Copy my EXACT resume and cover letter, Kick start your quant applications with a 33%-DISCOUNT Fall Application Sale using code RRWDV1T4 buymeacoffee.com/myquantitative/extras Need interview advice or want to chat? Drop me an email at myquantitative@gmail.com Practise makes Perfect, more interview questions: ruclips.net/p/PLaOlxMtlLosX8V6f3U_wZeMgNftNW_RYz Unconventional advice aspiring Quants are not using enough! : ruclips.net/video/BKs_1lVTNrs/видео.html
Theres an intuitive solution that is much easier -- the expected covariance between Y an Z is .64, and the maximum possible covariance is 1 in the case where Y and Z are directly proportional. The covariance between Y and Z is symmetrically distributed, so the lower bound is .64 - .36 = .28
Max is obviously 1 when they are aligned, for min just use the cosine rule. Correlation is like cosine in n dimensions, so cosx =.8, cosy =.8, i.e. x=y, so min is cos(x+y) = cos(2x) = 2cos^x-1 = 2*.8^2-1 = .28, similarly max is cos(x-y) = cos0=1.
@diegosic7520 Thank you for the comment! Truly appreciate it! However, like many industries, the interview process and methods of assessment are not always representative of the actual responsibilities of a Quant. Like software engineers with coding questions, some questions faced during the quant interview process can be unnecessarily complex, meant to be a subtle IQ test and hidden tax on one's time (practice on enough volume of questions and some patterns may emerge). That being said, I try my best to place myself in the shoes of someone preparing for such interviews, particularly someone from a non-traditional background (Quants have a reputation for being math and CS geniuses but the truth is far from it, most of us just have a proclivity for numbers and sciences and just want a job) when illustrating these interview problems.
Glad to see jane street question :) Thought this problem was not very satisfying but great solution and explanation nonetheless. Look forward to new videos
Thank you! @TheYeti Yes I was keen to get one of these out ASAP. True, it's definitely the easy end of the spectrum of Jane-Street style questions but I also keep in mind that quant questions can truly span a wide breadth of complexity, from the shockingly trivial to I-don't-even-know-where-to-begin.
My university suggested Quantitative Methods (unsure though if this was a class offered previous years as I have not seen it on their lists before), Calculus, Linear Algebra, Probability and Statistics. Also taking programming as electives because most firms want you to know stuff like Python and OCaml.
@dark_lord98 @taylal9200 Thank you for the comment and the suggestions. @taylal9200 suggestions nailed it. Most rigorous STEM courses would likely have you encounter most, if not all of these courses, to various levels of depth and complexity. You will likely still have to supplement a lot of learning by yourself. I would also add that getting relevant real-world experience as quickly as possible via internships and research opportunities. These do not necessarily expect you be applying cutting edge mathematics, nor even be in a finance specific context, but it is an opportunity to pick up other critical skills such as data wrangling, research methodology and presentation skills. Regarding programming, being able to develop expertise and demonstrating them through projects and work expertise in one popular language will go further than just listing a long list languages you may have used once or twice. Unless you are aiming for highly specialised roles that require specialised languages, working in a popular language like Python is sufficient. Firms that use multiple languages expect that you can pick up new fields and languages fast, it's unrealistic to expect an applicant to bring a high level of proficiency across multiple fields from the get-go. Such applicants exist and naturally the top firms get their pick, but it's unrealistic for all firms to impose strict requirements because they would just be shooting themselves in the foot. I personally only had demonstrable experience in Python and MATLAB, I had C++ through an elective but I honestly never learnt it well nor did I do well on that, hence I never used it as a talking point in interviews. tldr: Any rigorous STEM subject should expose you to most of the required fields, some better than others. Lean towards applied mathematics, computer science, physics, EEE but also supplement with self-study and real-world experience with internships and research experience.
Hi, I am currently pursuing a degree in Actuarial Science. I am slowly gaining interest in quant. Would you say I can still become a quant with this degree? It's not as rigorous as a degree in pure mathematics, and most of the time I'll be using R. My plan to break into quant would be to pursue a masters in quant finance after a few years of working as an actuary, and probably some quant internships after my masters before landing a permanent role. What are your thoughts on this. Btw, really interesting video, quite a very nice way to solve it. Though I doubt I could think of a solution straight away in an interview setting, at least not at my current level. Thank you in advance 👍👍
@zigbo5659 Thank you for the support and I appreciate the comment! Every little bit helps the algorithm! You can still become a quant with that degree. There are many people with degrees that are not as rigorous as pure mathematics who become quants. Plenty of engineering students have made such a switch. There is plenty of mathematics there but as somebody who went through such a degree, I wouldn't place it within the mathematically rigorous category yet. Your plan is the safest path. A Masters in Financial Engineering is the typical path for individuals looking to make the transition. who lack a sufficiently transferable skillset to enter an entry level quant role e.g. engineers. Assuming you have a sufficiently strong mathematics background (in fact your grounding in statistics and probability from Actuarial Science will be an excellent foundation here), the MFE will serve as a "second chance", equipping you with a more focus set of quantitative finance knowledge and most importantly, allowing you to be considered as a student again and apply for the coveted internships and research experience. With this refreshed C.V. in-hand, coupled with some months of interview prep, you should be able to land an entry-level quant role. Of course the usual caveats apply with regards to picking the right school and programme. Unfortunately, I do not have experience here since I do not possess a MFE, but the RUclipsr Dimitri Bianco has spoken about this at length on his channel, so I defer all opinions to him on this matter! My only concern would be your programming experience, I'm unsure how much your degree taught you or if you are self-taught, but I would INSIST on getting proficient in Python and if time permits (unlikely), in a C language as well. Your MFE programme should have a course teaching you one of the C languages but I wouldn't recommend having that as your first experience, the C languages are not kind to beginners, much less if the instructor is lackadaisical in teaching it. In the meantime, before you start your MFE, I would also recommend picking up as much machine learning knowledge as possible. This can open up more opportunities during your MFE and hence, affecting your trajectory after your MFE as well. If you're not already familiar with Python, this will be a bonus since you will definitely have to use Python during your process of studying machine learning. Will the role you eventually land be at a top-firm with the famous eye-watering compensations? That's another question and also a function of how hard you push yourself! Whereabouts are you based in the world? Always curious who are clicking on my videos!
Copy my EXACT resume and cover letter, Kick start your quant applications with a 33%-DISCOUNT Fall Application Sale using code RRWDV1T4
buymeacoffee.com/myquantitative/extras
Need interview advice or want to chat? Drop me an email at myquantitative@gmail.com
Practise makes Perfect, more interview questions: ruclips.net/p/PLaOlxMtlLosX8V6f3U_wZeMgNftNW_RYz
Unconventional advice aspiring Quants are not using enough! : ruclips.net/video/BKs_1lVTNrs/видео.html
Theres an intuitive solution that is much easier -- the expected covariance between Y an Z is .64, and the maximum possible covariance is 1 in the case where Y and Z are directly proportional. The covariance between Y and Z is symmetrically distributed, so the lower bound is .64 - .36 = .28
What does it mean that covvariance should be symmetrically distributed and why the solution is (0.8)^2 - (1 - (0.8)^2)?
@@franokkkappa8882 Covairance follows an eliptical distribution and eliptical distributions are symmetrical.
Why is the expected variance .64?
Max is obviously 1 when they are aligned, for min just use the cosine rule. Correlation is like cosine in n dimensions, so cosx =.8, cosy =.8, i.e. x=y, so min is cos(x+y) = cos(2x) = 2cos^x-1 = 2*.8^2-1 = .28, similarly max is cos(x-y) = cos0=1.
Smart method dude!
What’s the logic behind min being cos(x+y) and max being cos(x-y)?
Great video! You are seriously making me consider this profession jus because of the way you explain the problems
@diegosic7520
Thank you for the comment! Truly appreciate it!
However, like many industries, the interview process and methods of assessment are not always representative of the actual responsibilities of a Quant. Like software engineers with coding questions, some questions faced during the quant interview process can be unnecessarily complex, meant to be a subtle IQ test and hidden tax on one's time (practice on enough volume of questions and some patterns may emerge).
That being said, I try my best to place myself in the shoes of someone preparing for such interviews, particularly someone from a non-traditional background (Quants have a reputation for being math and CS geniuses but the truth is far from it, most of us just have a proclivity for numbers and sciences and just want a job) when illustrating these interview problems.
How do you make these really good visuals? Do you use Manim? Great video by the way
Glad to see jane street question :) Thought this problem was not very satisfying but great solution and explanation nonetheless. Look forward to new videos
Thank you! @TheYeti
Yes I was keen to get one of these out ASAP. True, it's definitely the easy end of the spectrum of Jane-Street style questions but I also keep in mind that quant questions can truly span a wide breadth of complexity, from the shockingly trivial to I-don't-even-know-where-to-begin.
Bro can you suggest what topics should i study in maths to get into quant field ?
My university suggested Quantitative Methods (unsure though if this was a class offered previous years as I have not seen it on their lists before), Calculus, Linear Algebra, Probability and Statistics. Also taking programming as electives because most firms want you to know stuff like Python and OCaml.
@dark_lord98 @taylal9200
Thank you for the comment and the suggestions.
@taylal9200 suggestions nailed it. Most rigorous STEM courses would likely have you encounter most, if not all of these courses, to various levels of depth and complexity. You will likely still have to supplement a lot of learning by yourself. I would also add that getting relevant real-world experience as quickly as possible via internships and research opportunities. These do not necessarily expect you be applying cutting edge mathematics, nor even be in a finance specific context, but it is an opportunity to pick up other critical skills such as data wrangling, research methodology and presentation skills.
Regarding programming, being able to develop expertise and demonstrating them through projects and work expertise in one popular language will go further than just listing a long list languages you may have used once or twice. Unless you are aiming for highly specialised roles that require specialised languages, working in a popular language like Python is sufficient. Firms that use multiple languages expect that you can pick up new fields and languages fast, it's unrealistic to expect an applicant to bring a high level of proficiency across multiple fields from the get-go. Such applicants exist and naturally the top firms get their pick, but it's unrealistic for all firms to impose strict requirements because they would just be shooting themselves in the foot. I personally only had demonstrable experience in Python and MATLAB, I had C++ through an elective but I honestly never learnt it well nor did I do well on that, hence I never used it as a talking point in interviews.
tldr: Any rigorous STEM subject should expose you to most of the required fields, some better than others. Lean towards applied mathematics, computer science, physics, EEE but also supplement with self-study and real-world experience with internships and research experience.
@my_quantitative thanks for explaining
js reject me at the resume level, you hv my respect
@tsunningwah3471 Just Jane Street being Jane Street. But there is more to the quant world than just Jane Street!
Same
I actually got past the resume stage but do not know if I can even solve these problems. I have not done Maths in 7 years!
@@travelvideoz are u newgrad? or have been working for sometime
Hi, I am currently pursuing a degree in Actuarial Science. I am slowly gaining interest in quant. Would you say I can still become a quant with this degree? It's not as rigorous as a degree in pure mathematics, and most of the time I'll be using R.
My plan to break into quant would be to pursue a masters in quant finance after a few years of working as an actuary, and probably some quant internships after my masters before landing a permanent role. What are your thoughts on this.
Btw, really interesting video, quite a very nice way to solve it. Though I doubt I could think of a solution straight away in an interview setting, at least not at my current level.
Thank you in advance 👍👍
@zigbo5659
Thank you for the support and I appreciate the comment! Every little bit helps the algorithm!
You can still become a quant with that degree. There are many people with degrees that are not as rigorous as pure mathematics who become quants. Plenty of engineering students have made such a switch. There is plenty of mathematics there but as somebody who went through such a degree, I wouldn't place it within the mathematically rigorous category yet.
Your plan is the safest path. A Masters in Financial Engineering is the typical path for individuals looking to make the transition. who lack a sufficiently transferable skillset to enter an entry level quant role e.g. engineers. Assuming you have a sufficiently strong mathematics background (in fact your grounding in statistics and probability from Actuarial Science will be an excellent foundation here), the MFE will serve as a "second chance", equipping you with a more focus set of quantitative finance knowledge and most importantly, allowing you to be considered as a student again and apply for the coveted internships and research experience. With this refreshed C.V. in-hand, coupled with some months of interview prep, you should be able to land an entry-level quant role. Of course the usual caveats apply with regards to picking the right school and programme. Unfortunately, I do not have experience here since I do not possess a MFE, but the RUclipsr Dimitri Bianco has spoken about this at length on his channel, so I defer all opinions to him on this matter!
My only concern would be your programming experience, I'm unsure how much your degree taught you or if you are self-taught, but I would INSIST on getting proficient in Python and if time permits (unlikely), in a C language as well. Your MFE programme should have a course teaching you one of the C languages but I wouldn't recommend having that as your first experience, the C languages are not kind to beginners, much less if the instructor is lackadaisical in teaching it.
In the meantime, before you start your MFE, I would also recommend picking up as much machine learning knowledge as possible. This can open up more opportunities during your MFE and hence, affecting your trajectory after your MFE as well. If you're not already familiar with Python, this will be a bonus since you will definitely have to use Python during your process of studying machine learning.
Will the role you eventually land be at a top-firm with the famous eye-watering compensations? That's another question and also a function of how hard you push yourself!
Whereabouts are you based in the world? Always curious who are clicking on my videos!
is this username supposed to be a reference to the big short scene lol
@jwh523 my name is NOT Yang, I do speak English and I am a winner of no math competitions :(
n1
Cheers @lorenzobaggi161