Claudio Albanese
- Courses6
- Reviews8
- School: University of Toronto
- Campus: St. George Campus
- Department: Mathematics
- Email address: Join to see
- Phone: Join to see
-
Location:
Toronto, ON - Dates at University of Toronto: May 2004 - December 2007
- Office Hours: Join to see
Biography
University of Toronto St. George Campus - Mathematics
Experience
Carador PLC
Non Executive Director
Claudio worked at Carador PLC as a Non Executive Director
Global Valuation Limited
CEO
Global Valuation London
Imperial College London
Professor of Mathematical Finance
Claudio worked at Imperial College London as a Professor of Mathematical Finance
King's College London
Visiting Professor of Financial Mathematics
Claudio worked at King's College London as a Visiting Professor of Financial Mathematics
IXIS CIB
consultant
Claudio worked at IXIS CIB as a consultant
Education
Eidgenössische Technische Hochschule Zürich
PhD
Physics
Publications
Coherent Global Market Simulations
Quantitative Finance
Valuing, hedging and securitizing counterparty credit risk involves analyzing large portfolios of netting sets over time horizons spanning decades. Theory dictates that the simulation measure should be coherent, i.e. arbitrage free. It should also be used consistently both to simulate and to value all instruments. This article describes the Mathematics and the software architecture of a risk system that accomplishes this task. The usage pattern is based on an offline phase to calibrate and generate model libraries. Valuation and simulation algorithms are planned offline with portfolio specific optimizations. The interactive user-driven phase includes a coherent global market simulation taking a few minutes and a real time data exploration phase with quick response time. Data exploration includes 3-dimensional risk visualization of portfolio loss distributions and sensitivities. It also includes risk resolution capability for outliers from the global portfolio level down to the single instrument level and hedge ratio optimization. The network bottleneck is bypassed by using heterogeneous boards with acceleration. The memory bottleneck is avoided at the algorithmic level by adapting the mathematical framework to revolve around a handful of compute-bound algorithms.
Coherent Global Market Simulations
Quantitative Finance
Valuing, hedging and securitizing counterparty credit risk involves analyzing large portfolios of netting sets over time horizons spanning decades. Theory dictates that the simulation measure should be coherent, i.e. arbitrage free. It should also be used consistently both to simulate and to value all instruments. This article describes the Mathematics and the software architecture of a risk system that accomplishes this task. The usage pattern is based on an offline phase to calibrate and generate model libraries. Valuation and simulation algorithms are planned offline with portfolio specific optimizations. The interactive user-driven phase includes a coherent global market simulation taking a few minutes and a real time data exploration phase with quick response time. Data exploration includes 3-dimensional risk visualization of portfolio loss distributions and sensitivities. It also includes risk resolution capability for outliers from the global portfolio level down to the single instrument level and hedge ratio optimization. The network bottleneck is bypassed by using heterogeneous boards with acceleration. The memory bottleneck is avoided at the algorithmic level by adapting the mathematical framework to revolve around a handful of compute-bound algorithms.
Capital and Funding
Risk Magazine
Banking operations are being rewired around a pair of KVA/FVA metrics which quantify market incompleteness, i.e. the impossibility of perfect replication. The FVA is the cost of funding of debt liabilities while the KVA is the risk adjustment for equity liabilities, also called cost of capital. The two metrics are intertwined with each other, since equity capital is itself a source of funding, fungible with debt financing. In this paper, we define the KVA and FVA metrics in terms of projections for Economic Capital and costs of funding. If implemented within the proper accounting framework, KVA/FVA mark-to-market leads to reporting rules for earnings which are both informative and useful to devise a sustainable strategy for dividend payments.
Coherent Global Market Simulations
Quantitative Finance
Valuing, hedging and securitizing counterparty credit risk involves analyzing large portfolios of netting sets over time horizons spanning decades. Theory dictates that the simulation measure should be coherent, i.e. arbitrage free. It should also be used consistently both to simulate and to value all instruments. This article describes the Mathematics and the software architecture of a risk system that accomplishes this task. The usage pattern is based on an offline phase to calibrate and generate model libraries. Valuation and simulation algorithms are planned offline with portfolio specific optimizations. The interactive user-driven phase includes a coherent global market simulation taking a few minutes and a real time data exploration phase with quick response time. Data exploration includes 3-dimensional risk visualization of portfolio loss distributions and sensitivities. It also includes risk resolution capability for outliers from the global portfolio level down to the single instrument level and hedge ratio optimization. The network bottleneck is bypassed by using heterogeneous boards with acceleration. The memory bottleneck is avoided at the algorithmic level by adapting the mathematical framework to revolve around a handful of compute-bound algorithms.
Capital and Funding
Risk Magazine
Banking operations are being rewired around a pair of KVA/FVA metrics which quantify market incompleteness, i.e. the impossibility of perfect replication. The FVA is the cost of funding of debt liabilities while the KVA is the risk adjustment for equity liabilities, also called cost of capital. The two metrics are intertwined with each other, since equity capital is itself a source of funding, fungible with debt financing. In this paper, we define the KVA and FVA metrics in terms of projections for Economic Capital and costs of funding. If implemented within the proper accounting framework, KVA/FVA mark-to-market leads to reporting rules for earnings which are both informative and useful to devise a sustainable strategy for dividend payments.
Coherent Global Market Simulations
Quantitative Finance
Valuing, hedging and securitizing counterparty credit risk involves analyzing large portfolios of netting sets over time horizons spanning decades. Theory dictates that the simulation measure should be coherent, i.e. arbitrage free. It should also be used consistently both to simulate and to value all instruments. This article describes the Mathematics and the software architecture of a risk system that accomplishes this task. The usage pattern is based on an offline phase to calibrate and generate model libraries. Valuation and simulation algorithms are planned offline with portfolio specific optimizations. The interactive user-driven phase includes a coherent global market simulation taking a few minutes and a real time data exploration phase with quick response time. Data exploration includes 3-dimensional risk visualization of portfolio loss distributions and sensitivities. It also includes risk resolution capability for outliers from the global portfolio level down to the single instrument level and hedge ratio optimization. The network bottleneck is bypassed by using heterogeneous boards with acceleration. The memory bottleneck is avoided at the algorithmic level by adapting the mathematical framework to revolve around a handful of compute-bound algorithms.
Capital and Funding
Risk Magazine
Banking operations are being rewired around a pair of KVA/FVA metrics which quantify market incompleteness, i.e. the impossibility of perfect replication. The FVA is the cost of funding of debt liabilities while the KVA is the risk adjustment for equity liabilities, also called cost of capital. The two metrics are intertwined with each other, since equity capital is itself a source of funding, fungible with debt financing. In this paper, we define the KVA and FVA metrics in terms of projections for Economic Capital and costs of funding. If implemented within the proper accounting framework, KVA/FVA mark-to-market leads to reporting rules for earnings which are both informative and useful to devise a sustainable strategy for dividend payments.
Regression Sensitivities for Initial Margin Calculations
Risk Magazine
Implementations of the Standard Initial Margin Model (SIMM) and the Sensitivity Based Approach (SBA) in the Fundamental Review of the Trading Book (FRTB), both call for the calculation of sensitivities with respect to a standardised set of risk factors. Since standard factors are generally collinear and pricing functions are possibly rough, finding sensitivities qualifies as a mathematically ill-posed problem for which analytical derivatives do not provide a robust solution. Numerical instabilities are particularly problematic since they hamper reconciliation and make collateral optimisation strategies inefficient. In this article, we introduce a method for calculating sensitivities based on ridge regressions to keep sensitivities small and stable. We find that a drift term and FX cross-gammas significantly improves the accuracy of the P&L explain achieved in the SIMM methodology. The method implies rigorous upper bounds on errors in P&L explain, on which basis we adjust Initial Margin conservatively in order to pass back-testing benchmarks.
Coherent Global Market Simulations
Quantitative Finance
Valuing, hedging and securitizing counterparty credit risk involves analyzing large portfolios of netting sets over time horizons spanning decades. Theory dictates that the simulation measure should be coherent, i.e. arbitrage free. It should also be used consistently both to simulate and to value all instruments. This article describes the Mathematics and the software architecture of a risk system that accomplishes this task. The usage pattern is based on an offline phase to calibrate and generate model libraries. Valuation and simulation algorithms are planned offline with portfolio specific optimizations. The interactive user-driven phase includes a coherent global market simulation taking a few minutes and a real time data exploration phase with quick response time. Data exploration includes 3-dimensional risk visualization of portfolio loss distributions and sensitivities. It also includes risk resolution capability for outliers from the global portfolio level down to the single instrument level and hedge ratio optimization. The network bottleneck is bypassed by using heterogeneous boards with acceleration. The memory bottleneck is avoided at the algorithmic level by adapting the mathematical framework to revolve around a handful of compute-bound algorithms.
Capital and Funding
Risk Magazine
Banking operations are being rewired around a pair of KVA/FVA metrics which quantify market incompleteness, i.e. the impossibility of perfect replication. The FVA is the cost of funding of debt liabilities while the KVA is the risk adjustment for equity liabilities, also called cost of capital. The two metrics are intertwined with each other, since equity capital is itself a source of funding, fungible with debt financing. In this paper, we define the KVA and FVA metrics in terms of projections for Economic Capital and costs of funding. If implemented within the proper accounting framework, KVA/FVA mark-to-market leads to reporting rules for earnings which are both informative and useful to devise a sustainable strategy for dividend payments.
Regression Sensitivities for Initial Margin Calculations
Risk Magazine
Implementations of the Standard Initial Margin Model (SIMM) and the Sensitivity Based Approach (SBA) in the Fundamental Review of the Trading Book (FRTB), both call for the calculation of sensitivities with respect to a standardised set of risk factors. Since standard factors are generally collinear and pricing functions are possibly rough, finding sensitivities qualifies as a mathematically ill-posed problem for which analytical derivatives do not provide a robust solution. Numerical instabilities are particularly problematic since they hamper reconciliation and make collateral optimisation strategies inefficient. In this article, we introduce a method for calculating sensitivities based on ridge regressions to keep sensitivities small and stable. We find that a drift term and FX cross-gammas significantly improves the accuracy of the P&L explain achieved in the SIMM methodology. The method implies rigorous upper bounds on errors in P&L explain, on which basis we adjust Initial Margin conservatively in order to pass back-testing benchmarks.
