Hi Guys, I just passed my level 1. A little above the border line. My weakest subjects were Quants followed by Portfolio management (both below 50%) and Fixed Income 50%. I am just wondering how much of the topic of these 3 subjects fromm level 1 is repeated in level 2. If it is a lot of ground to cover, i should perhaps take re-read or even take classes for them. Do not want to be struggling in the end. Thanks for support.
Don’t revisit stuff from L1, start afresh from L2 and just hammer the material out.
Make it one of the first topics you study.
No need to revisit. Quant in level II is all about regressions. It’s a different beast. FI introduces harder concepts like shaping risk, binomial tree valuation, credit risk and CDS. Level 1 concepts like plug-n-play type calculations won’t help much here. Just start fresh and focus on the content presented in the level II curriculum.
Thanks guys… will follow the advice and start frm quants
Good luck! Quant in L2 is much rougher imo than in L1.
Since it’s early and there is a lot of time, if you feel weak with hypothesis testing and confidence intervals (especially which stat to use for 1-tail or 2-tail tests at different alpha), I would review those concepts introduced in L1 before tackling L2 quant.
In all honesty, I thought it really wasn’t that bad.
Each to their own but it mainly consisted of hypothesis testing and regression etc.
If I never ever ever ever have to read the words Auto regressive conditional heteroskedasticity again so long as I’m alive… it won’t be too soon.
#goodluck
Quant L2 is a different animal, don’t waste your time revisit L1 material.
I would say if you’re weak with z scores, hypothesis testing, etc you might want to spend some time reviewing it. This stuff comes up in Level 2 and level 3. It’s not very easy but with practice it should be.
I’m pretty sure that you mean that it _ will _ be too soon.
It is quite simpler than you think. It’s all about the errors. Errors of an AR equation are not equally dispersed along the time, so they are heteroskedastic.That’s bad for OLS, so there are a few methods for correcting the problem.
The most you will need to refresh on are confidence intervals, hypothesis testing (test statistic, critical value, p-value)