Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎

Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎

During last decades, financial markets have witnessed large losses due to their exposure to unexpected market crash. Resulting in these financial disasters, financial institutions, regulators and academics have developed intensive research to provide better measurement techniques and hedging tools....

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Bibliographic Details
Main Authors: Hassan Karnameh haghighi, Ali Rostami
Format: Article
Language:fas
Published: University of Isfahan 2018-12-01
Series:Journal of Asset Management and Financing
Subjects:
extreme value theory
long range-memory
value-at-risk
garch
Online Access:https://amf.ui.ac.ir/article_23282_596e923c6b897276e225913211120daa.pdf
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spelling doaj-efe10f2dfb554a58b3dab4a5b135343c2021-07-13T05:09:54ZfasUniversity of IsfahanJournal of Asset Management and Financing2383-11892383-11892018-12-016413515410.22108/amf.2018.102325.105323282Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎Hassan Karnameh haghighi0Ali Rostami1Sheikhbahaee University, Isfahan, IranSheikhbahaee University, Isfahan, IranDuring last decades, financial markets have witnessed large losses due to their exposure to unexpected market crash. Resulting in these financial disasters, financial institutions, regulators and academics have developed intensive research to provide better measurement techniques and hedging tools. Value-at-Risk (VaR) is the most popular risk measure in the financial industry. In this paper Application Extreme Value Theory and long-memory to Stock Market in Iran (In Framework Model-GARCH) was Checked. We use same long-range memory GARCH-type models (FIAGARCH, HYGARCH and FIAPARCH) and EVT to forecast the financial market risk. Findings Indicated that The FIAPARCH-EVT approach performs better in predicting the one day ahead VaRs for different series studied.https://amf.ui.ac.ir/article_23282_596e923c6b897276e225913211120daa.pdfextreme value theorylong range-memoryvalue-at-riskgarch
collection DOAJ
language fas
format Article
sources DOAJ
author Hassan Karnameh haghighi
Ali Rostami
spellingShingle Hassan Karnameh haghighi
Ali Rostami
Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎
Journal of Asset Management and Financing
extreme value theory
long range-memory
value-at-risk
garch
author_facet Hassan Karnameh haghighi
Ali Rostami
author_sort Hassan Karnameh haghighi
title Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎
title_short Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎
title_full Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎
title_fullStr Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎
title_full_unstemmed Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎
title_sort application extreme value theory and long-memory to stock market in iran (in framework model-garch)‎
publisher University of Isfahan
series Journal of Asset Management and Financing
issn 2383-1189
2383-1189
publishDate 2018-12-01
description During last decades, financial markets have witnessed large losses due to their exposure to unexpected market crash. Resulting in these financial disasters, financial institutions, regulators and academics have developed intensive research to provide better measurement techniques and hedging tools. Value-at-Risk (VaR) is the most popular risk measure in the financial industry. In this paper Application Extreme Value Theory and long-memory to Stock Market in Iran (In Framework Model-GARCH) was Checked. We use same long-range memory GARCH-type models (FIAGARCH, HYGARCH and FIAPARCH) and EVT to forecast the financial market risk. Findings Indicated that The FIAPARCH-EVT approach performs better in predicting the one day ahead VaRs for different series studied.
topic extreme value theory
long range-memory
value-at-risk
garch
url https://amf.ui.ac.ir/article_23282_596e923c6b897276e225913211120daa.pdf
work_keys_str_mv AT hassankarnamehhaghighi applicationextremevaluetheoryandlongmemorytostockmarketiniraninframeworkmodelgarch
AT alirostami applicationextremevaluetheoryandlongmemorytostockmarketiniraninframeworkmodelgarch
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