

Forecasting currency exchange rates is an important ÿnancial problem which is receiving increasin... more Forecasting currency exchange rates is an important ÿnancial problem which is receiving increasing attention especially because of its intrinsic di culty and practical applications. During the last few years, a number of nonlinear models have been proposed for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. Among them, neural networks and chaotic models have been used with encouraging results. It is the aim of this paper to provide a comparative evaluation of these two models over common data sets and variables and verify whether they are able to predict better than chance under the same experimental conditions. In particular, the data used in this study were the monthly exchange rates of the four major European currencies from 1973 to 1995. The prediction performance is measured in terms of the well-known normalized mean square error (NMSE) as well as in terms of the statistical signiÿcance of the forecasts obtained. To this end, a test statistic proposed by Mizrach has been considered. The experimental results obtained show that neural networks compare favorably with chaotic models, in terms of NMSE and, in turn, both models perform substantially better than that based on the random walk hypothesis. From the statistical signiÿcance standpoint, instead, it was found that neural networks' forecasts are statistically equivalent to those yielded by chaotic models and, in most cases, both turn out to be statistically better than those obtained by the random walk.

Combining Forecasts for Nonlinear Testing

Forecasting financial time series with dynamic skewness and kurtosis

S Co 2007, Dec 6, 2007

SSRN Electronic Journal, 2000

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch ge... more Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.

Contributions to Statistics, 2013

The use of computational methods in statistics to face complex problems and highly dimensional da... more The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This volume presents the revised version of a selection of the papers given at S.Co. 2011, the 7th Conference on Statistical Computation and Complex Systems, held in Padua, Italy, September 19-21, 2011. The S.Co. conference is a forum for the discussion of new developments and applications of statistical methods and computational techniques for complex and high-dimensional datasets. Although the topics covered in this volume are diverse, the same themes recur, as research is mostly fueled by the need to analyse complicated data sets, for which traditional methods do not provide viable solutions. Among the topics presented we have estimation of traffic matrices in a communications network, in the presence of long-range dependence; nonparametric mixed-effects models for epidemiology; advanced methods for neuroimaging; efficient computations and inference in environmental studies; hierarchical and nonparametric Bayesian methods with applications in genomic studies; Markov switching models to explain regime changes in the evolution of realized volatility for financial returns; joint modelling of financial returns and multiple daily realized measures; classification of multivariate linear-circular data, with applications to marine monitoring networks; forecasting of electricity supply functions, using principal component analysis and reduced rank regression; clustering based on nonparametric density estimation; surface estimation and spatial smoothing, with applications to the estimation of the blood-flow velocity field. Whilst not exhaustive, this list should give a feel of the range of issues discussed at the conference. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods. v vi Preface We wish to thank all contributors who made this volume possible. Finally, thanks must go to the reviewers, who responded rapidly when put under pressure and helped improve the papers with their valuable comments and suggestions.

Predictive dimension: an alternative definition to embedding dimension

COMPSTAT, 2000

Abstract. In this paper we propose an alternative definition to the embedding dimension that we c... more Abstract. In this paper we propose an alternative definition to the embedding dimension that we call predictive dimension. This dimension does not refer to the number of delayed variables needed to characterize the system but to the best predictions that can be ...

Statistical Methods and Applications, 2010

Distributional theory for Quasi-Maximum Likelihood estimators in long memory conditional heterosk... more Distributional theory for Quasi-Maximum Likelihood estimators in long memory conditional heteroskedastic models is not formally defined, even asympotically. Because of that, this paper analyses the performance of the Likelihood Ratio and the Lagrange Multiplier misspecification tests for Periodic Long Memory GARCH models. The real size and power of these tests are studied by means of Monte Carlo simulations with respect to the class of Generalized Long Memory GARCH models. An application to the U SD/JP Y exchange rate is also provided.

Journal of the Italian Statistical Society, 1996

Singular spectrum analysis has been proposed in the field of nonlinear dynamical systems as filte... more Singular spectrum analysis has been proposed in the field of nonlinear dynamical systems as filtering method. In this paper a criterion to choose the number of components which leads to the best filtering is proposed. The selection is made by minimizing the prediction elTor.

Quantitative Finance, 2012

In the mutual funds industry the rating process is very important, and Morningstar is surely the ... more In the mutual funds industry the rating process is very important, and Morningstar is surely the most influential international rating agency. In this work we consider the problem of evaluating if the risk component is adequately accounted for in the Morningstar rating. To face this problem we compare the ratings produced giving different weights to the risk component. The focus of the analysis is on testing the hypothesis that two similar rating procedures with different risk parameters (or, in statistical terms, two raters) are equivalent. To that end, first the notion of β−equivalence is introduced and then a Monte Carlo test for the hypothesis of β−equivalence is described. Finally, to answer the question on the role of risk in the Morningstar rating, we analyze 1763 monthly return time series of US mutual funds. Results show that the current Morningstar classification, based on a risk-adjusted measure, only marginally accounts for risk and that if we want that risk really matters, the risk parameter should be increased.

Neural Processing Letters, 1995

In this paper, we propose a combination of an adaptive noise-reduction algorithm based on Singula... more In this paper, we propose a combination of an adaptive noise-reduction algorithm based on Singular-Spectrum Analysis (SSA) and a standard feedforward neural prediction model. We test the tbrecast skill of our method on some short real-world and computergenerated time series with different amounts of additive noise. The results show that our combined technique has better performances than those offered by the same network directly applied to raw data, and therefore is well suited to forecast short and noisy time series with an underlying deterministic data generating process (DGP).

A Survey on the Four Families of Performance Measures

Journal of Economic Surveys, 2013

ABSTRACT Performance measurement is one of the most studied subjects in financial literature. Sin... more ABSTRACT Performance measurement is one of the most studied subjects in financial literature. Since the introduction of the Sharpe ratio in 1966, a large variety of new measures has appeared constantly in scientific journals as well as in practitioners' publications. The most complete and significant studies of performance measures, so far, have been written by Aftalion and Poncet, Le Sourd, Bacon, and Cogneau and H übner. A review of the most recent literature led us to collect several dozen measures that we classify into four families. We first present the class of relative measures, starting with the Sharpe ratio. Secondly, we analyse absolute measures, beginning with the most famous one - the Jensen alpha. Thirdly, we study general measures based on specific features of the return distribution, where the pioneering contributions are those of Bernardo and Ledoit, and Keating and Shadwick. Finally, the fourth set concerns a few measures that explicitly take into account the investor's utility functions.

Metodi Quantitativi Per La Gestione Automatica DI Fondi Comuni

labeconomia.unisa.it

ABSTRACT: In questa nota si presentano i risultati di uno studio relativo alle performance di una... more ABSTRACT: In questa nota si presentano i risultati di uno studio relativo alle performance di una strategia per la gestione automatica di un fondo comune. Tale strategiae articolata in due fasi e prende le mosse dalla prassi seguita da una societa di consulenza finanziaria ...

The interbanking liquidity market: Short-time prediction and the central bank reserve management

Decisions in Economics and Finance, 1997

Downloads: (external link) http://hdl.handle.net/10.1007/BF02688989 (text/html) Access to full te... more Downloads: (external link) http://hdl.handle.net/10.1007/BF02688989 (text/html) Access to full text is restricted to subscribers. ... Related works: This item may be available elsewhere in EconPapers: Search for items with the same title. ... This site is part of RePEc and all the ...

One-Step Prediction of Chaotic Time Series by Multivariate Reconstruction

Working Papers, 1997

... EconPapers has moved to http://EconPapers.repec.org! Please update your bookmarks. One-Step P... more ... EconPapers has moved to http://EconPapers.repec.org! Please update your bookmarks. One-Step Prediction of Chaotic Time Series by Multivariate Reconstruction. Francesco Lisi (). ...

Interval prediction for chaotic time series

Metron, 2001

Prediction of chaotic dynamical systems has been extensively studied and a wide variety of predic... more Prediction of chaotic dynamical systems has been extensively studied and a wide variety of predictive methods has been proposed (ie Tong, 1995). Most of this work, however, concerns just point prediction, that is prediction based on a single number with no ...

Energy Economics, 2013

This paper considers how well the approach of combining forecasts extends to the context of elect... more This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk management.

Predictive accuracy for chaotic economic models

Economics Letters, 2001

In this work we present a technique to obtain prediction intervals for chaotic data. Using neares... more In this work we present a technique to obtain prediction intervals for chaotic data. Using nearest neighbors method we give estimates of local variance and percentiles of the prediction error distribution. This allows to define an interval containing a future value with ...

Econometric Reviews, 2008

A distinguishing feature of the intra-day time-varying volatility of financial time series is giv... more A distinguishing feature of the intra-day time-varying volatility of financial time series is given by the presence of long-range dependence of periodic type due mainly to time-of-the-day phenomena. In this work we introduce a model able to describe the empirical evidence given by this periodic longmemory behaviour. The model, named PLM-GARCH (Periodic Long Memory GARCH), represents a natural extension of the FIGARCH model proposed for modelling long-range persistence of the volatility of financial time series. Periodic long memory versions of EGARCH (PLM-EGARCH) models are also considered. Some properties and characteristics of the models are given and an estimation procedure based on quasi maximum likelihood is established. Further possible extensions of the model to take into account multiple sources of periodic long-memory behaviour are suggested. Some empirical applications on intra-day financial time series are also provided.

Il mercto interbancario dei depositi: Previsione a breve e gestione della riserva obbligatoria

Decisions in Economics and Finance, 1997

La capacità di fare previsioni sul mercato monetario è un punto cruciale nella gestione ottima di... more La capacità di fare previsioni sul mercato monetario è un punto cruciale nella gestione ottima di tesoreria di una banca, anche in virtú della possibilità di mobilizzare parte della riserva obbligatoria presso la Banca d’Italia. In questo studio viene mostrato come l’uso congiuuto di tecniche di previsione per i tassi a breve e la costruzione di un modello decisionale per

Computational Statistics & Data Analysis, 1999

Forecasting currency exchange rates is an important ÿnancial problem which is receiving increasin... more Forecasting currency exchange rates is an important ÿnancial problem which is receiving increasing attention especially because of its intrinsic di culty and practical applications. During the last few years, a number of nonlinear models have been proposed for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. Among them, neural networks and chaotic models have been used with encouraging results. It is the aim of this paper to provide a comparative evaluation of these two models over common data sets and variables and verify whether they are able to predict better than chance under the same experimental conditions. In particular, the data used in this study were the monthly exchange rates of the four major European currencies from 1973 to 1995. The prediction performance is measured in terms of the well-known normalized mean square error (NMSE) as well as in terms of the statistical signiÿcance of the forecasts obtained. To this end, a test statistic proposed by Mizrach has been considered. The experimental results obtained show that neural networks compare favorably with chaotic models, in terms of NMSE and, in turn, both models perform substantially better than that based on the random walk hypothesis. From the statistical signiÿcance standpoint, instead, it was found that neural networks' forecasts are statistically equivalent to those yielded by chaotic models and, in most cases, both turn out to be statistically better than those obtained by the random walk.