Prudent Valuation & Model Risk Quantification
(2014) FMS820 20142Mathematical Statistics
- Abstract (Swedish)
- This paper is a master's thesis by Erik Kivilo and Carl Olofsson at the Faculty
of Engineering (LTH) at Lund University. The research and writing
took place during the fall of 2014 under the supervision of Magnus Wiktorsson
at the Division of Mathematical Statistics at Lund University, and Per
Thastrom at EY, Copenhagen.
The thesis concerns prudent valuation of fair-valued nancial instruments
under current regulation on capital requirements for credit institutions and
investment rms within the EU. The aim is to explain the concept of prudent
valuation, and develop statistical methods for the calculation of additional
valuation adjustments (AVAs) required by the regulations. As there has
been little focus on model risk in previous... (More) - This paper is a master's thesis by Erik Kivilo and Carl Olofsson at the Faculty
of Engineering (LTH) at Lund University. The research and writing
took place during the fall of 2014 under the supervision of Magnus Wiktorsson
at the Division of Mathematical Statistics at Lund University, and Per
Thastrom at EY, Copenhagen.
The thesis concerns prudent valuation of fair-valued nancial instruments
under current regulation on capital requirements for credit institutions and
investment rms within the EU. The aim is to explain the concept of prudent
valuation, and develop statistical methods for the calculation of additional
valuation adjustments (AVAs) required by the regulations. As there has
been little focus on model risk in previous regulations, the main objective is
to quantify model risk AVA in a way that is compliant, using current research
on prudent valuation and model risk.
The method suggested in this paper captures the instantaneous valuation
uncertainty related to model risk as dened in the regulation. The rst
step of the method is to dene a group of plausible models and calibration
approaches for the instrument type. Each combination of model and calibration
is then assigned a probability weight based on the number of parameters
in the model, and a measure of t that includes all available market data.
When prices for the instrument have been calculated for all the dierent
models and calibrations, the probability weights are used to form a cumulative
price probability distribution for the instrument. The method is to our
understanding in line with the current regulation, and should according to
us hold some advantages compared to other proposed methods. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4888910
- author
- Kivilo, Erik and Olofsson, Carl
- supervisor
- organization
- course
- FMS820 20142
- year
- 2014
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 4888910
- date added to LUP
- 2014-12-22 11:50:34
- date last changed
- 2016-02-04 04:01:19
@misc{4888910, abstract = {{This paper is a master's thesis by Erik Kivilo and Carl Olofsson at the Faculty of Engineering (LTH) at Lund University. The research and writing took place during the fall of 2014 under the supervision of Magnus Wiktorsson at the Division of Mathematical Statistics at Lund University, and Per Thastrom at EY, Copenhagen. The thesis concerns prudent valuation of fair-valued nancial instruments under current regulation on capital requirements for credit institutions and investment rms within the EU. The aim is to explain the concept of prudent valuation, and develop statistical methods for the calculation of additional valuation adjustments (AVAs) required by the regulations. As there has been little focus on model risk in previous regulations, the main objective is to quantify model risk AVA in a way that is compliant, using current research on prudent valuation and model risk. The method suggested in this paper captures the instantaneous valuation uncertainty related to model risk as dened in the regulation. The rst step of the method is to dene a group of plausible models and calibration approaches for the instrument type. Each combination of model and calibration is then assigned a probability weight based on the number of parameters in the model, and a measure of t that includes all available market data. When prices for the instrument have been calculated for all the dierent models and calibrations, the probability weights are used to form a cumulative price probability distribution for the instrument. The method is to our understanding in line with the current regulation, and should according to us hold some advantages compared to other proposed methods.}}, author = {{Kivilo, Erik and Olofsson, Carl}}, language = {{eng}}, note = {{Student Paper}}, title = {{Prudent Valuation & Model Risk Quantification}}, year = {{2014}}, }