Time Series Forecasting: An Application to Balance Sheet

Detalhes bibliográficos
Autor(a) principal: Murillo, Federica Vieira Y de Araoz
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145479
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Time Series Forecasting: An Application to Balance SheetCGDICAAPbalance sheet projection riskforecastingtime seriesARIMAXInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Internal Capital Adequacy Assessment Process (ICAAP) provides a qualitative and quantitative assessment of capital risks to which banking institutions are exposed to in their activity. Caixa Geral de Dep´ositos (CGD) is a relevant player in the Portuguese banking system, and as such it has to perform an ongoing review of ICAAP exercise to evaluate its ability to identify, assess, mitigate and report on its risks. In order to properly quantify all the risks the institution is exposed to, several models need to be developed to help estimate the amount of capital that is needed to cover potential unexpected losses arising from each type of risk. Given the European and Portuguese guidelines these models also have to comply with certain requirements defined by Banco de Portugal, European Central Bank (ECB) and European Banking Authority (EBA) regarding ICAAP exercise. One of the risks CGD is exposed to is the risk of an unfavourable evolution of the main credit items in its Balance Sheet and as such, it is necessary to estimate the evolution of certain credit items (in terms of their volumes and spread rates). These estimations are needed for relevant segments such as housing credit, consumer and other credit, public sector credit, real estate activities credit, non-financial corporate credit and term and sight deposits. To estimate the evolution of these balance sheet items, a robust and reliable methodology must be applied, so that it can truly help strategic decision-making process over a horizon period of three years and the appropriate amount of capital can be allocated. At CGD, Balance Sheet credit volumes and spread rates had been being estimated through multiple linear regressions to which macroeconomic indicators are added as explanatory variables. The problem with this methodology, is that these type of dependent and explanatory financial variables are usually in the form of time series, indicating the existence of correlation between any observation and the previous one, meaning that there is dependence on the past historical information. Applying multiple linear regressions to this type of data leads to poor statistical results and to the non-compliance of all the statistical assumptions linear regressions must respect. Within this context, the need to turn to a more adequate and robust methodology became more evident and time series forecasting appeared to be the so long needed solution that would allow to reach reliable statistical results. Time series forecasting is commonly used in economics and finance, denoting a robust technique to predict macroeconomic variables representing a feasible approach to apply to estimate CGD’s main credit volumes and spread rates of the balance sheet. In this project, we investigate the estimation of Balance Sheet credit volumes and spreads rates using time series forecasting aiming to assess the models suitability to quantify the risk of unfavourable balance sheet evolution of the main credit segments. The models proposed for this purpose, are the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) models. The results obtained proved to have robust statistical results and high performance, which were verified by analysing residuals statistical behaviour and key performance indicators such as the Mean Squared Error (MSE) and the Akaike Information Criterion (AIC) of the final models selected for each target variable.Pinheiro, Flávio Luís PortasRUNMurillo, Federica Vieira Y de Araoz2022-11-14T16:07:34Z2022-10-242022-10-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145479TID:203097351enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:25:53Zoai:run.unl.pt:10362/145479Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:06.031183Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Time Series Forecasting: An Application to Balance Sheet
title Time Series Forecasting: An Application to Balance Sheet
spellingShingle Time Series Forecasting: An Application to Balance Sheet
Murillo, Federica Vieira Y de Araoz
CGD
ICAAP
balance sheet projection risk
forecasting
time series
ARIMAX
title_short Time Series Forecasting: An Application to Balance Sheet
title_full Time Series Forecasting: An Application to Balance Sheet
title_fullStr Time Series Forecasting: An Application to Balance Sheet
title_full_unstemmed Time Series Forecasting: An Application to Balance Sheet
title_sort Time Series Forecasting: An Application to Balance Sheet
author Murillo, Federica Vieira Y de Araoz
author_facet Murillo, Federica Vieira Y de Araoz
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Murillo, Federica Vieira Y de Araoz
dc.subject.por.fl_str_mv CGD
ICAAP
balance sheet projection risk
forecasting
time series
ARIMAX
topic CGD
ICAAP
balance sheet projection risk
forecasting
time series
ARIMAX
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2022
dc.date.none.fl_str_mv 2022-11-14T16:07:34Z
2022-10-24
2022-10-24T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145479
TID:203097351
url http://hdl.handle.net/10362/145479
identifier_str_mv TID:203097351
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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