Teuta Xhindi – European University of Tirana, Bulevardi „Gjergj Fishta”, Nd.70, H.1, Tiranë, Albania
Ermela Kripa – European University of Tirana, Bulevardi „Gjergj Fishta”, Nd.70, H.1, Tiranë, Albania

DOI: https://doi.org/10.31410/ERAZ.S.P.2019.227

5th International Conference – ERAZ 2019 – KNOWLEDGE BASED SUSTAINABLE DEVELOPMENT, Budapest – Hungary, May 23, 2019, SELECTED PAPERS

Published by: Association of Economists and Managers of the Balkans – Belgrade, Serbia
Conference partners: Faculty of Economics and Business, Mediterranean University, Montenegro; University of National and World Economy – Sofia, Bulgaria; Faculty of Commercial and Business Studies – Celje, Slovenia; Faculty of Applied Management, Economics and Finance – Belgrade, Serbia;

ISBN 978-86-80194-21-9, ISSN 2683-5568, DOI: https://doi.org/10.31410/ERAZ.S.P.2019


Risk management is one of the most important processes of all agents operating in the financial
and non-financial markets. It is the combination of three steps: risk assessment, emission and
exposure control and risk monitoring. As of the assessment step, the VaR model is the most common
approach used to measure the market value at risk.
The aim of this paper is to evaluate the performance of the VaR model, in measuring the relative risk in
the Albanian foreign exchange market, where future prices in foreign exchange market are calculated
using the Monte Carlo simulation. In our analysis, we have considered the coefficient of variation a
good tool in measuring relative risk. The utilized data is taken from the official website of the Bank
of Albania, corresponding to the daily rates of exchange in the Albanian foreign exchange market of
January 3, 2018 to January 3, 2019. The instrument used is the simple linear regression, where the dependent
variable is VaR and the independent variable is the coefficient of variation.
The result of the study is: The VaR Model isn’t a good instrument to measure the exchange rate risk in
our case.

Key words

Value at Risk, Coefficient of Variation, Monte Carlo simulation, Simple Linear Regression.


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