Dipartimento di Economia
Università degli Studi di Perugia

Relatore Umberto Cherubini, Università di Bologna 

Data 15 dicembre 2023 h. 12:00 

Luogo Dipartimento di Economia, Aula 202 

Abstract : We model the bid-ask spreads of call and put options by a two-tail distortion (2TD) of a reference probability distribution. The model applies the Choquet pricing approach with no-arbitrage restrictions, requiring a duality relationship between the capacities pricing long and short positions of call and put options. Moreover, the put-call parity relationship requires that the sum of bid ask spreads of call and put option with the same strike be invariant across the strikes. We calibrate the 2TD model with a simple Sugeno distortion on a sample of two months daily data for three stock indexes and three different reference models and show that the 2TD generally provides a better fit to the data than the standard distortion of one tail only.    Moreover, the estimate of the distortion parameter happens to be very similar across the different models. 

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Relatore: Arjun Shah (King’s College London)

 Data: 14 dicembre 2023 ore 11.30

Luogo: Aula 23 – Dipartimento di Economia (Via Alessandro Pascoli, 20)

Abstract: Scientific knowledge creates value by serving as an input to technology development. Variation in such value across disciplines, institutions, and regions may inform efficient targeting of science funding. This project proposes a method to rank scientific output by the value of its knowledge spillovers to technology. Using the full patent-paper citation network, we assign a portion of the private returns from each patented invention to the inventions and scientific contributions it builds upon directly or indirectly. We use our method to describe heterogeneity in spillovers across countries, disciplines and institutions, with a particular focus on the UK and comparator countries. We consider spillovers from science into global innovation, and those that are retained within national boundaries. We also discuss the value created by science flowing into specific areas of technology, specifically innovations in clean industries. Such analysis can help to inform industrial and innovation policies.

Organizzazione

Francesco Venturini (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.

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Data e ora: 14 novembre 2023, ore 10.30

Luogo: Aula 2

Titolo: Modello di business dell’industria assicurativa e sua evoluzione

Relatore: Dr. Alberto Tosti

Titolo: Corporate technologies nell’assicurazione

Relatore: Prof. Massimo De Felice

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Relatore: Tommaso Proietti (Università di Roma “Tor Vergata”)

 Data: 29 settembre 2023 dalle 12:00 alle 13:00

Luogo: Aula 101 – Dipartimento di Economia (Via Alessandro Pascoli, 20)

Abstract

Serial dependence and predictability are two sides of the same coin. The literature has considered alternative measures of these two fundamental concepts. In this paper, we aim to distill the most predictable aspect of a univariate time series, i.e., the one for which predictability is optimized. Our target measure is the mutual information between the past and future of a random process, a broad measure of predictability that takes into account all future forecast horizons, rather than focusing on the one-step-ahead prediction error mean square error. The first most predictable aspect is defined as the measurable transformation of the series, which maximizes the mutual information between past and future. The proposed transformation arises from the linear combination of a set of basis functions localized at the quantiles of the unconditional distribution of the process. The mutual information is estimated as a function of the sample partial autocorrelations, by a semiparametric method which estimates an infinite sum by a regularized finite sum. The second most predictable aspect can also be defined, subject to suitable orthogonality restrictions. We also investigate using the most predictable aspect for testing the null of no predictability.

Organizzazione

David Aristei (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.)

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