(EViews10): How to Estimate GARCH-in-Mean Models #garchmodels #garchm #tgarch #volatility #egarch

00:07:51
https://www.youtube.com/watch?v=M5d1Lj20pP0

Zusammenfassung

TLDRUshbu video GARCH modellarini baholash va tahmin qilish jarayonini tushuntiradi. Muallif, riskli aktivlarni ushlab turish uchun investorlar talab qiladigan risk mukofotining qanday hisoblanishini va bu mukofotning shartli dispersiya yoki standart og'ish orqali qanday ifodalanishini ko'rsatadi. Video davomida muallif GARCH modellarini baholashda shartli dispersiya va standart og'ishdan foydalanish jarayonini ko'rsatadi va natijalarni taqqoslaydi. Natijalar shuni ko'rsatadiki, har ikkala holatda ham koeffitsientlar statistik jihatdan ahamiyatsizdir, bu esa aktivning riskli emasligini anglatadi.

Mitbringsel

  • 📊 GARCH modeli riskni modellashtirish uchun ishlatiladi.
  • 💰 Risk mukofoti aktivning risk darajasiga bog'liq.
  • 📈 Shartli dispersiya vaqt o'tishi bilan o'zgaradi.
  • 📉 Standart og'ish dispersiyaning kvadrat ildizidir.
  • ❌ Koeffitsientlar statistik jihatdan ahamiyatsiz bo'lsa, aktiv riskli emas.
  • 🔍 GARCH modellarini baholashda shartli dispersiya va standart og'ishdan foydalanish mumkin.
  • 📚 O'qish va tadqiqotlar GARCH modellarini tushunishga yordam beradi.
  • 🎥 Video ko'rishdan tashqari, ilmiy maqolalarni o'qish muhimdir.
  • 🤝 Fikr-mulohazalar va savollar muallifga yordam beradi.
  • 🔄 Video seriyasini davom ettirish uchun izohlar qoldiring.

Zeitleiste

  • 00:00:00 - 00:07:51

    Ushbu video GARCH modelini baholashga bag'ishlangan. Muallif, riskli aktivlarni ushlab turish uchun investorlar talab qiladigan mukofotning, risk darajasiga bog'liqligini tushuntiradi. GARCH modelida shartli dispersiya va standart og'ishdan foydalanish orqali vaqt o'tishi bilan o'zgaruvchi risk mukofotini qanday baholash mumkinligini ko'rsatadi. Muallif, shartli dispersiya va standart og'ishning asosiy tenglamalarga kiritilishi natijasida olingan natijalarni taqqoslaydi va har ikkala holatda ham koeffitsientlarning statistik ahamiyati yo'qligini ta'kidlaydi. Bu, investorlar uchun aktivning risk darajasini baholashda muhim xulosalar chiqarishga yordam beradi.

Mind Map

Video-Fragen und Antworten

  • GARCH modeli nima?

    GARCH modeli, shartli heteroskedastiklikni modellashtirish uchun ishlatiladigan statistik modeldir.

  • Risk mukofoti qanday hisoblanadi?

    Risk mukofoti, aktivning risk darajasiga qarab belgilangan mukofotdir.

  • Shartli dispersiya va standart og'ish o'rtasidagi farq nima?

    Shartli dispersiya, vaqt o'tishi bilan o'zgaruvchi dispersiyani ifodalaydi, standart og'ish esa dispersiyaning kvadrat ildizidir.

  • Nima uchun koeffitsientlar statistik jihatdan ahamiyatsiz?

    Koeffitsientlar statistik jihatdan ahamiyatsiz bo'lsa, bu aktivning riskli emasligini anglatadi.

  • GARCH modellarini qanday baholash mumkin?

    GARCH modellarini baholash uchun shartli dispersiya yoki standart og'ishdan foydalanish mumkin.

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Untertitel
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Automatisches Blättern:
  • 00:00:00
    we are still on the Gaucho model and
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    series this is coach econometrics and it
  • 00:00:04
    is good to have you back in this video
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    I'll be estimating a guardian main
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    Moodle give you the intuition behind
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    where we estimates gotcha in main
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    modules but before I do so in my usual
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    way I will encourage you to please watch
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    these videos in sequential order please
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    do not skip any I really wants you to
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    understand how to estimate GARCH models
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    so let us get some intuition for
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    estimating a gosh M model remember that
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    risk-averse investors may require a
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    premium as a compensation for them to
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    hold a risky assets that premium is
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    clearly a function of the risk that is
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    the higher the risk the higher the
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    premium should be if the risk is now
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    captured by the volatility or by the
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    conditional variance then the
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    conditional variance may enter into the
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    conditional mean equation as specified
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    here so this is the conditional variance
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    in the main equation so the GOC M allows
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    the conditional mean to depend on its
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    own conditional variance its models a
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    time varying risk premium the same catch
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    em model can also use the standard
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    deviation of the series to capture the
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    risk so in the first one we have the
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    conditional variance and the second one
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    we have the standard deviation therefore
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    the GOC um PQ model can be generalized
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    towards you are seen on the screen so
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    the first two are the main equations for
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    the GOC M while this one this part this
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    last equation is the usual conditional
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    variance equation that we are used to
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    buy now so now let's proceed to give us
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    an estimate the Gotcha model using the
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    variance in the main equation then
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    secondly we use the standard deviation
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    and the main equation and we compare
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    your results I double
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    Kongo series I got a quick click on
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    estimates equation I list the series in
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    the usual form that we'll be using
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    I go to methods I change it to arch
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    please follow me and do likewise with
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    your data remember we are estimating a
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    catch
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    M model so we come to this box and we
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    change none we open it and we chain on
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    to variance so let's start with the
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    variance forced in the main equation so
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    we change this to variance we go to
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    options optimization method I've been
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    using a vyas legacy so I change this to
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    Eevee Isleta see I don't change any
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    other thing I go back to specification
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    everything looks fine here my sample
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    size is okay is the usual sample size I
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    click okay so here we can see the GOC m
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    model using the variance in the main
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    equation and the conditional variance is
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    captured by the gosh you can see here in
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    the main equation also do not confuse it
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    with the gosh -1 here they are not the
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    same
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    remember we estimating in catch a model
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    and what can you observe the coefficient
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    of the variance in the main equation is
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    not statistically significant but we can
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    say that by including it's the main
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    equation it has improved the
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    significance of the got charm in the
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    variance equation so this is what we can
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    contribute by using the variance in the
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    main equation probe value is 20 percents
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    or 21% clearly not significant by
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    including it has improved the Gaucho
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    term in the various equation now let's
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    use the standard deviation and see
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    whether we are going to have a different
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    outcome so we click on estimates the
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    modified variance now to standard
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    deviation we don't change anything we
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    click ok so here we have once you see a
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    square root gosh this is the standard
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    deviation so we can also see that the
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    standard deviation is clearly not
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    significant is 22.2%
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    it's not significant statistically by
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    including it has improved the Gaucho
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    term in the variance equation so the
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    findings is not different from what we
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    go from the DEA's so what do we conclude
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    as a financial analyst let's go back to
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    powerpoints for my explanation so this
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    is the specification I use to remember
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    to change the HM from non so variance
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    and my optimization method is a vyas
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    legacy so this is for the variance
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    equation and here is a result that we
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    got we saw that the conditional variance
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    term in the main equation is
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    statistically not significant so it's
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    one percent approximately here so what
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    do we conclude we can say that this
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    fixed premium is no significant to hedge
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    it gets holding a risky assets it's not
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    significant so we can say that the asset
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    in question may not be risky to hold so
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    as an investor if you are using the
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    variance to help you against holding the
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    risk you can clearly see that this asset
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    is not risky at all so you can hold it
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    the variance term which is a cash town
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    is clearly not significant and the main
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    equation again for the standard
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    deviation remember to modify the mbox to
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    reflect standard deviation I use a vyas
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    legacy as usual and our results also
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    shows that the standard deviation
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    coefficient is also not significant is
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    over 22 percents same conclusion we can
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    say that the risk premium is not
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    significant to hedge against holding
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    this risk and therefore we can say that
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    he asset is not risky to hold so this
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    video has summarized your basic
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    knowledge on how you can estimate in
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    kajam model using the conditional
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    variance on a min equation or using the
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    standard deviation in the main equation
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    check out the coefficient in the menu
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    question to see where a significant
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    enough to be a premium that will hurt
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    against
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    holding a risky asset in my situation
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    both coefficients are statistically not
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    significant so these are references are
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    readings to support what we have just
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    watched in relation to estimating a
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    gaucho a model kindly read of at least
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    one or two papers
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    it was strengthen your understanding of
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    GARCH models and video tutorials are
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    clearly not sufficient you have to read
  • 00:06:59
    thank you so much we have covered five
  • 00:07:01
    topics now as you can see on the screen
  • 00:07:03
    do not skip any keep yourself abreast of
  • 00:07:06
    GARCH models by watching these videos in
  • 00:07:10
    sequential order I am grateful to all
  • 00:07:12
    the comments I've received so far since
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    I began this gosh modeling series
  • 00:07:16
    they've been encouraging and thank you
  • 00:07:17
    for your questions for the queries for
  • 00:07:19
    the comments for the critics for the
  • 00:07:22
    uploads for the commendation I thank you
  • 00:07:25
    all so very much
  • 00:07:26
    continue to share my videos to your
  • 00:07:28
    students to your academic community as
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    many who are still afraid of
  • 00:07:32
    econometrics please tell them chronicle
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    matrix simplifies understanding thank
  • 00:07:38
    you for watching please don't go away
  • 00:07:40
    I'll be right back with the next video
  • 00:07:42
    which is on how to estimate a treasured
  • 00:07:46
    Gaucho or what you can call the gjr gosh
Tags
  • GARCH
  • ekonometrika
  • risk mukofoti
  • shartli dispersiya
  • standart og'ish
  • modellashtirish
  • statistik ahamiyat
  • investorlar
  • riskli aktivlar
  • tahlil