銀行債券組合VaR的統計分配與波動性
Other Title
The Statistical Distribution and Volatility of Bond Portfolio VaR
for Banks
for Banks
Resource
銀行債卷組合VaR的統計分配與波動性
Date Issued
2002
Date
2002
Author(s)
DOI
902416H002004
Abstract
Traditional Asset and Liability
Management (ALM) uses accrual
accounting rather than marking all
positions to market. Another criticism
comes from the reliability of long term
forecasts used in ALM. Due to the
alleged deficiencies, Value at Risk (VaR)
was proposed as an alternative to ALM.
VaR estimates the implied changes in
portfolio value by the estimated
portfolio volatility. In general, VaR is
used to manage short-term (1 - 10 days ）
market risk.
There are three basic methods for
computing VaR measures: historical
simulation, variance-covariance matrix
(VC), and Monte Carlo simulation (MC)
methods. However, banks are permitted
to use combinations or variations of
these methods for VaR reporting. This
research combines the Vlaar (2000)
framework to compare various VaR
computing methods, and Login (2000)
proposal to apply EVT to VaR
estimation, to identify the proper
assumptions and parameters
specification for the VaR computing in
Taiwan. The extreme value theory (EVT)
method is based on the distribution
describing only the behavior of extreme
returns.
Using the actual portfolio weight
held by local banks on December 31,
2000, and data from October 1, 1993 to
April 30, 2002, we find that for models
with time-varying variances, methods
that combine variance-covariance and
Monte Carlo methods, as well as
EVTmethod, provides better results.
Simple VC or MC method leads to
under-estimation of VaR. Furthermore,
we find that the more complicated
GARCH models seem unable to
completely capture the interest rate
volatility over the studying period.
Subjects
Variance-Covariance
Method
Monte Carlo Simulation
Extreme Value Theory
Publisher
臺北市：國立臺灣大學國際企業學系暨研究所
Type
report
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