نتایج جستجو برای: egarch و ardl

تعداد نتایج: 763180  

2009
Nedal T. Ratrout Syed Masiur Rahman

إ ةيرهجملا جماربلا لمشي اذهو ،ةيرورملا ةآرحلا ةاآاحم جمارب روطت يف مهاس ةيتامولعملا ايجولونكتلا يف ريبكلا مدقتلا ن ) Microscopic ( ، نلا ىلع بلطلا ةاآاحم اًضيأ لمشي نايحلأا ضعب يفو ، تاعطاقتو قرط نم اهيف امب لقنلا ةموظنم ةاآاحمب حمسيل يللآا بساحلا تاقيبطت قاطنو لق . ةيرهجملا ةيرورملا ةآرحلا ةاآاحم جمارب نيب نراقتو عجارت ةقرولا هذهو ) Microscopic ( ةيلومشلاو ) Macroscopic ( فلاتخلاا هجوأ ىلع ةزآر...

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2010
Manabu Asai Michael McAleer Hang Seng

The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation (MECC) model. The paper applies the WDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents a...

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1997
M. Hashem Pesaran Yongcheol Shin

This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of long-run relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the short-run parameters are p T -consistent with the asymptotically singular covariance matrix, and the ARDL-based estimators of the long-run ...

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2011
Bhzad Sidawi

اهتفاقثو اهرخفو ةملأا ةيأ ةيوه نم أزجتي لا اءزج يرامعملا ثارتلا لكشي . ةيخيراتلا ةرامعلا رصانع نا يف اهؤانبو اهميمصت مت دق ةيملاسلإا تابلطتمل يويحو لاعف لكشب ةباجتسلاا ىلع ةرداق نوكت ثيحب يضاملا ةينيدلاو ةيسفنلاو ةيعامتجلااو ةيئيبلاو ةيداملا سانلا . رضاحلا تقولا يف نويرامعملا نوسدنهملا موقي رصانعلا ذخأب اوذخأي ام ةداعو ميمصتلا عيراشم يف اهلاخدا و ةيملاسلإا ةيخيراتلا ينابملا نم ةفلتخملا نيوكت ر...

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2014
Michael McAleer Christian M. Hafner

One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH (or EGARCH) specification. In addition to asymmetry, which captures the different effects on conditional volatility of positive and negative effects of equal magnitude, EGARCH can also accommodate leverage, which is the negative correlation between returns shocks and subsequent shocks to volatil...

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2011
M. Mohammadzadeh Moghaddam M. Darijani

Magnetic data is useful in both hydrocarbon exploration and mining exploration. In hydrocarbon exploration, aeromagnetic data is an effective method for locating strike-slip faulting in magnetic basement. Also, high resolution surveys enhance delineation of both subtle basement features and intrasedimentary sources. In fact, magnetic methods has evolved from its sole use for mapping basement st...

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2017

شور : ،ينيلاب ييامزآراک هعلاطم نيا رد ۲۰ يحارج لمع ديدناك يندومزآ هب يموتكرتسيه دندش هداد ياج لرتنک و شيامزآ هورگ ود رد يفداصت روط . راتفر لوط رد يحارج زا شيپ يتخانش ينامرد ۸ ورگ یارب هسلج شيامزآ ه دوبهب عيرست ظاحل زا هورگ ود ره ،يحارج لمع زا سپ و دش هداد شزومآ رد يرتسب ياهزور دادعت ،يفرصم نكسم يوراد زود نازيم ينعي يمسج تيلاعف هب تشگزاب و ناتسراميب دندش يسررب هرمزور ياه . هداد شهوژپ ياه يرام...

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2010
Jibendu Kumar Mantri

The present study aims at applying different methods i.e GARCH, EGARCH, GJRGARCH, IGARCH & ANN models for calculating the volatilities of Indian stock markets. Fourteen years of data of BSE Sensex & NSE Nifty are used to calculate the volatilities. The performance of data exhibits that, there is no difference in the volatilities of Sensex, & Nifty estimated under the GARCH, EGARCH, GJR GARCH, I...

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2018

کچ ی هد پ ی ش مز ی هن ه و فد : ساسا د مردنس رد نامرد ي سفنت سرتس ي ظنت نادازون داح ي سکا لدابت م ي و نژ د ي سکا ي د هدوب نبرک تسا طسوت هک کبس اـه ي ناـمرد ي فلتخم ي هلمجزا لکتورپ INSURE ماجنا م ي دوش ا اذل . ي هعلاطم ن فدهاب اقم ي هس عضو ي ت اه ي ندب ي عضو رب رمد و زاب قاط ي سفنت ت ي هـب لاتـبم سراـن نادازون ردنس د م ي سفنت سرتس ي لکتورپ اب نامرد تحت داح INSURE ماجنا درگ ...

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2017
Chia-Lin Chang Michael McAleer

In the class of univariate conditional volatility models, the three most popular are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH) model of Nelson (1990, 1991). For purposes of deriving the mathematical regularit...

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    • "; pgn_html+=pgn_li; } document.getElementById("pgn-ul").innerHTML=pgn_html var pgn_links = document.querySelectorAll('.mypgn'); pgn_links.forEach(function(pgn_link) { pgn_link.addEventListener('click', paginate) }) } function post_and_fetch(data,url) { showLoading() xhr = new XMLHttpRequest(); xhr.open('POST', url, true); xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8'); xhr.onreadystatechange = function() { if (xhr.readyState === 4 && xhr.status === 200) { var resp = xhr.responseText; resp_json=JSON.parse(resp) resp_place = document.getElementById("search_result_div") resp_place.innerHTML = resp_json['results'] search_meta = resp_json['meta'] update_search_meta(search_meta) update_pagination() hideLoading() } }; xhr.send(JSON.stringify(data)); } function unfilter() { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":"unfilter", "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } function deactivate_all_bars(){ var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(bar) { bar.dataset.active = false bar.style = "stroke:#71a3c5;" }) } year_chart.on("created", function() { var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(check) { check.addEventListener('click', checkIndex); }) }); function checkIndex(event) { var yrchart = document.querySelectorAll('.ct-bar'); var year_bar = event.target if (year_bar.dataset.active == "true") { unfilter_res = unfilter() year_bar.dataset.active = false year_bar.style = "stroke:#1d2b3699;" } else { deactivate_all_bars() year_bar.dataset.active = true year_bar.style = "stroke:#e56f6f;" filter_year = chart_data['labels'][Array.from(yrchart).indexOf(year_bar)] url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":filter_year, "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } } function showLoading() { document.getElementById("loading").style.display = "block"; setTimeout(hideLoading, 10000); // 10 seconds } function hideLoading() { document.getElementById("loading").style.display = "none"; } -->