Good books for a relative amateur

Hemant Dua · 8/5/14

Hello people,

I had a paper in Mathematical Finance in the final year of my undergraduation (I'm a math grad, btw, looking to pursue an MFE next). The two recommended texts I read for the same were Options, Futures and Other Derivatives (John C. Hull) and Investment Science (David Luenberger).

Given that I'm a relative amateur in need of greater exposure to finance, and specifically, quantitative finance, what books should I proceed to read next? I've seen the Master reading list for Quants, MFE (Financial Engineering) students, however it's hard to figure which ones I'll be able to comprehend easily. Which book is the next step in the right direction after Hull and Luenberger?

I would appreciate any suggestions. Thanks.

zebullon · 8/5/14

I'd say "Stochastic calculus for finance 2" by Shreve.
It is clear, proofs are there and the style is easy to read... Typos list are available and corrections to exercises too.

Hemant Dua · 8/5/14

http://www.amazon.com/Stochastic-Ca...-1&keywords=Stochastic+calculus+for+finance+2

^This one, isn't it?

What about the first part of the same? Are most of its topics already covered in Hull?

Thanks a lot for the reply. Much appreciated.

zebullon · 8/5/14

Yes it's this one.
The first volume is about discrete models and the maths u'll find inside are of few interest to math undergrad (also u can find most of the binomial model discussion already in Hull) I think.
U'll find the necessary concepts from vol.1 recalled and developed more thoroughly if necessary in vol.2

I could not say however how much they overlap but I don't think Hull speak about Feynman Kac for example ? or go in technical details about change of measure... Someone could correct me on this point.

Hemant Dua · 8/6/14

The binomial model is indeed discussed in detail in Hull. And yeah, no mention of Feynman Kac there. So I suppose I should just purchase Vol.II then.

A big thank you once again, zebullon. Do you have any other suggestions?

What about the rest of the people here? Are there other books I should look at right now for Stochastic Calculus? Or derivatives pricing?

Jedison · 8/6/14

A First Course in Probability/Introduction to Probability Models by Ross, and Rudin's Principles of Mathematical Analysis are some of my favorites.

Jedison · 8/6/14

Shreve's Cont. Time Models is a really excellent intro if you're looking for a basic overview of stochastic calculus' main theorems, however, I find it a bit light on the theory. For most people I don't think it's a problem but I found most of the proofs to be quite hand-wavy. To be fair, though, true understanding of stochastic calc won't be achieved during an MFE which is what this book was designed for

Hemant Dua · 8/6/14

I see, Jedison. I've actually studied from Sheldon Ross' texts before, but those were borrowed from the University library.

I've heard that Mood and Graybill is the best for Probability and Stats. What do you reckon?

Jedison · 8/6/14

Hemant Dua said:
I see, Jedison. I've actually studied from Sheldon Ross' texts before, but those were borrowed from the University library.

I've heard that Mood and Graybill is the best for Probability and Stats. What do you reckon?

I can't comment as I haven't studied from them before. Ross' texts are nice because they provide a good amount of questions on combinatorics, markov chains, and poisson processes that are good for interview prep. I find myself still going back to A First Course in Probability to drill through tricky counting problems

Hemant Dua · 8/6/14

Would you say it covers most elementary topics in probability, from continuous/discrete distributions to joint density functions and linear regressions, all the way through to the Law of Large Numbers, the Central Limit theorem and Markov Chains?

Thanks for the prompt response, btw

Jedison · 8/6/14

Hemant Dua said:
Would you say it covers most elementary topics in probability, from continuous/discrete distributions to joint density functions and linear regressions, all the way through to the Law of Large Numbers, the Central Limit theorem and Markov Chains?

Thanks for the prompt response, btw

Linear regressions no, but all other topics yes. Introduction to Probability Models does a nice job with both discrete and continuous markov chains. The other topics are all touched upon in A First Course in Probability

Hemant Dua · 8/6/14

Thanks, man.

Just found out that a good pal of mine has already discovered a free eBook for the same :D

Jedison · 8/6/14

Ya was going to mention I was able to find pdfs for all of the aforementioned (before purchasing hard copies). Nothing wrong with trying before you buy

Hemant Dua · 8/6/14

Jedison said:
Ya was going to mention I was able to find pdfs for all of the aforementioned (before purchasing hard copies). Nothing wrong with trying before you buy

Cheers, mate

Hemant Dua · 8/6/14

Does anyone else have further suggestions?

The suggestions so far are much appreciated.

presto · 8/14/14

Sheldon Ross' book A First Course to Probability seems to have assumed mythic proportions, but read the reviews of daily users on Amazon. I prefer much more the books by Carol Ash and Henk Tijms:

http://www.amazon.com/gp/product/0780310519/ref=cm_cr_asin_lnk

http://www.amazon.com/Understanding...?ie=UTF8&qid=1408004263&sr=8-1&keywords=tijms

The book of Henk Tijms also contains a lot of probability stuff of interest to quantitative finance.

Good books for a relative amateur

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