The measurement of operational risk has surely been one of the biggest challenges for banks worldwide. Most banks worldwide have opted for a value-at-risk (VaR) approach, based on the success achieved with market risk, to measure and quantify operational risk. The problem banks have is that they do not always find it difficult to calculate this VaR figure, as there are numerous mathematical and statistical methods and models that can calculate VaR, but they struggle to understand and interpret the values that are produced by VaR models and methods. Senior management and normal staff do not always understand how these VaR values will impact their decision-making and they do not always know how to incorporate these values in their day-to-day management of the bank. This study therefore aims to explain and discuss the calculation of VaR for operational risk as well as the factors that influence this figure, and then also to discuss how this figure is managed and the impact that it has on the management of a bank. The main goal of this study is then to explain the management of VaR for operational risk in order to understand how it can be incorporated in the overall management of a bank. The methodology used includes a literature review, in-depth interviews and a case study on a South African Retail Bank to determine and evaluate some of the most renowned methods for calculating VaR for operational risk. The first objective of this study is to define operational risk and all its elements in order to distinguish it from all the other risks the banking industry faces and to better understand the management thereof. It is the view of this study that it will be impossible to manage and measure operational risk if it is not clearly defined, and it is therefore important to have a clear and understandable definition of operational risk. The second objective is to establish an operational risk management process that will ensure a structured approach to the management of operational risk, by focusing on the different phases of operational risk. The process discussed by this study is a combination of some of the most frequent used processes by international banks, and is intended to guide the reader in terms of the steps required for managing operational risk. The third objective of this study is to discuss and explain the qualitative factors that play a role in the management of operational risk, and to determine where these factors fit into the operational risk process and the role they play in calculating the VaR for operational risk. These qualitative factors include, amongst others, key risk indicators (KRIs), risk and control self-assessments and the tracking of operational losses. The fourth objective is to identify and evaluate the quantitative factors that play a role in the management of operational risk, to distinguish these factors from the qualitative factors, and also to determine where these factors fit into the operational risk management process and the role they play in calculating VaR for operational risk. Most of these quantitative factors are prescribed by the Base1 Committee by means of its New Capital Accord, whereby this new framework aims to measure operational risk in order to determine the amount of capital needed to safeguard a bank against operational risk. The fifth objective is to discuss and explain the calculation of VaR for operational risk by means of discussing all the elements of this calculation. This study mainly bases its discussion on the loss distribution approach (LDA), where the frequency and severity of operational loss events are convoluted by means of Monte Carlo simulations. This study uses real data obtained from a South African Retail Bank to illustrate this calculation on a practical level. The sixth and final objective of this study is to explain how VaR for operational risk is interpreted in order for management to deal with it and make proper management decisions based on it. The above-mentioned discussion is predominantly based on the two types of capital that are influenced by VaR for operational risk. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2007.

The measurement of operational risk has surely been one of the biggest challenges for banks worldwide. Most banks worldwide have opted for a value-at-risk (VaR) approach, based on the success achieved with market risk, to measure and quantify operational risk. The problem banks have is that they do not always find it difficult to calculate this VaR figure, as there are numerous mathematical and statistical methods and models that can calculate VaR, but they struggle to understand and interpret the values that are produced by VaR models and methods. Senior management and normal staff do not always understand how these VaR values will impact their decision-making and they do not always know how to incorporate these values in their day-to-day management of the bank. This study therefore aims to explain and discuss the calculation of VaR for operational risk as well as the factors that influence this figure, and then also to discuss how this figure is managed and the impact that it has on the management of a bank. The main goal of this study is then to explain the management of VaR for operational risk in order to understand how it can be incorporated in the overall management of a bank. The methodology used includes a literature review, in-depth interviews and a case study on a South African Retail Bank to determine and evaluate some of the most renowned methods for calculating VaR for operational risk. The first objective of this study is to define operational risk and all its elements in order to distinguish it from all the other risks the banking industry faces and to better understand the management thereof. It is the view of this study that it will be impossible to manage and measure operational risk if it is not clearly defined, and it is therefore important to have a clear and understandable definition of operational risk. The second objective is to establish an operational risk management process that will ensure a structured approach to the management of operational risk, by focusing on the different phases of operational risk. The process discussed by this study is a combination of some of the most frequent used processes by international banks, and is intended to guide the reader in terms of the steps required for managing operational risk. The third objective of this study is to discuss and explain the qualitative factors that play a role in the management of operational risk, and to determine where these factors fit into the operational risk process and the role they play in calculating the VaR for operational risk. These qualitative factors include, amongst others, key risk indicators (KRIs), risk and control self-assessments and the tracking of operational losses. The fourth objective is to identify and evaluate the quantitative factors that play a role in the management of operational risk, to distinguish these factors from the qualitative factors, and also to determine where these factors fit into the operational risk management process and the role they play in calculating VaR for operational risk. Most of these quantitative factors are prescribed by the Base1 Committee by means of its New Capital Accord, whereby this new framework aims to measure operational risk in order to determine the amount of capital needed to safeguard a bank against operational risk. The fifth objective is to discuss and explain the calculation of VaR for operational risk by means of discussing all the elements of this calculation. This study mainly bases its discussion on the loss distribution approach (LDA), where the frequency and severity of operational loss events are convoluted by means of Monte Carlo simulations. This study uses real data obtained from a South African Retail Bank to illustrate this calculation on a practical level. The sixth and final objective of this study is to explain how VaR for operational risk is interpreted in order for management to deal with it and make proper management decisions based on it. The above-mentioned discussion is predominantly based on the two types of capital that are influenced by VaR for operational risk. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2007.

This thesis examines the ethnobotany, physiology, anatomy, and nutritional value of edible lodgepole pine (Pinus contorta Douglas ex Louden var. latifolia Engelm. ex S. Watson ) cambium. Many First Peoples of the Pacific Northwest historically used lodgepole pine cambium. It was so popular among interior First Peoples of British Columbia that it was considered a universal food. Even though harvesting and consumption of pine cambium is diminishing in popularity today, I was able to learn from some Tsilhqot‟in First Peoples on Redstone Reserve who had prior experience with pine cambium. Nutritional analysis of lodgepole pine cambium revealed the tissues to be high in protein and sugar as well as a suite of micronutrients, which contribute to overall immunity and electrolyte balance. While lodgepole pine cambium is considered a sweet, seasonal treat by many First Peoples it is evident through my analysis that there are added nutritional benefits beyond the pleasure of consumption. This research illustrates an important case study of an endangered traditional food, which can be integrated into modern diets today. It also explores the integration of multiple disciplines of knowledge to inform this subject matter, providing multiple dimensions to understanding cambium production, timing of harvest, and benefit of consumption. / Graduate

傳統用變異數─共變異數法來衡量投資組合的風險，當投資組合的資產項目很多時，則影響此投資組合價值變動的因素來自不同的市場，而每個市場又可能不只包含一個影響因子，所以，影響這個投資組合變動的因子可能非常複雜。不僅如此，當投資組合的資產項目很多時，則此投資組合的共變異數矩陣會變的十分龐大，在計算此共變異數矩陣會產生一些問題，如共變異矩陣可能為負，以及線性重合的問題，因此本研究希望能應用主成份分析 (Principal Component Analysis)，解決上述的問題，提供一個快速又不失準確的方法，來衡量投資組合的風險。 本研究的實證結果發現，主成份分析法計算出來的風險值，在95%信心水準下，若移動窗口為60天，其穿透次數比簡單加權移動平均法多一次，與指數加權移動平均法有相同的預測能力，若移動窗口為250天，其穿透次數比簡單加權移動平均法多一次，比指數加權移動平均法多五次；在99%信心水準下，若移動窗口為60天，與簡單加權移動平均法和指數加權移動平均法有相同的預測能力，若移動窗口為250天，其穿透次數比簡單加權移動平均法多一次，比指數加權移動平均法多三次。由此可知，運用主成份分析法來計算投資組合的風險值，不僅可以解決共變異數矩陣過於龐大所帶來的問題，且在預測投資組合的風險值上，也有很好的結果。

Univ., Diss.--St. Gallen, 2004.

Thèse (Ph. D.)--Université Laval, 2007. / Bibliogr.: f. 228-238. Publié aussi en version électronique dans la Collection Mémoires et thèses électroniques.

Diss.--Universität Frankfurt am Main, 1987.

Submitted by ALEXSON FILGUEIRAS DUTRA null (alexsonbrejo@hotmail.com) on 2017-12-06T13:54:46Z No. of bitstreams: 1 Tese_Alexson_Filgueiras_Dutra.pdf: 1878635 bytes, checksum: f8d5902f17d40824b2fda4baa95a9ac2 (MD5) / Submitted by ALEXSON FILGUEIRAS DUTRA null (alexsonbrejo@hotmail.com) on 2017-12-11T18:47:11Z No. of bitstreams: 1 Tese_Alexson_Filgueiras_Dutra.pdf: 1878635 bytes, checksum: f8d5902f17d40824b2fda4baa95a9ac2 (MD5) / Approved for entry into archive by Alexandra Maria Donadon Lusser Segali null (alexmar@fcav.unesp.br) on 2017-12-13T10:07:01Z (GMT) No. of bitstreams: 1 dutra_af_dr_jabo.pdf.pdf: 1878635 bytes, checksum: f8d5902f17d40824b2fda4baa95a9ac2 (MD5) / Made available in DSpace on 2017-12-13T10:07:01Z (GMT). No. of bitstreams: 1 dutra_af_dr_jabo.pdf.pdf: 1878635 bytes, checksum: f8d5902f17d40824b2fda4baa95a9ac2 (MD5) Previous issue date: 2017-11-16 / O selênio (Se) é um elemento essencial para os seres humanos, no entanto, em plantas é considerado benéfico. O Se pode atuar como antioxidante de células vegetais, melhorando o crescimento das plantas. A aplicação de Se em culturas para fins de biofortificação tem sido uma estratégia eficaz para aumentar o fornecimento do elemento na dieta da população. Entretanto, para ter sucesso é fundamental estudar a aplicação de Se, conhecendo a fonte e concentração adequadas, de forma que não prejudique o crescimento, os aspectos nutricionais, bioquímicos e produtivos da planta. Assim, objetivou-se avaliar o efeito de fontes e concentrações de Se na biofortificação e aspectos nutricionais, fisiológicos e enzimáticos de plantas de couve-flor. O estudo foi realizado em ambiente protegido, com plantas de couve-flor cultivadas em sistema hidropônico, sob concentrações de Se (0, 5, 15, 30 e 60 μmol L-1) nas fontes selenato e selenito de sódio. Os tratamentos foram organizados em delineamento inteiramente casualizado, em esquema fatorial 5 x 2, com quatro repetições. Verificou-se que a aplicação de Se na concentração de 5 μmol L-1 na solução nutritiva promoveu crescimento das plantas e teor do elemento na inflorescência da couve-flor foi superior ao limite considerado adequado pelo Codex Alimentarius. O fornecimento de Se, na fonte selenato, pode ter contribuído para inibir a peroxidação lipídica e a produção de espécies reativas de oxigênio, uma vez que com esse elemento houve melhora na taxa de fotossíntese e na regulação da atividade enzimática das plantas. Em altas concentrações, o Se fornecido principalmente na fonte selenito, afeta negativamente o equilibrio nutricional das plantas e, em consequência, o crescimento, os apectos fisiológicos, enzimáticos e produtivos da couve-flor, depreciando a qualidade do produto.

Submitted by Eliza Barboza (agro_eliza@hotmail.com) on 2018-06-08T14:26:27Z No. of bitstreams: 1 Eliza Barboza TESE versão.pdf: 1545927 bytes, checksum: ac8fd976ca18df84595a993ec2a3e701 (MD5) / Approved for entry into archive by Alexandra Maria Donadon Lusser Segali null (alexmar@fcav.unesp.br) on 2018-06-12T16:57:44Z (GMT) No. of bitstreams: 1 barboza_e_dr_jabo.pdf: 1545927 bytes, checksum: ac8fd976ca18df84595a993ec2a3e701 (MD5) / Made available in DSpace on 2018-06-12T16:57:44Z (GMT). No. of bitstreams: 1 barboza_e_dr_jabo.pdf: 1545927 bytes, checksum: ac8fd976ca18df84595a993ec2a3e701 (MD5) Previous issue date: 2018-04-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O objetivo deste trabalho foi avaliar concentrações de selênio (selenato e selenito de sódio) no crescimento, produção e biofortificação de duas cultivares de repolho. Para isso foram realizados dois experimentos em casa de vegetação, em sistema hidropônico, no período de julho a novembro de 2014, na UNESP, campus Jaboticabal-SP. Os experimentos diferiram conforme a fonte de selênio (Se) utilizada (selenato e selenito de sódio). Em ambos os experimentos, avaliaram-se dois fatores, sendo: duas cultivares de repolho (‘Fênix’ e 'Red Dinasty’) e cinco concentrações de Se (0, 5, 15, 30 e 60 µmol L-1) na solução nutritiva. O delineamento experimental utilizado foi o de blocos casualizados, com esquema fatorial 2 x 5 e quatro repetições. As variáveis analisadas foram o teor e acúmulo de Se na raiz e cabeça de repolho, área, diâmetro, comprimento e densidade de raiz, produtividade de repolho, massa seca de raiz, caule e cabeça de repolho e o teor e acúmulo de nitrogênio, fósforo e enxofre na cabeça de repolho. As fontes selenato e selenito promoveram o crescimento das raízes de repolho devido aos aumentos verificados em área, diâmetro, comprimento e densidade de raiz, em baixas concentrações de Se aplicadas. O aumento da produção de massa seca de raiz provavelmente foi devido o maior crescimento da raiz, que foi influenciado pelo fornecimento de Se. A massa seca do caule foi diferente apenas entre as cultivares de repolho com aplicação de selenato ou selenito. O teor de nitrogênio apresentou diferenças apenas entre as cultivares quando utilizada a fonte selenito. Ambas as fontes de Se também proporcionaram aumentos na absorção de fósforo e enxofre quando baixas concentrações de Se foram fornecidas. Observou-se que a aplicação de até 16,77 e 7,54 µmol L-1 de Se com as fontes selenato e selenito, respectivamente, proporcionaram os maiores incrementos na produtividade do repolho, e mesmo com a redução da produtividade com concentrações acima destas, os valores foram maiores que o tratamento controle. Isso indica que o Se foi benéfico ao repolho, pois não reduziu a sua produção. Verificou-se que a biofortificação foi eficiente para ambas as cultivares de repolho, que absorveram e acumularam Se na cabeça de repolho em função do incremento de Se, tanto com selenato como selenito. / The objective of this research was to evaluate concentrations of selenium (sodium selenate and selenite) in the growth, production and biofortification of two cabbage cultivars. Two experiments were carried out in greenhouse under hydroponic system, from July to November, 2014, at UNESP, Jaboticabal-SP campus. The experiments differed according to the source of selenium (Se) used (sodium selenate and selenite). In both experiments two factors were evaluated: two cabbage cultivars ('Fênix' and 'Red Dinasty') and five concentrations of Se (0, 5, 15, 30 and 60 μmol L-1) in the nutrient solution. The experimental was a randomized block design, with 2 x 5 factorial and four replications. The variables analyzed were the content and accumulation of Se in the root and shoot cabbage, evaluating area, diameter, length, density and dry mass in the root, cabbage yield, stem and cabbage head, and the content accumulation of nitrogen, phosphorus and sulfur in the cabbage shoot. Selenate and selenite sources promoted the growth of cabbage roots due to increases in area, diameter, length and root density at low concentrations of Se applied. The increase in root dry mass production was probably due to higher root growth, which was influenced by the supply of Se. The dry mass of the stem was different only between the cabbage cultivars with application of selenate or selenite. The nitrogen content presented differences only among the cultivars when the selenite source was used. Both S sources also provided increases in phosphorus and sulfur absorption when low concentrations of if were provided. It was observed that the application of up to 16,77 and 7,54 μmol L-1 of Se with selenate and selenite sources, respectively, provided the greatest increases in cabbage yield, and even with the reduction of productivity with concentrations above these, the values were higher than the control treatment. These indicate that the Se was beneficial to the cabbage, since it did not reduce its production. It was verified that biofortification was efficient for both cabbage cultivars, which absorbed and accumulated Se in the cabbage head as a function of the increase of Se, with both selenate and selenite.

[pt] A dissertação apresenta uma proposta de modelo de fatores fundamentalista, de base diária, como uma alternativa mais parcimoniosa do que as tradicionais no cálculo do Value-at- Risk. Surpreendidos por prejuízos causados geralmente por elevada exposição ao risco de mercado, bancos e instituições vêm dedicando atenção especial ao aprimoramento de técnicas de gerenciamento de risco. O estudo relembra conceitos básicos de teoria de carteiras, em particular os modelos de equilíbrio de precificação de ativos, como o CAPM e o APT. Em seguida, a metodologia do Value-at- Risk é abordada, bem como os principais modelos de volatilidade utilizados na sua implementação. Os modelos multi-fatoriais, como os desenvolvidos pela Barra International, são apresentados como simplificadores do processo de estimação da matriz de covariâncias dos ativos de uma carteira. Finalmente, um modelo diário de fatores é estimado e aplicado no cálculo do VaR de uma carteira de ações brasileiras. Os resultados o qualificam como uma ferramenta consistente, eficiente e estruturada no cálculo e análise do risco financeiro.