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We set the context for capital approximation within the framework of the Basel II / III regulatory capital accords. This is particularly topical as the Basel III accord is shortly due to take effect. In this regard, we provide a summary of…

Risk Management · Quantitative Finance 2013-03-13 Gareth W. Peters , Rodrigo S. Targino , Pavel V. Shevchenko

Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach allows a provision for reduction of capital as a result of insurance mitigation of up to 20%. This paper studies the behaviour of different insurance…

Risk Management · Quantitative Finance 2010-11-04 Gareth W. Peters , Aaron D. Byrnes , Pavel V. Shevchenko

Inference in extreme value theory relies on a limited number of extreme observations, making estimation challenging. To address this limitation, we propose a non-parametric simulation scheme, the multivariate extreme events spectral…

Methodology · Statistics 2026-04-13 Nisrine Madhar , Juliette Legrand , Maud Thomas

On March 4th 2016 the Basel Committee on Banking Supervision published a consultative document where a new methodology, called the Standardized Measurement Approach (SMA), is introduced for computing Operational Risk regulatory capital for…

Risk Management · Quantitative Finance 2016-07-05 Giulio Mignola , Roberto Ugoccioni , Eric Cope

Event-related potentials (ERPs) extracted from electroencephalography (EEG) data in response to stimuli are widely used in psychological and neuroscience experiments. A major goal is to link ERP characteristic components to subject-level…

Methodology · Statistics 2024-06-11 Cheng-Han Yu , Meng Li , Marina Vannucci

This paper presents a novel approach to stochastic volatility (SV) modeling by utilizing nonparametric techniques that enhance our ability to capture the volatility of financial time series data, with a particular emphasis on the…

Computation · Statistics 2025-02-18 Yudong Feng , Ashis Gangopadhyay

Models for extreme values accommodating non-stationarity have been amply studied and evaluated from a parametric perspective. Whilst these models are flexible, in the sense that many parametrizations can be explored, they assume an…

Applications · Statistics 2022-02-16 Evandro Konzen , Claudia Neves , Philip Jonathan

The Basel Committee on Banking Supervision proposed replacing all approaches for operational risk capital, including the Advanced Measurement Approach (AMA), with a simplified formula called the Standardized Measurement Approach (SMA). This…

Risk Management · Quantitative Finance 2025-02-04 Omar Briceno Cruzado

The management of operational risk in the banking industry has undergone significant changes over the last decade due to substantial changes in operational risk environment. Globalization, deregulation, the use of complex financial products…

Risk Management · Quantitative Finance 2014-05-22 Pavel V. Shevchenko , Gareth W. Peters

Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach…

Risk Management · Quantitative Finance 2016-09-15 Gareth W. Peters , Pavel V. Shevchenko , Bertrand Hassani , Ariane Chapelle

We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the…

Methodology · Statistics 2019-07-23 Yanxun Xu , Daniel Scharfstein , Peter Müller , Michael Daniels

Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach is not prescriptive regarding the class of statistical model utilised to undertake capital estimation. It has however become well accepted to utlise a…

Risk Management · Quantitative Finance 2011-02-18 Gareth W. Peters , Pavel Shevchenko , Mark Young , Wendy Yip

This paper proposes a semiparametric stochastic volatility (SV) model that relaxes the restrictive Gaussian assumption in both the return and volatility error terms, allowing them to follow flexible, nonparametric distributions with…

Computation · Statistics 2025-06-03 Yudong Feng , Ashis Gangopadhyay

Risk management is particularly concerned with extreme events, but analysing these events is often hindered by the scarcity of data, especially in a multivariate context. This data scarcity complicates risk management efforts. Various tools…

Methodology · Statistics 2026-01-15 Nisrine Madhar , Juliette Legrand , Maud Thomas

Recurrent event data are common in clinical studies when participants are followed longitudinally, and are often subject to a terminal event. With the increasing popularity of large pragmatic trials with a heterogeneous source population,…

Methodology · Statistics 2022-12-06 Xinyuan Tian , Maria Ciarleglio , Jiachen Cai , Erich Greene , Denise Esserman , Fan Li , Yize Zhao

Modern high-frequency trading (HFT) environments are characterized by sudden price spikes that present both risk and opportunity, but conventional financial models often fail to capture the required fine temporal structure. Spiking Neural…

Machine Learning · Computer Science 2025-12-08 Brian Ezinwoke , Oliver Rhodes

We present a novel approach to ecological risk assessment by recasting the Species Sensitivity Distribution (SSD) method within a Bayesian nonparametric (BNP) framework. Widely mandated by environmental regulatory bodies globally, SSD has…

Methodology · Statistics 2026-02-05 Louise Alamichel , Julyan Arbel , Guillaume Kon Kam King , Igor Prünster

Operational risk capital estimation under Basel II/III requires quantifying aggregate losses at extreme confidence levels of 99.9% and beyond, yet the standard Loss Distribution Approach (LDA) assumes independence between loss frequency and…

Computational Engineering, Finance, and Science · Computer Science 2026-05-25 Juan Ballesteros Gómez , Eduardo C. Garrido-Merchán , Pedro Pablo Pérez-Velasco

The largest US banks are required by regulatory mandate to estimate the operational risk capital they must hold using an Advanced Measurement Approach (AMA) as defined by the Basel II/III Accords. Most use the Loss Distribution Approach…

Risk Management · Quantitative Finance 2014-12-01 J. D. Opdyke

According to the Loss Distribution Approach, the operational risk of a bank is determined as 99.9% quantile of the respective loss distribution, covering unexpected severe events. The 99.9% quantile can be considered a tail event. As…

Risk Management · Quantitative Finance 2015-03-17 Nataliya Horbenko , Peter Ruckdeschel , Taehan Bae
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