Three approaches to value at risk (VaR) and volatility (FRM T4-1)

Поделиться
HTML-код
  • Опубликовано: 4 окт 2024

Комментарии • 18

  • @rodlu811
    @rodlu811 3 года назад

    Never seen someone explain this hard subject with so much clarity and simplicity.

  • @samrathore9396
    @samrathore9396 5 лет назад +3

    Very well explained, its actually tough to explain the mathematical functions using the principles and concepts.

  • @HARSHBENIWAL23
    @HARSHBENIWAL23 5 лет назад +1

    I was stuck on this chapter for so... Thanks David for helping me out!!
    Kudos!!

    • @bionicturtle
      @bionicturtle  4 года назад

      You're welcome! We are glad that our video was so helpful :)

  • @yvesprimeau6031
    @yvesprimeau6031 4 года назад

    Exellent explanation. The diagram of volatility help me to get the big picture of the concept. I suggest more diagrams too help us to visualize all the concepts of FRM at large....

  • @Small1400
    @Small1400 3 года назад +1

    Great videos! I was wondering what the best, most appropriate, approach would be to calculate volatility and eventually VaR in electricity markets, where often times prices are negative. Thanks in advance!

  • @adventuresunlimited4483
    @adventuresunlimited4483 3 года назад

    Amazing Videos.I referred to many sources but this is the first time I understood the concept. Is there an excel sheet for Monte Carlo VaR calculation ?

  • @ntcuong01ct1
    @ntcuong01ct1 3 года назад

    Hello friends,
    I have a few questions:
    1 / Risks will be specified after we have identified the audience, objectives, and operational processes ?.
    2 / Risk will be directly integrated into the business process ?.
    3 / The Risk department is responsible for determining the VaR (Value at Risk) and presenting it to the Board of Directors seeing the risks and proactively preventing them?
    4 / Actively preventing risks will help us improve the value of products / services to customers?

  • @jaylev85
    @jaylev85 4 года назад

    Have you posted any videos that discuss Cholesky decomposition? more specifically the procedure for generating correlated variables from independent Rho's to fatten the tails. I think this is a technique that attemps to sovle some of the limitations of the Var-Covar approach (i.e. the "parameteric" or "historical method" mentioned above).

  • @viciousbeats8510
    @viciousbeats8510 7 месяцев назад

    Is "r" interpreted as returns squared or (return minus mean of returns) squared?

  • @lmagz84
    @lmagz84 3 года назад

    How can we measure it using eviews

  • @aslivinschi
    @aslivinschi 3 года назад

    Dear professor, what about if I want to understand at 95% confidence what could be my best results instead of the risk of lost.?

    • @bionicturtle
      @bionicturtle  3 года назад +1

      Hi @Alexel maybe due to language differences, I cannot understand your question (apologies). This video reviews the three basic approaches to VaR, and VaR is the statistical way to answer "What is the worst expected loss with 9X% confidence?" Thanks,

    • @aslivinschi
      @aslivinschi 3 года назад +1

      @@bionicturtle many thanks! I am looking at the gains instead of losses, is kind of a reverse of VAR. I am trying to figure out at 90% confidence interval the minimum amount to gain instead of loosing. So, I done this formula for the min gains =Mean + (Std * Z-Stat) instead of doing as usual for the VAR= Mean - (STD *Z-Stat). I just changed the sign to plus, is that enough?

    • @bionicturtle
      @bionicturtle  3 года назад +1

      @@aslivinschi Oh, okay, yes sure you can do that! Maybe we call it "value at [to] gain"? aka, VaG. It would be similarly one-sided such that, if normal, at 95.0% Z = +1.645. And you would add the mean, just as you show, where your format is implicitly P(+)/L(-) which is natural math, gains are positive. So looks good to me

    • @aslivinschi
      @aslivinschi 3 года назад

      @@bionicturtle you are amazing. Thanks David

  • @somebody5186
    @somebody5186 6 месяцев назад

    Too much words. As for me.