How sure you are about the .95 percent depends on your P value which should be discussed. how sure is your 95 percent. 10 percent sure.. ? 50 percent?
@khanpreston1. a .95 percent interval means that you , the researcher, is .95 percent sure that your test expected value will lie within the area of interest.. therefore you are then .05 percent unsure that your study is true. distribution mean is the "average of the data" so when you see a bell curve, the data is normally distributed ,(on a sactter plot if you want to use excel and plot some points)
so when calculating a 95 percent interval .. you use the 1.96 z value for a critical point and then your test point. my question is: if you used the T value 95 percent interval(1 tailed test of course) the dispersion is much greater so why would someone that is profit bound, want to calculate a 1 tailed z value when they .95 percent interval would be more favorable with a t test?
this which will give you not only a false sense of risk, as well as an incorrect perception of acceptable risk. Hence disaster for you in the short, long, or medium term. Intellectual dishonesty is DANGEROUS...Beware...
OK these type of risk management analysis is what created the current finanical system crisis due to its maligned uses by users who didnt and (dont) understand the details of its mechanics.
It is true. It doesn't take into consideration how big losses would be realized at the end of the tails. You could make +/- 0,000001 bucks 99% of the time and lose a fortune 1% of the time and it would say it's ok.
thank you very much, you really give me a clear ideas about these approaches which are required to be covered in my assignment.. but one question; are there any alternative approaches for VaR approaches which can be used in measuring risk in financial institutions?
@3king3kong3 you have to calculate the z value or t value of a one tailed test. google: statistics z value or t vallue. i havent calculated it but thats how you do it so i cant say if 98.2 is correct. you need 2 thisngs. std dev. mu which is the sample mean. so your forluma is z= X- mu / n (number of sample) times sq rt. sigma(std. deviation)
You simulate many possible future scenarios with monte carlo. There are at least two approaches: either you draw with replacement from historical data, either you approximate the distribution of data and draw from it. As for the models themselves, it solely depends on the problem. It can be as simple as one normally distributed variate, or as complex as the need dictates, with many variates that have many complex relationships and unstable parameters.
Thanks! That is helpful.
riddd9 8 months ago
Does anyone else think he sounds like kermit the frog?
jimmymac91 10 months ago in playlist Derivatives
@jimmymac91 It's not easy being green (but i can see how you confuse a turtle with a frog, easy mistake)
bionicturtledotcom 10 months ago 3
How sure you are about the .95 percent depends on your P value which should be discussed. how sure is your 95 percent. 10 percent sure.. ? 50 percent?
Creolebway 1 year ago
actually.. this data is pretty normally distributed.. more so than many others. and the normal dist. at 2:32 is more unrealistic in real life.
Creolebway 1 year ago
@khanpreston1. a .95 percent interval means that you , the researcher, is .95 percent sure that your test expected value will lie within the area of interest.. therefore you are then .05 percent unsure that your study is true. distribution mean is the "average of the data" so when you see a bell curve, the data is normally distributed ,(on a sactter plot if you want to use excel and plot some points)
Creolebway 1 year ago
so when calculating a 95 percent interval .. you use the 1.96 z value for a critical point and then your test point. my question is: if you used the T value 95 percent interval(1 tailed test of course) the dispersion is much greater so why would someone that is profit bound, want to calculate a 1 tailed z value when they .95 percent interval would be more favorable with a t test?
Creolebway 1 year ago
this which will give you not only a false sense of risk, as well as an incorrect perception of acceptable risk. Hence disaster for you in the short, long, or medium term. Intellectual dishonesty is DANGEROUS...Beware...
sysopkc 1 year ago
IF YOU USE this type of risk management you are contributing to a structure that is fundimentally flawed,
sysopkc 1 year ago
OK these type of risk management analysis is what created the current finanical system crisis due to its maligned uses by users who didnt and (dont) understand the details of its mechanics.
sysopkc 1 year ago
Which approach is best one to use ?
voiceofutube 1 year ago
@voiceofutube none of them!
sysopkc 1 year ago
@sysopkc I think. you are supporter of Nassim Nicholas Taleb's view which he described in his book "Black Swan"...?
voiceofutube 1 year ago
@voiceofutube
It is true. It doesn't take into consideration how big losses would be realized at the end of the tails. You could make +/- 0,000001 bucks 99% of the time and lose a fortune 1% of the time and it would say it's ok.
mourantell 1 year ago
what is confidence interval and what does distribution means?
khanpreston1 1 year ago
thank you very much, you really give me a clear ideas about these approaches which are required to be covered in my assignment.. but one question; are there any alternative approaches for VaR approaches which can be used in measuring risk in financial institutions?
kindheartedable 2 years ago
Thanks!!!
david1111koo 2 years ago
at 3:07, please tell me how you found that the 5% level would be at approx. 98.2..?/
3king3kong3 2 years ago
@3king3kong3 you have to calculate the z value or t value of a one tailed test. google: statistics z value or t vallue. i havent calculated it but thats how you do it so i cant say if 98.2 is correct. you need 2 thisngs. std dev. mu which is the sample mean. so your forluma is z= X- mu / n (number of sample) times sq rt. sigma(std. deviation)
Creolebway 1 year ago
Question: who determines the mathematica models used in Montecarlo simulation? Are they random?
OneHourDesign 3 years ago 3
You simulate many possible future scenarios with monte carlo. There are at least two approaches: either you draw with replacement from historical data, either you approximate the distribution of data and draw from it. As for the models themselves, it solely depends on the problem. It can be as simple as one normally distributed variate, or as complex as the need dictates, with many variates that have many complex relationships and unstable parameters.
Indrius 3 years ago
Many thanks to you !!
conanyoon 3 years ago
Thanks for your clear explanations.
givingstars 3 years ago