Questions for quants

[Angipapi]

This is my plan for after I finish the prequisitive maths for quantitative finance. The book choices are from another forum.
I have several questions about them.

Notes by david lando and rolf poulsen - alternative to Shreve1 (better imo)
2 - bjorks book on arbitrage theory - alternative to Shreve2 (more to the point imo)
3 - C++ by Bjarne Stoustrup, or joshis book
4 - Econometrics, Fumio Hayashi
5 - Glassermans book on MC
6 - A good book on PDEs for finance, Wilmotts might be a try but I don't know that book
(Option pricing: Mathematical models and computation)

(I am planning to learn stochastic processes from a book called mathematical modelling and computation in finance.)
1.Do Tomas bjorks and shreve’s books include enough measure theory?

2.Is Tomas Bjork a good alternative to shreve?
I saw that Tomas bjork doesn’t have anything on jump processes.

3.Are David landos notes a possible alternative for Shreve 1 and if so are they better or worse?

4.is the econometrics books attached any good?

 

[emetrics]

A few cents...

3) - I'm probably biased since I followed several courses by RP and loved every one of them. I thought the notes gave a very nice intro to discrete time finance and provided a relative smooth transition to e.g. Björk, which is also a very well written book.

4) - Yes, Hayashi is definitely considered a good graduate econometrics book for time-series analysis. It provides a lot of theory with GMM as a unifying base, which one may like or dislike. Depending on the field of econometrics you're interested in, you may also consider other books. For primarily cross-sectional and panel type data, books such as "Papa" Wooldridge (2010) and Cameron-Trivedi (my favorite) are popular. For financial econometrics, Tsay or Taylor are fine intro books while Francq & Zakoian (2019) is more advanced and technical.

 

[Angipapi]

Hi Emetrics,
Thanks for answering so fast!
I have a few questions about what you wrote.
What would be the difference between a time series analysis focused econometrics book and a financial econometrics book, eg difference in focus?
Also, do you believe I should supplement jump processes from Shreve 2?

 

[Angipapi]

Would you mind answering the bottom four questions?

 

[Daniel Duffy]

6)
2022 style state-of-art PDE/FDM (subumes all pde books in finance).

Numerical Methods in Computational Finance: A Partial Differential Equation (PDE/FDM) Approach

<p>This book is a detailed and step-by-step introduction to the mathematical foundations of ordinary and partial differential equations, their approximation by the finite difference method and applications to computational finance. The book is structured so that it can be read by beginners...

www.wiley.com

Online Courses :: Datasim

www.datasim.nl

3) Joshi C++ is 1990s and (severely) outdated.

The best C++ training is Quantnet C++.

 

[dangtransontung]

a bit curious on your preq prepration. what did you learn?

 

[Angipapi]

a bit curious on your preq prepration. what did you learn?

Calculus 1-3 and Linear Algebra.
I'm gonna start differential equations, Probability and then stochastic processes

 

[emetrics]

Hi Emetrics,
Thanks for answering so fast!
I have a few questions about what you wrote.
What would be the difference between a time series analysis focused econometrics book and a financial econometrics book, eg difference in focus?
Also, do you believe I should supplement jump processes from Shreve 2?

Time series refers to the structure of the data, i.e. timely ordered variables, as opposed to cross-sectional or panel/longitudinal data. Financial refers to the topic/genre of econometrics that you're studying. Most often in financial econometrics you'll be working with financial time series, but the topic may just as well include cross-sectional data and analysis. Financial econometrics, at least when working with financial return series, is traditionally more skewed towards volatility inference rather than expected return inference, since it's a more feasible task.

Wrt. supplementing - Well, since Björk doesn't contain anything/much on jump processes and you seem keen on learning it, you don't really have a choice but to supplement. In general I like to consult several different books when learning a topic to get different views from different authors, but each to their own.

Wrt. remaining questions - I didn't (and I'm not going to) answer your remaining four original questions, since I'm no expert on neither C++ or numerical PDE computations. Daniel Duffys tips is most likely your best bet there.