With political and economic scenarios changing at an ever faster pace, it is necessary to understand the potential effects on asset prices. Today, the topic of rising inflation in the US as well as in the Eurozone, although still considered temporary by central banks, confronts us with the "unexpected risk" of a deviation from the baseline scenario. This implies the risk of having an aggressive monetary policy in the US, in a restrictive direction, therefore harmful to the financial markets. In this context, the question arises: is it possible to contemplate these events beforehand and act in good time? The answer is Yes and good risk management practices are important, using stress testing / scenario analysis techniques to accompany risk measures such as VaR and Expected Shortfall. Implementing this concept, through the implementation of stress test / scenario analysis - Bloomberg Economics Forecast Models® and Bloomberg Factor Models® - the present work seeks to consider plausible adverse scenarios that may arise and to assess the related impacts in terms of portfolio. The final aim is to improve the information set for the investor, allowing him to avoid potential market falls, as far as possible, that could prevent him from achieving his investment objectives.
{"title":"Beyond VaR and Expected Shortfall: The Stress Testing/Scenario Analysis approach for protecting the investors in the post-Covid19 era","authors":"G. Macchia","doi":"10.47473/2020rmm0109","DOIUrl":"https://doi.org/10.47473/2020rmm0109","url":null,"abstract":"With political and economic scenarios changing at an ever faster pace, it is necessary to understand the potential effects on asset prices. Today, the topic of rising inflation in the US as well as in the Eurozone, although still considered temporary by central banks, confronts us with the \"unexpected risk\" of a deviation from the baseline scenario. This implies the risk of having an aggressive monetary policy in the US, in a restrictive direction, therefore harmful to the financial markets. In this context, the question arises: is it possible to contemplate these events beforehand and act in good time? The answer is Yes and good risk management practices are important, using stress testing / scenario analysis techniques to accompany risk measures such as VaR and Expected Shortfall. Implementing this concept, through the implementation of stress test / scenario analysis - Bloomberg Economics Forecast Models® and Bloomberg Factor Models® - the present work seeks to consider plausible adverse scenarios that may arise and to assess the related impacts in terms of portfolio. The final aim is to improve the information set for the investor, allowing him to avoid potential market falls, as far as possible, that could prevent him from achieving his investment objectives.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129224091","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}
Luca Bartolucci, Guido Luciano Genero, Maurizio Pierigè, F. Verachi
In the context of the rapid changes that have occurred in recent years, characterized by veritable 'black swans' such as the COVID-19 pandemic and extreme weather events that are occurring with increasing frequency, the issue of climate change has come into the focus of banking regulators and supervisors. Therefore banking institutions, if they are subject to the Single Supervisory Mechanism, have been called upon to develop (and, subsequently, to integrate into their business practices) methodologies for the identification, quantification and management of such risks, mainly under the profiles of: - Transition Risk, associated with policies undertaken by governments to foster climate change mitigation and adaptation; - Physical Risk, associated with the occurrence of extreme climatic events and its impact on the bank's assets. This paper analyzes one of the most significant hazards within the Physical Risk domain, which is Flood Risk. The measurement is focused on the prospective evolution of the flood events on a portfolio of mortgages secured by residential properties. The impact of this risk driver is subsequently reflected through the movement of appropriate transmission mechanisms on the LGD and PD parameters relating to the exposures in the scope. Finally, the effect on loan adjustments is provided, by recalculating the expected losses that result from the stressed projections. The flood risk projection is executed on a long-term timeframe, developing over 3 climate scenarios up to 2050. The choice of this hazard is due to its relevance in terms of frequency of events and harmfulness, a relevance that is confirmed by its inclusion in both the top-down climate stress testing exercises carried out by the ECB and in the bottom-up climate stress testing exercise promoted by the ECB itself in 2022 and carried out by the SSM Banks. A comprehensive simulation framework, structured as follows, is then presented: - a macro-climate scenario simulation engine; - the downscaling of these scenarios to obtain localized climate effects on individual properties; - the transmission of these effects into a depreciation formula for the individual property; - the LGD stress associated with the devaluation of the collateral property, and the PD stress that goes along with it, obtained by correlation.
{"title":"Estimation of Flood Risk on a residential mortgages portfolio","authors":"Luca Bartolucci, Guido Luciano Genero, Maurizio Pierigè, F. Verachi","doi":"10.47473/2020rmm0110","DOIUrl":"https://doi.org/10.47473/2020rmm0110","url":null,"abstract":"In the context of the rapid changes that have occurred in recent years, characterized by veritable 'black swans' such as the COVID-19 pandemic and extreme weather events that are occurring with increasing frequency, the issue of climate change has come into the focus of banking regulators and supervisors. Therefore banking institutions, if they are subject to the Single Supervisory Mechanism, have been called upon to develop (and, subsequently, to integrate into their business practices) methodologies for the identification, quantification and management of such risks, mainly under the profiles of: - Transition Risk, associated with policies undertaken by governments to foster climate change mitigation and adaptation; - Physical Risk, associated with the occurrence of extreme climatic events and its impact on the bank's assets. This paper analyzes one of the most significant hazards within the Physical Risk domain, which is Flood Risk. The measurement is focused on the prospective evolution of the flood events on a portfolio of mortgages secured by residential properties. The impact of this risk driver is subsequently reflected through the movement of appropriate transmission mechanisms on the LGD and PD parameters relating to the exposures in the scope. Finally, the effect on loan adjustments is provided, by recalculating the expected losses that result from the stressed projections. The flood risk projection is executed on a long-term timeframe, developing over 3 climate scenarios up to 2050. The choice of this hazard is due to its relevance in terms of frequency of events and harmfulness, a relevance that is confirmed by its inclusion in both the top-down climate stress testing exercises carried out by the ECB and in the bottom-up climate stress testing exercise promoted by the ECB itself in 2022 and carried out by the SSM Banks. A comprehensive simulation framework, structured as follows, is then presented: - a macro-climate scenario simulation engine; - the downscaling of these scenarios to obtain localized climate effects on individual properties; - the transmission of these effects into a depreciation formula for the individual property; - the LGD stress associated with the devaluation of the collateral property, and the PD stress that goes along with it, obtained by correlation.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127860301","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}
G. Birindelli, Michelangelo Bruno, A. Citterio, Umberto Fuso, Guido Luciano Genero, Andrea Magurano
{"title":"Banks' governance and risk management frameworks: how to integrate ESG and climate risks","authors":"G. Birindelli, Michelangelo Bruno, A. Citterio, Umberto Fuso, Guido Luciano Genero, Andrea Magurano","doi":"10.47473/2020rmm0103","DOIUrl":"https://doi.org/10.47473/2020rmm0103","url":null,"abstract":"","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123701916","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}
The simplification and assumptions that models must necessarily employ sometimes come at the cost of accuracy and structural integrity under stress. This exposes the bank to model risk: the risk of economic or reputation loss due to errors in the development, implementation or use of models.
{"title":"A Gentle Introduction to Model Risk Quantification in Commercial Banking","authors":"Tiziano Bellini","doi":"10.47473/2020rmm0101","DOIUrl":"https://doi.org/10.47473/2020rmm0101","url":null,"abstract":"The simplification and assumptions that models must necessarily employ sometimes come at the cost of accuracy and structural integrity under stress. This exposes the bank to model risk: the risk of economic or reputation loss due to errors in the development, implementation or use of models.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436164","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":"Capital adequacy in banks and sustainable finance: the Green Supporting Factor","authors":"M. Intonti, Annalisa Ceo, G. Ferri","doi":"10.47473/2020rmm0105","DOIUrl":"https://doi.org/10.47473/2020rmm0105","url":null,"abstract":"","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125242889","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}
Carlo Frazzei, Davide Segantin, Patrizia Dolci, Alessandro Garufi, S. Zavattari, Ilaria Giommaroni, Andrea Rodonò
In light of the finalization of the new regulatory framework for market with the adoption of the FRTB at EU level through the publication of CRR III, financial institutions are consolidating the implementations aimed to comply with the new regulatory requirements. The main purpose of this article is to analyze how banks are preparing for the go-live of IMA FRTB reporting – expected to be in January 2024 – focusing on the challenges that they are facing especially in terms of model transformations. In particular, an in-depth analysis will be carried out on the main methodological issues of the new regulatory context technicalities,in order to provide guidelines and market best practices on the Internal Model Approach (IMA) topics shared between Front Office, Risk Management as well as Control Structures.
{"title":"Fundamental review of the trading book - Stato dell’arte sulle implementazioni dell’Internal Model Approach","authors":"Carlo Frazzei, Davide Segantin, Patrizia Dolci, Alessandro Garufi, S. Zavattari, Ilaria Giommaroni, Andrea Rodonò","doi":"10.47473/2020rmm0095","DOIUrl":"https://doi.org/10.47473/2020rmm0095","url":null,"abstract":"In light of the finalization of the new regulatory framework for market with the adoption of the FRTB at EU level through the publication of CRR III, financial institutions are consolidating the implementations aimed to comply with the new regulatory requirements. The main purpose of this article is to analyze how banks are preparing for the go-live of IMA FRTB reporting – expected to be in January 2024 – focusing on the challenges that they are facing especially in terms of model transformations. In particular, an in-depth analysis will be carried out on the main methodological issues of the new regulatory context technicalities,in order to provide guidelines and market best practices on the Internal Model Approach (IMA) topics shared between Front Office, Risk Management as well as Control Structures.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"78 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113961171","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}
An Inflation-Indexed Swap (IIS) is a derivative in which, at every payment date, the counterparties swap an inflation rate with a fixed rate. For the calculation of the Inflation Leg cash flows it is necessary to build a mathematical model suitable for the Consumer Price Index (CPI) projection. For this purpose, quants typically start by using market quotes for the Zero-Coupon swaps in order to derive the future trend of the inflation index, together with a seasonality model for capturing the typical periodical effects. In this study, we propose a forecasting model for inflation seasonality based on a Long Short Term Memory (LSTM) network: a deep learning methodology particularly useful for forecasting purposes. The CPI predictions are conducted using a FinTech paradigm, but in respect of the traditional quantitative finance theory developed in this research field. The paper is structured according to the following sections: the first two parts illustrate the pricing methodologies for the most popular IIS: the Zero Coupon Inflation-Indexed Swap (ZCIIS) and the Year-on-Year Inflation-Indexed Swap (YYIIS); section 3 deals with the traditional standard method for the forecast of CPI values (trend + seasonality), while section 4 describes the LSTM architecture, and section 5 focuses on CPI projections, also called inflation bootstrap. Then section 6 describes a robust check, implementing a traditional SARIMA model in order to improve the interpretation of the LSTM outputs; finally, section 7 concludes with a real market case, where the two methodologies are used for computing the fair-value for a YYIIS and the model risk is quantified.
{"title":"Deep Learning for seasonality modelling in Inflation-Indexed Swap pricing","authors":"P. Giribone, D. Martelli","doi":"10.47473/2020rmm0099","DOIUrl":"https://doi.org/10.47473/2020rmm0099","url":null,"abstract":"An Inflation-Indexed Swap (IIS) is a derivative in which, at every payment date, the counterparties swap an inflation rate with a fixed rate. For the calculation of the Inflation Leg cash flows it is necessary to build a mathematical model suitable for the Consumer Price Index (CPI) projection. For this purpose, quants typically start by using market quotes for the Zero-Coupon swaps in order to derive the future trend of the inflation index, together with a seasonality model for capturing the typical periodical effects. In this study, we propose a forecasting model for inflation seasonality based on a Long Short Term Memory (LSTM) network: a deep learning methodology particularly useful for forecasting purposes. The CPI predictions are conducted using a FinTech paradigm, but in respect of the traditional quantitative finance theory developed in this research field. The paper is structured according to the following sections: the first two parts illustrate the pricing methodologies for the most popular IIS: the Zero Coupon Inflation-Indexed Swap (ZCIIS) and the Year-on-Year Inflation-Indexed Swap (YYIIS); section 3 deals with the traditional standard method for the forecast of CPI values (trend + seasonality), while section 4 describes the LSTM architecture, and section 5 focuses on CPI projections, also called inflation bootstrap. Then section 6 describes a robust check, implementing a traditional SARIMA model in order to improve the interpretation of the LSTM outputs; finally, section 7 concludes with a real market case, where the two methodologies are used for computing the fair-value for a YYIIS and the model risk is quantified.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130231820","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}
The World Bank data confirm that the recovery scenario will be different depending on the type of nation, the fundamentals of its economy, etc.. The Bank of Italy expects a growth of more than 4% for Italy at the end of 2021. The Italian banking system has shown great flexibility in dealing with the coronavirus emergency, taking a completely different form from the last in 2008 recession, when credit institutions were part of the problem. With their new social role, today in fact they are leading players. The health of the banking sector has also changed compared to 2008, with a stronger capital position, underlying the substantial resilience of the ecosystem and a more advanced expertise in NPL management. The role of the banks operating in Italy has been and will be to support firms, households and the growth of the economy with the sound and prudent distribution of credit, the offer of modern and efficient payment services thanks also to new technologies, business advice to companies for the development and internationalization. A clear evolution is opening up for banks in post-Covid towards digital business with a growing commitment in terms of investments in information technology.
{"title":"Covid-19 crisis and its impacts on the economic and financial sector","authors":"C. Giliberto","doi":"10.47473/2020rmm0098","DOIUrl":"https://doi.org/10.47473/2020rmm0098","url":null,"abstract":"The World Bank data confirm that the recovery scenario will be different depending on the type of nation, the fundamentals of its economy, etc.. The Bank of Italy expects a growth of more than 4% for Italy at the end of 2021. The Italian banking system has shown great flexibility in dealing with the coronavirus emergency, taking a completely different form from the last in 2008 recession, when credit institutions were part of the problem. With their new social role, today in fact they are leading players. The health of the banking sector has also changed compared to 2008, with a stronger capital position, underlying the substantial resilience of the ecosystem and a more advanced expertise in NPL management. The role of the banks operating in Italy has been and will be to support firms, households and the growth of the economy with the sound and prudent distribution of credit, the offer of modern and efficient payment services thanks also to new technologies, business advice to companies for the development and internationalization. A clear evolution is opening up for banks in post-Covid towards digital business with a growing commitment in terms of investments in information technology.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133725948","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}
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