Bank of Italy Research Paper Series最新文献

{"title":"Uncertainty Matters: Evidence from a High-frequency Identification Strategy","authors":"Piergiorgio Alessandri, A. Gazzani, Alejandro Vicondoa","doi":"10.2139/ssrn.3659570","DOIUrl":"https://doi.org/10.2139/ssrn.3659570","url":null,"abstract":"Assessing the role of uncertainty shocks as a driver of business cycle fluctuations is challenging because spikes in uncertainty often coincide with news about economic fundamentals. To tackle this problem, we exploit daily data to identify uncertainty shocks that (i) impact the VXO volatility index, and (ii) are statistically independent from level shocks affecting stock prices. We then use the identified series of uncertainty shocks in a monthly VAR to estimate their macroeconomic effects on the US economy. An exogenous increase in uncertainty depresses economic activity and prices, significantly affecting both labor and capital goods markets. Uncertainty shocks account for about 20% of the cyclical fluctuations in employment and industrial production.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126198901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Update of the Bank of Italy Methodology Underlying the Estimation of Price-Competitiveness Misalignments","authors":"Claire Giordano","doi":"10.2139/ssrn.3612758","DOIUrl":"https://doi.org/10.2139/ssrn.3612758","url":null,"abstract":"This paper documents the recent innovations to the Bank of Italy methodology underlying the estimation of price-competitiveness misalignments , first put forward in Giordano (2018); it also provides the most recent misalignment estimates for the euro area and for its four main economies, based on five alternatively deflated indicators. The extension of the sample period, the recalibration of the trade weights employed and the significant data revisions and refinements introduced have not qualitatively modified the assessment of misalignments since 1999 for the afore-mentioned economies, although point estimates have changed non-negligibly. In the first half of 2019, no significant price competitiveness misalignment is recorded for Italy and for the euro area as a whole, whereas for France, Germany and Spain there is still evidence of a modest undervaluation.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134132450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Firms’ Listings: What is New? Italy Versus the Main European Stock Exchanges","authors":"Paolo Finaldi Russo, Fabio Parlapiano, Daniele Pianeselli, I. Supino","doi":"10.2139/ssrn.3612754","DOIUrl":"https://doi.org/10.2139/ssrn.3612754","url":null,"abstract":"Over the last decade and a half non-financial corporations’ (NFCs) listings have displayed a heterogeneous pattern across European countries. The number of listed NFCs has increased in Italy and Spain, while it has declined in Germany, France and the United Kingdom. In Italy, the increase in the number of listed firms has been driven by SMEs’ listings, leaving the stock market small by international standards. We break down the size gap of the Italian equity market (with respect to its European peers) into the share of listed companies and their relative size. We show that the lower share of listed NFCs in Italy accounts for the gap with France and the UK, while the smaller size of Italian public firms has a crucial bearing on the differences with Germany and Spain. Counterfactual exercises provide evidence that there is limited room to bridge these gaps, as the structure of the Italian economy leans towards small enterprises. Policy measures aimed at fostering SMEs’ propensity to go public may be more effective in promoting the further development of the Italian stock exchange.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123306283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality Checks on Granular Banking Data: An Experimental Approach Based on Machine Learning?","authors":"Fabio Zambuto, Maria Rosaria Buzzi, G. Costanzo, Marco Di Lucido, Barbara La Ganga, Pasquale Maddaloni, F. Papale, Emiliano Svezia","doi":"10.2139/ssrn.3612688","DOIUrl":"https://doi.org/10.2139/ssrn.3612688","url":null,"abstract":"We propose a new methodology, based on machine learning algorithms, for the automatic detection of outliers in the data that banks report to the Bank of Italy. Our analysis focuses on granular data gathered within the statistical data collection on payment services, in which the lack of strong ex ante deterministic relationships among the collected variables makes standard diagnostic approaches less powerful. Quantile regression forests are used to derive a region of acceptance for the targeted information. For a given level of probability, plausibility thresholds are obtained on the basis of individual bank characteristics and are automatically updated as new data are reported. The approach was applied to validate semi-annual data on debit card issuance received from reporting agents between December 2016 and June 2018. The algorithm was trained with data reported in previous periods and tested by cross-checking the identified outliers with the reporting agents. The method made it possible to detect, with a high level of precision in term of false positives, new outliers that had not been detected using the standard procedures.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127280043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Institutional Sector Cassifier, a Machine Learning Approach","authors":"Paolo Massaro, I. Vannini, O. Giudice","doi":"10.2139/ssrn.3612710","DOIUrl":"https://doi.org/10.2139/ssrn.3612710","url":null,"abstract":"We implement machine learning techniques to obtain an automatic classification by sector of economic activity of the Italian companies recorded in the Bank of Italy Entities Register. To this end, first we extract a sample of correctly classified corporations from the universe of Italian companies. Second, we select a set of features that are related to the sector of economic activity code and use these to implement supervised approaches to infer output predictions. We choose a multi-step approach based on the hierarchical structure of the sector classification. Because of the imbalance in the target classes, at each step, we first apply two resampling procedures – random oversampling and the Synthetic Minority Over-sampling Technique – to get a more balanced training set. Then, we fit Gradient Boosting and Support Vector Machine models. Overall, the performance of our multi-step classifier yields very reliable predictions of the sector code. This approach can be employed to make the whole classification process more efficient by reducing the area of manual intervention.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116683070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Climate Risk for Finance in Italy","authors":"Ivan Faiella, Danila Malvolti","doi":"10.2139/ssrn.3612671","DOIUrl":"https://doi.org/10.2139/ssrn.3612671","url":null,"abstract":"The increasing attention paid to the possible consequences of climate change for the financial sector has strengthened international cooperation on green finance, with initiatives from both the industry and the institutions. International surveys show that so far there has been no adequate growth in awareness of the risks linked to climate change and the opportunities linked to the transition towards a low carbon economy. Evidence acquired on Climate-Related Financial Risk (CRFR) disclosure in Italy has confirmed the same conclusions. We have therefore identified three steps with the aim of encouraging financial institutions to take CRFR into account in their corporate risk management strategies: 1) create a information hub to gather the information required for assessing the CRFR; 2) compile a list of the information not yet available; 3) define standard methodologies that allow the climate scenarios to be part of the decision-making processes of financial institutions.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121444010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public Credit Guarantees and Financial Additionalities Across SME Risk Classes","authors":"Emanuele Ciani, M. Gallo, Zeno Rotondi","doi":"10.2139/ssrn.3612924","DOIUrl":"https://doi.org/10.2139/ssrn.3612924","url":null,"abstract":"In this paper we study the functioning of the Italian public guarantee fund (“Fondo Centrale di Garanzia”, FCG) for Small and Medium Enterprises (SMEs). Using an instrumental variable strategy, based on the eligibility for the FCG, we investigate whether the guarantee generated additional loans and/or lower interest rates to SMEs. Differently from previous literature, by focusing on the lending activity of a single large Italian lender we control for the probability of default as assessed by the bank’s internal rating model, and we examine whether the effects of the guarantee differ across firms belonging to different classes of risk. We find that guaranteed firms receive an additional amount of credit equal to 7-8 percent of their total banking exposure. We also estimate a reduction of about 50 basis points of interest rates applied to term loans granted to guaranteed firms. The effects on credit availability are concentrated in the intermediate class of solvent firms, i.e. those neither too safe nor too risky. Conversely, interest rate effects are present in all classes, but for the least risky firms. Finally, we observe a stronger impact of the guarantee for solvent firms with a longer relationship with the bank. This finding questions their ability to reduce financial frictions for very young firms.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132601177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corporate Leverage and Monetary Policy Effectiveness in the Euro Area","authors":"Simone Auer, M. Bernardini, Martina Cecioni","doi":"10.2139/ssrn.3887498","DOIUrl":"https://doi.org/10.2139/ssrn.3887498","url":null,"abstract":"Using country-industry level data and high-frequency identified monetary policy shocks, we find evidence of a positive but non-linear relationship between corporate leverage and the effectiveness of monetary policy in the euro area. More leveraged industries tend to increase their production more strongly after an expansionary monetary policy shock, pointing to a non-negligible role of financial frictions in the transmission mechanism. However, at high leverage ratios this positive relation becomes weaker and eventually inverts. This finding is consistent with recent theoretical studies arguing about the role of credit risk in dampening the financial accelerator channel.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126940774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gli stranieri in vacanza in Italia: prodotti turistici, destinazioni e caratteristiche dei viaggiatori [Foreigners on Holiday in Italy: Tourism Products, Destinations and Travelers’ Characteristics]","authors":"Andrea Filippone, M. Gallo, Patrizia Passiglia, V. Romano","doi":"10.2139/ssrn.3446735","DOIUrl":"https://doi.org/10.2139/ssrn.3446735","url":null,"abstract":"Italian Abstract: Il lavoro analizza le tendenze recenti dei viaggi degli stranieri in vacanza in Italia, distinguendo i diversi tipi di vacanza offerti, le relative destinazioni, classificate in base al grado di urbanizzazione e alla dotazione di patrimonio culturale, e le caratteristiche dei viaggiatori che vi si associano. Dal 2010 l’Italia ha registrato una dinamica degli arrivi internazionali per turismo leisure in linea con quella globale, grazie alla crescita sostenuta del turismo culturale che rappresenta ormai la metà dei pernottamenti degli stranieri in vacanza in Italia. Il patrimonio artistico e culturale del Paese esercita una grande attrazione e anche altri prodotti di rilievo, come le vacanze rurali e al mare, si sono arricchiti di tali contenuti. Poiché le grandi città d’arte sono il principale polo di attrazione, specialmente per il turista che giunge per la prima volta in Italia; ne consegue un aumento della concentrazione dei turisti nelle principali aree urbane nazionali. I turisti che decidono di tornare in Italia visitano con maggior frequenza i centri minori. English Abstract: [The work analyses the recent trends in trips by foreigners on holiday in Italy, distinguishing among different types of holidays offered and the related destinations, classified according to the degree of urbanization and the endowment of cultural heritage. It also studies the characteristics of the travellers who are associated with them. Since 2010, the trend in international arrivals for leisure tourism in Italy has been similar to the global trend thanks to the sustained growth of cultural tourism, which now represents half of all overnight stays by foreigners on holiday in Italy. The artistic and cultural heritage of the country exerts a great power of attraction and enriches Italy’s other relevant tourist products, such as rural and seaside vacations. Since the great cities of art are the main attraction, especially for those who visit Italy for the first time, it follows that there is an increase in the concentration of tourists in the main national urban areas. Tourists who decide to come back to Italy tend to visit smaller towns more frequently.]","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133173235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Turismo in Italia: numeri e potenziale di sviluppo [Tourism in Italy: Figures and Potential for Development]","authors":"A. Petrella, Roberto Torrini, Guglielmo Barone, E. Beretta, Emanuele Breda, Rita Cappariello, G. Ciaccio, L. Conti, Francesco David, P. Degasperi, Angela di Gioia, Alberto Felettigh, Andrea Filippone, Giovanna Firpo, Massimo Gallo, Paolo Guaitini, G. Papini, Patrizia Passiglia, Fabio Quintiliani, Giacomo Roma, V. Romano, D. Scalise","doi":"10.2139/ssrn.3446768","DOIUrl":"https://doi.org/10.2139/ssrn.3446768","url":null,"abstract":"The paper analyses the main trends and structural characteristics of the Italian tourism sector, which accounts for more than 5% of GDP and more than 6% of employment in the country. The study shows that in recent years the national tourist system has recorded a good growth performance, boosted by rapidly-expanding international demand for tourism services and by the competitive advantage of a unique artistic and cultural heritage. This has been associated with an upgrading of hotel accommodation in favour of higher-quality structures, and with an expansion of the non-hotel segment, partly fostered by the spread of the sharing economy and online intermediation. It is apparent, however, that some tourism potential still needs to be exploited i?½ especially in Southern Italy, where the sector seems relatively undersized i?½ and that potential risks are associated with the growing concentration of tourist flows in few locations. In order to cope with these problems, the paper argues that there should be a nationwide coordination of tourism policies, providing stable guidelines for the sector.","PeriodicalId":389704,"journal":{"name":"Bank of Italy Research Paper Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128966898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}