Giant nonlinear response at the nanoscale driven by bound states in the continuum

Being motivated by the recent prediction of high-$Q$ supercavity modes in subwavelength dielectric resonators, we study the second-harmonic generation from isolated subwavelength AlGaAs nanoantennas pumped by a structured light. We reveal that nonlinear effects at the nanoscale can be enhanced dramatically provided the resonator parameters are tuned to the regime of the bound state in the continuum. We predict a record-high conversion efficiency for nanoscale resonators that exceeds by two orders of magnitude the conversion efficiency observed at the conditions of magnetic dipole Mie resonance, thus opening the way for highly-efficient nonlinear metadevices.Comment: 7 pages, 4 figures, 1 tabl

Blind to carbon risk? An Analysis of Stock Market's Reaction to the Paris Agreement.

It is increasingly recognized that a transition to sustainable finance is crucial to scale up the low-carbon investments needed to achieve the global climate targets. A main barrier to portfolios' decarbonization is the lack of conclusive evidence on whether low-carbon investments add value to a portfolio, and on whether markets react to climate announcements by rewarding (penalizing) low-carbon (carbon-intensive) assets. To fill this gap, we develop an empirical analysis of the low-carbon and carbon-intensive indices for the EU, US and global stock markets. We test if financial markets are pricing the Paris Agreement (PA) by decreasing (increasing) the systematic risk and increasing (decreasing) the portfolio weights of low-carbon (carbon-intensive) indices afterwards. We find that after the PA the correlation among low-carbon and carbon-intensive indices drops. The overall systematic risk for the low-carbon indices decreases consistently, while stock markets' reaction is mild for most of carbon-intensive indices. Moreover, the weight of the low-carbon indices within an optimal portfolio tends to increase after the PA. This evidence suggests that stock market investors have started to consider low-carbon assets as an appealing investment opportunity after the PA but have not penalized yet carbon-intensive assets

Model selection in hidden Markov models : a simulation study

A review of model selection procedures in hidden Markov models reveals contrasting evidence about the reliability and the precision of the most commonly used methods. In order to evaluate and compare existing proposals, we develop a Monte Carlo experiment which allows a powerful insight on the behaviour of the most widespread model selection methods. We find that the number of observations, the conditional state-dependent probabilities, and the latent transition matrix are the main factors influencing information criteria and likelihood ratio test results. We also find evidence that, for shorter univariate time series, AIC strongly outperforms BIC

PARX model for football matches predictions

We propose an innovative approach to model and predict the outcome of football matches based on the Poisson Autoregression with eXogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen and Rahbek (2016). We show that this methodology is particularly suited to model the goals distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may incorporate also the betting volumes or strategic price settings in order to exploit bettors’ biases. The out-of-sample performance of the PARX model is better than the reference approach by Dixon and Coles (1997). We also evaluate our approach in a simple betting strategy which is applied to the English football Premier League data for the 2013/2014 and 2014/2015 seasons. The results show that the return from the betting strategy is larger than 35% in all the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold which allows to exploit the inefficiency of the betting market

Disequilibria and contagion in financial markets: Evidence from a new test

This paper provides an analysis of contagion by measuring disequilibria in risk premium dynamics. We propose to test financial contagion using an econometric procedure where we first estimate the preference parameters of the consumption-based asset pricing model (C-CAPM) to measure the equilibrium risk premia in different countries and then we consider the difference between empirical and equilibrium risk premia to test crosscountry disequilibrium episodes due to contagion. Disequilibrium in financial markets is modeled by the multivariate DCC-GARCH model including a deterministic crisis variable. Our approach allows to identify the disequilibria generated by increases in volatility that is not explained by fundamentals but is endogenous to financial markets and to evaluate the existence of contagion effects defined by exogenous shifts in cross-country return correlations during crisis periods. Our results show evidence of contagion from the U.S. to U.K., Japan, France, and Italy during the crisis started in 2007-08

A dynamic analysis of stock markets using a hidden Markov model

none2siThis paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student-t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies.openL. De Angelis; L.J. PaasL. De Angelis; L.J. Paa

Accuracy and precision of an intraoral scanner in complex prosthetic rehabilitations: an in vitro study

The main purpose of this study is to measure the accuracy and the precision of the intraoral optical scanner CS3500® (Carestream Dental LLC, Atlanta, USA) in complex clinical situations as full arch rehabilitations on impl ants. 50 scans of the acrylic resin model were performed by using CS3500® (Carestream Dental LLC, Atlanta, USA) scanner. Each scan was compared with the virtual model derived from scanning with the laboratory scanner Dscan3® (Enhanced Geometry Soluti on, Bologna, Italy) to measure a possible misalignment. The alignment error was found to be 79,6 ( ± 12,87)  m. The measurement was taken at the level of 2 distal scan - abutments. The scanner's precision ranges from 24 to 52  m , depending on the dist ance between scan - abutment. CS3500® (Carestream Dental LLC, Atlanta, USA) intraoral scanner has detected a valid device in the execution of complex rehabilitations on implants. His accuracy and precision values fall within the range established in li terature to define acceptable the prosthetic fitting on full arch implant rehabilitation

Metodi statistici a variabili latenti per lo studio di fenomeni finanziari

Negli ultimi decenni il concetto di variabile latente ha riscosso un enorme successo nelle discipline statistiche come attestano i numerosi lavori scientifici presenti in letteratura. In particolare, nelle scienze sociali e in psicometria, l’uso del concetto di variabile latente è stato largamente adottato per far fronte al problema di misurare quantità che, in natura, non possono essere direttamente osservate. La vasta letteratura riguardante questa metodologia si espande, in maniera più limitata, anche al campo della ricerca economica ed econometrica. Nonostante esistano studi di modelli a struttura latente applicati a variabili di tipo economico, molto pochi sono i lavori che considerano variabili finanziarie e, finora, praticamente nessun ricercatore ha messo in connessione la teoria standard di portafoglio con la metodologia dei modelli statistici a variabili latenti. L’obiettivo del lavoro è quello di ricorrere alle potenzialità esplicative ed investigative dei metodi statistici a variabili latenti per l’analisi dei fenomeni finanziari. Si fa riferimento, in particolare, ai modelli a classe latente che consentono di sviluppare soluzioni metodologicamente corrette per importanti problemi ancora aperti in campo finanziario. In primo luogo, la natura stessa delle variabili finanziarie è riconducibile al paradigma delle variabili latenti. Infatti, variabili come il rischio ed il rendimento atteso non possono essere misurate direttamente e necessitano di approssimazioni per valutarne l’entità. Tuttavia, trascurare la natura non osservabile delle variabili finanziarie può portare a decisioni di investimento inopportune o, talvolta, addirittura disastrose. Secondariamente, vengono prese in considerazione le capacità dei modelli a classi latenti nel contesto della classificazione. Per i prodotti finanziari, infatti, una corretta classificazione sulla base del profilo (latente) di rischio e rendimento rappresenta il presupposto indispensabile per poter sviluppare efficaci strategie di investimento. Ci si propone, inoltre, di sviluppare un collegamento, finora mancante, tra uno dei principali riferimenti della finanza moderna, la teoria classica del portafoglio di Markowitz, e la metodologia statistica dei modelli a variabili latenti. In questo contesto, si vogliono investigare, in particolare, i benefici che i modelli a variabili latenti possono dare allo studio di ottimizzazione del profilo rischio - rendimento atteso di un portafoglio di attività finanziarie. Lo sviluppo di numeri indici dei prezzi delle attività finanziarie caratterizzati da una solida base metodologica rappresenta un ulteriore aspetto nel quale i modelli a classe latente possono svolgere un ruolo di fondamentale importanza. In particolare, si propone di analizzare il contesto dei numeri indici dei prezzi settoriali, che costituiscono uno dei riferimenti più importanti nelle strategie di diversificazione del rischio. Infine, il passaggio da una specificazione statica ad una analisi dinamica coglie aspetti metodologici di frontiera che possono essere investigati nell’ambito dei modelli markoviani a classi latenti. Il profilo latente di rischio – rendimento può essere, così, investigato in riferimento alle diverse fasi dei mercati finanziari, per le quali le probabilità di transizione consentono valutazioni di tipo previsivo di forte interesse

A comparison of sequential and information-based methods for determining the co-integration rank in heteroskedastic VAR models

In this paper we investigate the behaviour of a number of methods for estimating the co-integration rank in VAR systems characterized by heteroskedastic innovation processes. In particular we compare the efficacy of the most widely used information criteria, such as AIC and BIC, with the commonly used sequential approach of Johansen (1996) based around the use of either asymptotic or wild bootstrap-based likelihood ratio type tests. Complementing recent work done for the latter in Cavaliere, Rahbek and Taylor (2013, Econometric Reviews, forthcoming), we establish the asymptotic properties of the procedures based on information criteria in the presence of heteroskedasticity (conditional or unconditional) of a quite general and unknown form. The relative finite-sample properties of the different methods are investigated by means of a Monte Carlo simulation study. For the simulation DGPs considered in the analysis, we find that the BIC-based procedure and the bootstrap sequential test procedure deliver the best overall performance in terms of their frequency of selecting the correct co-integration rank across different values of the co-integration rank, sample size, stationary dynamics and models of heteroskedasticity. Of these the wild bootstrap procedure is perhaps the more reliable overall since it avoids a significant tendency seen in the BIC-based method to over-estimate the co-integration rank in relatively small sample sizes