11,574 research outputs found
Implementing Loss Distribution Approach for Operational Risk
To quantify the operational risk capital charge under the current regulatory
framework for banking supervision, referred to as Basel II, many banks adopt
the Loss Distribution Approach. There are many modeling issues that should be
resolved to use the approach in practice. In this paper we review the
quantitative methods suggested in literature for implementation of the
approach. In particular, the use of the Bayesian inference method that allows
to take expert judgement and parameter uncertainty into account, modeling
dependence and inclusion of insurance are discussed
Loss Distribution Approach for Operational Risk Capital Modelling under Basel II: Combining Different Data Sources for Risk Estimation
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 and changes in information technology have resulted in
exposure to new risks very different from market and credit risks. In response,
Basel Committee for banking Supervision has developed a regulatory framework,
referred to as Basel II, that introduced operational risk category and
corresponding capital requirements. Over the past five years, major banks in
most parts of the world have received accreditation under the Basel II Advanced
Measurement Approach (AMA) by adopting the loss distribution approach (LDA)
despite there being a number of unresolved methodological challenges in its
implementation. Different approaches and methods are still under hot debate. In
this paper, we review methods proposed in the literature for combining
different data sources (internal data, external data and scenario analysis)
which is one of the regulatory requirement for AMA
A Bayesian Networks Approach to Operational Risk
A system for Operational Risk management based on the computational paradigm
of Bayesian Networks is presented. The algorithm allows the construction of a
Bayesian Network targeted for each bank using only internal loss data, and
takes into account in a simple and realistic way the correlations among
different processes of the bank. The internal losses are averaged over a
variable time horizon, so that the correlations at different times are removed,
while the correlations at the same time are kept: the averaged losses are thus
suitable to perform the learning of the network topology and parameters. The
algorithm has been validated on synthetic time series. It should be stressed
that the practical implementation of the proposed algorithm has a small impact
on the organizational structure of a bank and requires an investment in human
resources limited to the computational area
Bayesian networks for enterprise risk assessment
According to different typologies of activity and priority, risks can assume
diverse meanings and it can be assessed in different ways. In general risk is
measured in terms of a probability combination of an event (frequency) and its
consequence (impact). To estimate the frequency and the impact (severity)
historical data or expert opinions (either qualitative or quantitative data)
are used. Moreover qualitative data must be converted in numerical values to be
used in the model. In the case of enterprise risk assessment the considered
risks are, for instance, strategic, operational, legal and of image, which many
times are difficult to be quantified. So in most cases only expert data,
gathered by scorecard approaches, are available for risk analysis. The Bayesian
Network is a useful tool to integrate different information and in particular
to study the risk's joint distribution by using data collected from experts. In
this paper we want to show a possible approach for building a Bayesian networks
in the particular case in which only prior probabilities of node states and
marginal correlations between nodes are available, and when the variables have
only two states
A Bayesian copula model for stochastic claims reserving
We present a full Bayesian model for assessing the reserve requirement of multiline Non-Life insurance companies. Bayesian models for claims reserving allow to account for expert knowledge in the evaluation of Outstanding Loss Liabilities, allowing the use of additional information at a low cost. This paper combines a standard Bayesian approach for the estimation of marginal distribution for the single Lines of Business for a Non-Life insurance company and a Bayesian copula procedure for the estimation of aggregate reserves. The model we present allows to "mix" own-assessments of dependence between LoBs at a company level and market-wide estimates provided by regulators. We illustrate results for the single lines of business and we compare standard copula aggregation for different copula choices and the Bayesian copula approach.stochastic claims reserving; bayesian copulas; solvency capital requirement; loss reserving; bayesian methods
Bayesian Methods for Measuring Operational Risk
The likely imposition by regulators of minimum standards for capital to cover 'other risks' has been a driving force behind the recent interest in operational risk management. Much discussion has been centered on the form of capital charges for other risks. At the same time major banks are developing models to improve internal management of operational processes, new insurance products for operational risks are being designed and there is growing interest in alternative risk transfer, through OR-linked products.
The Present, Future and Imperfect of Financial Risk Management
Current research on financial risk management applications of econometrics centres on the accurate assessment of individual market and credit risks with relatively little theoretical or applied econometric research on other types of risk, aggregation risk, data incompleteness and optimal risk control. We argue that consideration of the model risk arising from crude aggregation rules and inadequate data could lead to a new class of reduced form Bayesian risk assessment models. Logically, these models should be set within a common factor framework that allows proper risk aggregation methods to be developed. We explain how such a framework could also provide the essential links between risk control, risk assessments and the optimal allocation of resources.Financial risk assessment; risk control, RAROC, economic capital; regulatory capital; optimal allocation of resources
Fast calibrated additive quantile regression
We propose a novel framework for fitting additive quantile regression models,
which provides well calibrated inference about the conditional quantiles and
fast automatic estimation of the smoothing parameters, for model structures as
diverse as those usable with distributional GAMs, while maintaining equivalent
numerical efficiency and stability. The proposed methods are at once
statistically rigorous and computationally efficient, because they are based on
the general belief updating framework of Bissiri et al. (2016) to loss based
inference, but compute by adapting the stable fitting methods of Wood et al.
(2016). We show how the pinball loss is statistically suboptimal relative to a
novel smooth generalisation, which also gives access to fast estimation
methods. Further, we provide a novel calibration method for efficiently
selecting the 'learning rate' balancing the loss with the smoothing priors
during inference, thereby obtaining reliable quantile uncertainty estimates.
Our work was motivated by a probabilistic electricity load forecasting
application, used here to demonstrate the proposed approach. The methods
described here are implemented by the qgam R package, available on the
Comprehensive R Archive Network (CRAN)
- …