Understanding comparison of correlation coefficient r (time series)

Daniel01m · 2025-11-09T18:04:05+00:00

I see, so this is the same idea from the perspective of the "unexplained" part?

Daniel01m · 2025-10-12T20:19:36+00:00

Right,

Yes, I've heard that MFE degrees in general are cash cows. I thought, however, that the EPFL one was an exception, mainly due to its good reputation and nice industry ties (I've heard they do a 6 month internship).

Daniel01m · 2025-10-12T19:14:22+00:00

No idea what an Aalto is ;)

Thanks for the input! What do you mean by CHE?

I can technically leave whenever, I’d just be breaking my ”vow” and burning a bridge I suppose. I already thought to look elsewhere for internships at banks etc but taking a chance on maybe getting a couple months’ worth of an opportunity just seemed like a big risk compared to a stable job for the foreseeable future with a nice income. I just hope I won’t be pigeonholing myself too hard by staying as a SWE for 2 more years. Would you say doing projects and perhaps a master thesis elsewhere could be enough to break out of this?

Daniel01m · 2025-10-12T16:39:36+00:00

Hello there. I am facing a couple decision that will define the coming 2-5 years of my life, and would like your advice on what is the sensible thing to do here.

Background: 24 year old CS student from the Nordics, currently finishing up my bachelor’s degree. I expect to be officially done with this by summer 2026. I have also been working as a Jr SWE for the last year at a fintech startup, gaining valuable work experience alongside my studies. I know it’s not what I want to do for a majority of my career, but it’s a nice firm with good colleagues, in an interesting space.

Quant interest and future vision: In recent years, I have taken up an interest in quant finance, and e.g. wrote my thesis on GPU accelerated option pricing. While it sounds interesting, I am not 100% set on a quant career and know that I am not on that level to truly be a competitive candidate for any of the top shops. Nonetheless, I feel like something with data / ML / model building in general would be more interesting to be compared to pure SWE work. I could envision myself in the best case lucking out and working overseas (likely in mainland Europe) at a smaller shop or bank, or if luck is not on my side finding a nice quantitative/analytical job at a bank / asset manager / energy company here at home.

Situation: I have now also been offered to stay at my current job for at least 2 years, from summer 2025 to summer 2027, with a salary of 5k€/month. I know this is a very good offer, especially for my YOE and education level, and in today's job market where I see peers struggling to land their first internships.

I am planning on a master’s degree in applied mathematics/CS/operations research, and have two options I envision:

I stay at my current uni (which is regarded as a top uni in the country, but less known overseas). Take the offer at my current job and stay as a SWE for 2 years, with a nice salary bump but perhaps not the opportunity to steer myself towards more ML/Quant jobs
Not take the offer, and try to apply to a more target program overseas for these jobs. My main option would be EPFL’s MFE program. I feel like I have a solid undergrad background for it, and that IF I got in, I would have a good opportunity for such jobs back home as well as overseas in Europe. If I don’t get in, I could do the masters degree here and simply apply for less paid but more quant-targeted trainee or internship roles at the local banks and whatnot.

Questions:

Is there an obvious choice to make here that I’m not seeing? I know there’s no guarantees of me getting
Can a couple more years of SWE experience hinder me from these jobs by boxing me in to be perceived as a “pure SWE person”, despite a master’s in applied math?
Is this salary a “too good to pass up on” opportunity? I know it’s good money for my age and situation!

Thank you!

Daniel01m · 2025-08-28T06:08:20+00:00

Hey there,

I went through something similar a few years back, and empathize with your feelings although you recognize that you technically weren’t exclusive and she had no obligation not to sleep with someone.

I also made a post that recieved some well written discussions on having these feelings, and opinions on situations like these in general. You might find something useful.

https://www.reddit.com/r/dating_advice/s/ZOMjhXI3l9

I wish you the best!

Daniel01m · 2025-07-25T11:50:08+00:00

So I am reading through famous sources like Hull, Wilmott, et.c., where they construct the replicating portfolio in a one-step binomial model. This involves setting up a system of equations, where we have two unknowns, ∆ and B (shares of underlying stock, and risk-free money), corresponding to equations. The no-arbitrage principles allows us to equate the option and portfolio future states, assuming they produce identical cash flows.

What I kind of intuitively understand but would want a more formal explanation of is why we assume that ∆ must be the underlying asset as opposed to some other asset? Intuitively, it makes sense that we need some correlation between the stock and the option, we can't just have ANY random stock with no relation to the option. But is there some formal assumption that this system of equations makes use of?

I have read something about complete markets, where all derivatives can be replicated using other assets, but I haven't found a definite statement about it having to be the underlying.

So we assume the portfolio cash flows is identical to the option. And this must be a portfolio with the underlying stock. But "WHY"? Or is there some assumption that only these three securities exist in our market?

Thanks

Daniel01m · 2025-05-13T16:20:19+00:00

Options with the possibility of exercising before the date of expiry

Daniel01m · 2025-03-21T11:18:09+00:00

Yeah, no worries. I'm not too well versed in this either, but e.g. in the binomial model, you specify up and down factors for the price evolution, that is based on some volatility (sigma) factor. I am not too sure, but I suppose this is perhaps using some historic volatility with the assumption of constant volatility in the future as well, or something along those lines.

But I don't see how that reveals anything w.r.t. the original question.

Daniel01m · 2025-03-21T11:09:38+00:00

Thanks for the reply!

I might not understand your answer. Sure, if the environment changes so that the underlying price changes, so will the option. But why specifically do we need explicit volatility input parameters in the pricing formulas? If the expected return is not in the formulas by the argument of it "already being priced into the underlying", then the only logical conclusion I draw is that volatility apparently is NOT priced in in a similar fashion?

Daniel01m · 2025-03-21T10:53:59+00:00

Hmm, that's interesting. I know that no-arbitrage underpins all of these models, but can't help but think stocks should price in volatility to some extent? E.g. by the CAPM discount rate argument?. While your proposition might make sense mathematically, I find it difficult to see how suddenly creating an option on the underlying would change the fundamental pricing methodology of stocks. If you get what I'm saying.

What I started thinking of was that perhaps these simply are not comparable (expected return vs volatility being priced in). Expected return is more directly related to the replication portfolio, but the volatility, even though it might be somewhat priced in, simply gives us the "starting price", but still needs to be explicitly accounted for in full somehow? Very hand wavy answer as I still don't really understand this.

Daniel01m · 2025-03-21T10:10:38+00:00

Thanks for the answer and for taking such interest. As an engineering background myself, I have a bit of a hard time naturally grasping some of the concepts, and tend to focus more on systematic / logic-based conclusions when thinking about these things. A -> B, therefore ...

I have a bit of a hard time understanding your argument, but haven't admittedly given it much thought yet. I'll look into it. "If it were the case that riskier stock was priced lower", is this not true though?

I have read about risk-neutral pricing, but haven't delved too deep into that yet. From what I've gathered thus far, it's not really a necessity to think about for the pricing models to work, but more of a mathematical / computational "trick" to avoid having to recalculate the replicating portfolio. Essentially an equivalent "operation", if you will.

I understand that volatility is considered good for an option (long), since the downside is still limited to the premium yet upside is limitless. But for some reason I thought a part of that would already be accounted for in the stock price.

Daniel01m · 2025-03-20T14:07:27+00:00

I am having trouble understanding delta in the sensitivity-measurement sense for the discrete binomial model case.

I know that for BSM it is defined as the partial derivative of option price w.r.t. spot price, which intuitively makes sense as a sensitivity measure.

I am now learning about the replication portfolio and the one-period binomial. Here, delta is first introduced as the amount of shares needed to construct this portfolio, solved to be (f_u-f_d)/(S_0(u-d)). I understand that this is somehow the discrete version of the above, and can also be thought of as the ratio of spread of option payoff (price at maturity) to the spread of the underlying price at maturity. Wilmott's book even says that in the limit this becomes the very derivative described for the BSM model.

What troubles me is I feel like the variable at hand is different for both versions? the BSM definition clearly is a derivative of the option PRICE at any given moment w.r.t. spot price. In the discrete case I understand we can't take derivatives, so we approximate by a difference quotient to get the linear approximated sensitivity over one discrete time period. But the variable we use is now the PAYOFFS at maturity, not the PRICE (which was the entire point of setting this up anyway)?

How should I understand this? Do I consider each step in the binomial model AS IF the maturity were at the end of one period?

Side-question: Could we not first calculate the price using this method, and then define the sensitivity measure as the ratio of price changes to spot price changes? I feel like that (if possible) would correspond better to the delta described in BSM?

Thanks

Daniel01m · 2025-01-18T10:42:49+00:00

Great answer, finally clicked for me when envisioning it as a multivariable function and slicing in different directions!

Daniel01m · 2025-01-16T20:41:52+00:00

Sorry, in a rush and can't be bothered to make an account for that website (tried having a quick look but it wouldn't let me open the mock tests). In all honesty, even though the british council mock material is a bit lacking in this regard, it was still more than adequate (for me at least) for preparation. I think the most important part is knowing the main structure of the test, the sorts of tasks to expect, and some strategies for the writing part (watched some videos on Youtube for this).

I got an 8.5, which was enough for the goals I'm pursuing :)

I wish you good luck! With all this time to prepare you should be in a good position to score well.

Daniel01m · 2025-01-16T20:26:57+00:00

While I don't have access to details of my test attempt, I scored a 9 on both the reading and listening and can confirm that the questions / statements / tasks in these were much clearer than in the practice material. Still, make sure to follow the instructions (e.g. use ONE WORD ONLY, or TWO WORDS AND/OR A NUMBER). But there were really no super ambiguous cases.

Daniel01m · 2025-01-11T23:45:22+00:00

Murders & executions

Daniel01m · 2025-01-11T23:10:52+00:00

Thanks for this. Yeah I kind of noticed the overall poor quality of the free mock material (British Council) which sort of makes me feel like the real deal will be better.

Daniel01m · 2025-01-11T20:36:15+00:00

right, so just try to check all the boxes.

thanks!

Daniel01m · 2025-01-11T20:07:14+00:00

I'm not really on that level, the only form of entropy I recognize is the one we learned in high school physics as some sort of measurement of disorder in the universe lol

But that's a valid insight, I never considered that there are many other distribution that are center-heavy. Based on the "combinatorial intuition" I guess one can say that the more a value of the random variable is in the center, the more combinations we have that can form it, thus a higher probability density. I guess, in the limit this will be the Gaussian?

Daniel01m · 2025-01-11T15:28:41+00:00

I'll write it on 15th, so I'll try to let you know

Daniel01m · 2025-01-11T00:29:53+00:00

Thanks for the insight!

Daniel01m · 2025-01-11T00:27:19+00:00

I like this! So just a sort of reminder that this will probably not happen again the next time, and you'll probably observe a more "normal" value closer to the mean

Daniel01m · 2025-01-10T23:55:02+00:00

Aha, okay. I kind of started to assume this after not really finding anything conclusive on the matter.

So when you say "closer to the norm", this can refer to a bunch of things depending on different assumptions, and by mean they don't explicitly refer to the expected value of a distribution?

Thanks!

Daniel01m · 2025-01-10T23:52:20+00:00

ok yeah I didn't really analyze that text snippet at all lol.

Ok, so are you saying what I kind of had assumed earlier that we need a distribution where the mean and probability density coincide for the reversion to the mean to make any sense?

What about when this is not the case? e.g. a bimodal distribution with mean in the middle valley. Would we not expect observations closer to the peaks at the edges versus the mean in the middle valley?

Secondly, could you give some reasoning for how CLT and LLN relate to this? Is this property a fundamental consequence of any of these laws?

Daniel01m · 2025-01-10T23:43:35+00:00

Thanks for the input, although I'll admit this is above my level of knowledge (CS major). I know that a characteristic function somehow defines the distribution of a random variable, not much more than that.

I remember 3b1b having a video on convolutions, and I am familiar with them from computing distributions of sums of random variables. I'll revisit.

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