Stochastic Processes

[mez]

Hey Guys,

I am trying to take a Stochastic Processes class this summer, however, different schools have different focuses on the subjects covered. I wanted to know what are the most important subjects in Stochastic Processes course that I should be looking for; my goal is to prep for a MFE. If anyone has any idea, please share, thank you so much.

 

[siladm]

While taking a course in Stochastic Processes is very helpful, ideally you should take a course with emphasis on Stochastic Calculus to best prepare for an MFE.

Things you should take away from a course in Stochastic Processes:
- Wiener process
- Poisson process
- Martingale property
- Markov property
- Quadratic variation
I am sure that most of the courses out there should cover these.

The key topics in Stochastic calculus are:
- Ito integral
- Ito process
- Ito formula for stochastic differential of an Ito process
- Radon-Nikodym derivative and change of measure
- Girsanov theorem (with Novikov condition)
- Stochastic differential equations

Hope this helps.

 

[Michsund]

While taking a course in Stochastic Processes is very helpful, ideally you should take a course with emphasis on Stochastic Calculus to best prepare for an MFE.

Things you should take away from a course in Stochastic Processes:
- Wiener process
- Poisson process
- Martingale property
- Markov property
- Quadratic variation
I am sure that most of the courses out there should cover these.

The key topics in Stochastic calculus are:
- Ito integral
- Ito process
- Ito formula for stochastic differential of an Ito process
- Radon-Nikodym derivative and change of measure
- Girsanov theorem (with Novikov condition)
- Stochastic differential equations

Hope this helps.

Yeah would agree with this, but not many undergraduate courses in stochastic processes focus on this at all. My course during my undergrad went through all the basic stats/prob followed by Poisson process, queing theory, ending with markov.

 

[Squasher]

books for stochastic calculus for finance?
no prior knowledge

 

[Qui-Gon]

books for stochastic calculus for finance?
no prior knowledge

Shreve 1 & 2

 

[Squasher]

Shreve 1 & 2

thank you
which topics of finance should i equip myself with for a quant internship. no prior experience.

 

[Squasher]

Shreve 1 & 2

will the statistical knowldege offered by this book suffice? or should i study probability and stats seperately as well

 

[Qui-Gon]

thank you
which topics of finance should i equip myself with for a quant internship. no prior experience.

I would spend some time browsing here on QuantNet, there are many many threads with plenty of information to get you started. You should not expect people to spoon feed you with this. Part of the process of learning about a career is doing the dirty work on your own, and in this way you can refine your questions to be more specific. I think you need to take the time to understand various quant positions (again, QuantNet has so much information to get you started you just have to be willing to look) before you start thinking about preparing for interviews.

will the statistical knowldege offered by this book suffice? or should i study probability and stats seperately as well

These two books are not about statistics, they are about stochastic calculus. Yes, you should study statistics separately. I like both of Ruppert's books (Amazon.com: Statistics and Finance: An Introduction (Springer Texts in Statistics) (9780387202709): Ruppert, David: Books and Amazon.com: Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics) (9781461427490): Ruppert, David: Books) for a more applied/data driven approach, and Casella & Berger (Amazon.com: Statistical Inference (9780534243128): Casella, George, Berger, Roger L.: Books) and Wasserman (All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics): Wasserman, Larry: 0884556812948: Amazon.com: Books) for theory.

 

[Squasher]

thank you

 

[Michsund]

Start with discrete time and work your way to continuous time. I would suggest Shreve 1 and 2 as well. Would also look at a YouTube for help as there’s good videos there as well

 

[Yevgeniy]

Start with discrete time and work your way to continuous time. I would suggest Shreve 1 and 2 as well. Would also look at a YouTube for help as there’s good videos there as well

Michsund, what courses / textbooks to you imply by discrete time and continuous time?
Thank you,
Yev

 

[Michsund]

Michsund, what courses / textbooks to you imply by discrete time and continuous time?
Thank you,
Yev

Shreve 1 is discrete I believe and 2 is continuous

 

[lolalin]

These two books are not about statistics, they are about stochastic calculus. Yes, you should study statistics separately. I like both of Ruppert's books (Amazon.com: Statistics and Finance: An Introduction (Springer Texts in Statistics) (9780387202709): Ruppert, David: Books and Amazon.com: Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics) (9781461427490): Ruppert, David: Books) for a more applied/data driven approach, and Casella & Berger (Amazon.com: Statistical Inference (9780534243128): Casella, George, Berger, Roger L.: Books) and Wasserman (All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics): Wasserman, Larry: 0884556812948: Amazon.com: Books) for theory.

Hi Qui-Gon, do you have any book recommendation for Natural Language Processing?

The key topics in Stochastic calculus are:
- Ito integral
- Ito process
- Ito formula for stochastic differential of an Ito process
- Radon-Nikodym derivative and change of measure
- Girsanov theorem (with Novikov condition)
- Stochastic differential equations

Hope this helps.

Hi siladm, how important would you say Stopping Time/Optimal Stopping time is?

 

[Qui-Gon]

Hi Qui-Gon, do you have any book recommendation for Natural Language Processing?

I only know of this one Speech and Language Processing but have not gone through it so I don’t have any basis for recommending it — I have no experience with NLP.