Sensitivities of value at risk for non-linear portfolios
Read Online
Share

Sensitivities of value at risk for non-linear portfolios by Smaranda PДѓun

  • 866 Want to read
  • ·
  • 56 Currently reading

Published by National Library of Canada in Ottawa .
Written in English


Book details:

Edition Notes

Thesis (M.Sc.) -- University of Toronto, 1999.

SeriesCanadian theses = -- Thèses canadiennes
The Physical Object
FormatMicroform
Pagination1 microfiche : negative. --
ID Numbers
Open LibraryOL19264868M
ISBN 100612460576
OCLC/WorldCa47364960

Download Sensitivities of value at risk for non-linear portfolios

PDF EPUB FB2 MOBI RTF

  portfolio’s value is a not linear combination of the market prices of the underlying securities, Three Value-at-Risk (VaR) models, traditional estimate based Monte Carlo model, GARCH based Monte Carlo model, and resampling model, are developed to estimate risk of non-linear ://   Worst-Case Value-at-Risk of Non-Linear Portfolios Steve Zymler, Daniel Kuhn and Berç Rustem DepartmentofComputing ImperialCollegeofScience,TechnologyandMedicine Queen’sGate,LondonSW72AZ,UK. J Abstract Two Analytical Approximations of   Lecture 7: Value At Risk (VAR) Models Ken Abbott Developed for educational use at MIT and for publication through MIT OpenCourseware. No investment decisions should be Value-at- Risk (VaR) is a general measure of risk developed to equate risk across products and to aggregate risk on a portfolio basis. VaR is defined as the predicted worst-case loss with a specific confidence level (for example, 95%) over a period of time (for example, 1 day).

  Risk controlling implies controlling sensitivities to risk by taking offsetting positions to the same risk factors. The size of the hedging position depends on the relative sensitivities of the hedged instruments and the hedging instruments, as it was the case for hedging a   risk charge aims to capture the losses on the trading portfolio due issuers of equities and bonds defaulting. Finally, the residual risk add-on is a conservative notional value based add-on mainly for instruments with a non-linear pay-off that cannot be replicated with vanilla options. It aims to capture market risks beyond those Value at Risk (VaR) is the value that is equaled or exceeded the required percentage of times (1, 5, 10). Historical simulation is a non-parametric approach of estimating VaR, i.e. the returns are not subjected to any functional distribution. Estimate VaR directly from the data without deriving parameters or making assumptions about the entire   point in time. Value at Risk tries to provide an answer, at least within a reasonable bound. In fact, it is misleading to consider Value at Risk, or VaR as it is widely known, to be an alternative to risk adjusted value and probabilistic approaches. After all, it borrows liberally from both. However, the wide use of VaR as a tool for risk

  The presence of non linear instruments is responsible for the emergence of non Gaussian features in the price changes distribution of realistic portfolios, even for Normally distributed risk factors. This is especially true for the benchmark Delta Gamma Normal model, which in general exhibits exponentially damped power law tails. We show how the knowledge of the model characteristic B/abstract. The presence of non linear instruments is responsible for the emergence of non Gaussian features in the price changes distribution of realistic portfolios, even for Normally distributed risk ://   PORTFOLIO OPTIMIZATION WITH CONDITIONAL VALUE-AT-RISK OBJECTIVE AND CONSTRAINTS Pavlo Krokhmal1, Jonas Palmquist2, and Stanislav Uryasev1 Date: Septem Correspondence should be addressed to: Stanislav Uryasev 1University of Florida, Dept. of Industrial and Systems Engineering, PO Box , Weil Hall, Gainesville, FL , Tel.: ()   is calibrated to the credit risk treatment in the banking book toreduce the potential discrepancy in capital requirements for similar risk exposures across the banking book and trading book. As with the sensitivities based method, the Default Risk Charge allows for