Investigaciónhttp://hdl.handle.net/10016/161192017-04-29T23:25:49Z2017-04-29T23:25:49ZOptimal hedging under departures from the cost of carry valuation: evidence from the spanish stock index futures marketLafuente, Juan A.http://hdl.handle.net/10016/98532017-04-18T13:40:41Z2000-01-01T00:00:00ZOptimal hedging under departures from the cost of carry valuation: evidence from the spanish stock index futures market
Lafuente, Juan A.
Universidad Carlos III de Madrid. Departamento de Economía de la Empresa
This paper provides an a~alytical discussion of the optimal hedge ratio when discrepancies between the futures trading price and its theoretical valuation according to the cost-of-carry model occurs. Under the assumption of a geometric Brownian motion for spot prices we model the mispricing by a new specific noise in the theoretical dynamic of futures market. Empirical evidence above the model is provided for the Spanish stock index futures. Ex-post simulations reveal that hedging effectiveness applying the estimated ratio is similar to the achieved with a systematic unitary hedge ratio, the optimal one when a mispricing does not appear. However, a small number of futures contracts is needed.
2000-01-01T00:00:00ZClustering Big Data by Extreme Kurtosis ProjectionsPeña Sánchez de Rivera, DanielPrieto Fernández, Francisco JavierRendon Aguirre, Janeth Carolinahttp://hdl.handle.net/10016/245222017-04-29T00:14:33Z2017-04-27T00:00:00ZClustering Big Data by Extreme Kurtosis Projections
Peña Sánchez de Rivera, Daniel; Prieto Fernández, Francisco Javier; Rendon Aguirre, Janeth Carolina
Universidad Carlos III de Madrid. Departamento de Estadística
Clustering Big Data is an important problem because large samples of many variables are usually heterogeneous and include mixtures of several populations. It often happens that only some of a large set of variables are useful for clustering and working with all of them would be very inefficient and may make more difficult the identification of the clusters. Thus, searching for spaces of lower dimension that include all the relevant information about the clusters seems a sensible way to proceed in these situations. Peña and Prieto (2001) showed that the extreme kurtosis directions of projected data are optimal when the data has been generated by mixtures of two normal distributions. We generalize this result for any number of mixtures and show that the extreme kurtosis directions of the projected data are linear combinations of the optimal discriminant directions if we knew the centers of the components of the mixture. In order to separate the groups we want directions that split the data into two groups, each corresponding to different components of the mixture. We prove that these directions can be found from extreme kurtosis projections. This result suggests a new procedure to deal with many groups, working in a binary decision way and deciding at each step if the data should be split into two groups or we should stop. The decision is based on comparing a single distribution with a mixture of two distribution. The performance of the algorithm is analyzed through a simulation study.
2017-04-27T00:00:00ZEvaluating significant effects from alternative seeding systems : a Bayesian approach, with an application to the UEFA Champions LeagueCorona, FranciscoForrest, DavidTena, Juan de DiosWiper, Michael Peterhttp://hdl.handle.net/10016/245212017-04-29T00:14:35Z2017-04-01T00:00:00ZEvaluating significant effects from alternative seeding systems : a Bayesian approach, with an application to the UEFA Champions League
Corona, Francisco; Forrest, David; Tena, Juan de Dios; Wiper, Michael Peter
Universidad Carlos III de Madrid. Departamento de Estadística
The paper discusses how to evaluate alternative seeding systems in sports competitions. Prior papers have developed an approach which uses a forecasting model at the level of the individual match and then applies Monte Carlo simulation of the whole tournament to estimate the probabilities associated with various outcomes or combinations of outcomes. This allows, for example, a measure of outcome uncertainty to be attached to each proposed seeding regime. However, this established approach takes no note of the uncertainty surrounding the parameter estimates in the underlying match forecasting model and this precludes testing for statistically significant differences between probabilities or outcome uncertainty measures under alternative regimes. We propose a Bayesian approach which resolves this weakness in standard methodology and illustrate its potential by examining the effect of seeding rule changes implemented in the UEFA Champions League, a major football tournament, in 2015. The reform appears to have increased outcome uncertainty. We identify which clubs and which sorts of clubs were favourably or unfavourably affected by the reform, distinguishing effects on probabilities of progression to different phases of the competition.
2017-04-01T00:00:00ZCIFRA: Challenging the ICT Patent Framework for Responsible Innovation. D2.2: Empirical Evidence on ICT patents between 1990 and 2012Dalay, Hakki DoganFOSFURI, ANDREARaffaele, MorandiBlind, KnutFullea, JavierLópez-Carrasco, AntonioMartínez Ros, EsterÁlvarez Iturri, Silvana Valeriahttp://hdl.handle.net/10016/245192017-04-29T00:10:48Z2017-01-01T00:00:00ZCIFRA: Challenging the ICT Patent Framework for Responsible Innovation. D2.2: Empirical Evidence on ICT patents between 1990 and 2012
Dalay, Hakki Dogan; FOSFURI, ANDREA; Raffaele, Morandi; Blind, Knut; Fullea, Javier; López-Carrasco, Antonio; Martínez Ros, Ester; Álvarez Iturri, Silvana Valeria
2017-01-01T00:00:00Z