Paper: https://www-2.rotman.utoronto.ca/~kan/3032/pdf/FinancialAssetReturns/Garman_Klass_JB_1980.pdf
Could anyone confirm whether equation 11 is correct?
I was under the impression that a Jacobian scaling factor of \lambda^3 is required for the equality to hold mathematically?
EDIT: Changed question to focus on the correctness of eqn 11 rather than eqn 12.
Prior to AI, I had huge respect for academia and the pursuit of understanding complex things was a big driver for me. Now it’s hard to justify the effort. I could read published papers and spend time understanding them back-to-front, or I could simply ask AI to “explain it in simple terms and verify that it’s correct”.
Obviously AI can be a big help in understanding such papers (where I’d otherwise have to google a lot), but the value in understanding stuff seems trivial now.
So I guess my question has two parts:
* Is human understanding of stuff AI can handle meaningful; and
* If so, does anyone have any tips on being disciplined enough to not simply take an AI models answer as gospel and to use it to develop professionally/intellectually?
It’s almost 2 decades old, but the preface suggests its content should be “timeless” since it focuses on market mechanics rather than specific strategies.
I’ve read the first chapter. Nothing in there I strongly disagree with, but some lazy/imprecise use of language and minor numerical errors. Wondering whether it’s worth continuing with this one.
Specifically in relation to the following two points:
* Discuss the value of different people skills.
* Discuss the advantages of communicating clearly.
The game is done independently and communicating isn’t really a part of it. Do I just put some generic/hypothetical waffle to check this box?
As an aside, how harsh is the marking of the business game reflection and business report? If they fail you, do they give you feedback for how to improve it for the resubmission?
I have a DC pension with Aviva from my previous job and one with WTW (LifeSight) from my current role.
Both are in the highest risk fund (default for my age).
Over the past 5 years, the aviva fund is up around 50%. LifeSight is up 94%.
Over the past 1 year, aviva is up 25% and LifeSight is up 34%.
I’m aware that past performance is no promise of future returns, but would it be wise or unwise to transfer my aviva pension to LifeSight?
I’ve been building some python models recently, just for fun. So far, I’m using the FRED API to bootstrap yield curves and the yfinance library to access option chain data.
Since I’m just doing this for fun, data lag isn’t an issue. Data quality isn’t super important. What matters more to a that the data is free (or very cheap) and ideally accessible by API.
‘Data’ is intentionally vague - most market data would be worth having if it’s free to access and I’m sure I’d find a use for it. However, specific examples of data I’m hoping to use are:
* CDX spreads would be particularly useful at the moment though.
* Interest rate derivatives (probably not possible, since most are OTC).
* European option prices (tbh I don’t actually know if these exist - most actually traded options seem to be American)
Regarding the last one: I thought it would be fun to try and approximate the value of the “Americanism” of options (relative to their European counterparts). It would therefore be awesome if I could access prices for both American and European options on the same underlying to validate the accuracy of my approximations. However, it seems unlikely to me that such options exist (or that deep liquid markets for both option styles exist on the same underlying).