# University of York - Mathematical Finance (YOUR THOUGHTS)

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I have got a conditional offer from the University of York, for Mathematical Finance. Now the condition is, to clear their Pre - sessional course with above 60% marks. For that I will have to pay a fee of 1500 GBP.IS IT WORTH IT? WHAT ARE YOU THOUGHTS

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#5

Hey did you end up pursuing the Msc? I would lkke to get your input on both the pre sessional and the program itself in terms of the difficulty and course materials quality.

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Yes - i did get my MSc this January. Frankly its purely theoretical course. I mean dont go for it. There is almost zero programming skill (you an either take credit risk or C , but these are not weekly practical projects) . You know from the info online that how the semester goes. You start first week October, end last week November, get December study break, jan 2nd week exams. Your 2nd semester ends third week march. After this you got no classes. Your MATLAB practical project (which is absolute joke on programming given how little the prof teaches you and how much of it even is relevant given the amount of money you're spending). Your deadline for MATLAB practical project will be somewhere 10th - 15th April. Then you have the group project meetings beginning from May onwards (first week or April last week). Your exams for 2nd term are in May end (they were back in 2019 for me). The group project (which is 10 credits) has to be done WITH THE EXAM STUDIES. Tho the prof. relaxes the constrain on it. Your dissertation introduction starts from June first week (which is also deadline for group project). The next three months until September will be spent doing your dissertation. The supervisors are helpful.So, the course itself is very theoretical (which wasn't something I wanted). If you are looking for something practical, I would suggest go for MSC Financial Engineering. I mean it. Math Fin is too too theoretical. Its dry and will make no sense initially (until you have studied bsc mathematics in your undergrad).The main and the biggest drawback of the course is that all the eggs are in a single basket. I mean all you exams for all your course credits are written. Its a very risky thing if you are a person like me who is good at writing reports and practical projects but bad at memorizing things only to vomit them in the examination hall. I got lot of friends in Fin Egg in York, and they had consistent projects. But is Fin Engg better than Math Fin , I do not know honestly but the thing is Fin Egg in York focuses mostly on advanced econometrics. Math Fin focuses of application of Borel set theory everywhere and the stochastic calculus. Please make your decision very carefully.

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#7

I am actually thinking about doing the online version. Since I do not have an undergrad degree in Math, I would assume I will need to pass the pre sessional course first before I can be admitted. from what I am reading about the course description on the website, and like you mentioned, it highly focuses on the theoretical side. Although I am hoping they consider including machine learning and econometrics in the curriculum.

Now I am not sure if I want to spend almost 20K to study this course....

Now I am not sure if I want to spend almost 20K to study this course....

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Look - what I recommend is this. There is an online MSC in Financial Engineering from World Quant university - which I am doing right now. (https://wqu.org/programs/mscfe) - and its all FREE. Its courses are good but heavy. It has R and python programming. I would suggest you to do this course. The university (except the premium ones like the ivies and oxbridge) are not Worth spending money. From the 20k tag you're telling, it seems like you're an International student. 20K for an online degree HONESTLY SOUNDS NOT GOOD. Check the above link I gave. Can you tell me what is your undergrad degree? and what exactly are you aiming to become. Also the thing is pre- sessional itself costs 1000 pounds. Its nothing worth the money - trust me. Much better to spend that money doing FMVA or Breaking into Wall Street,

Regarding the course - no the math fin or fin egg at york doesn't have machine learning or practical econometrics whatsoever. They will not teach you to build your own trading system and trade. The reason for this is most people at such universities are career professors. They would want you to rote learn their own written books and vomit (thus giving them more credibility) while they spend their time doing research at the uni (and get grants). Also given the coronavirus thing; and the uncertainty, I would strong recommend against making any brash decision. If you need any more advice, just DM me.

Regarding the course - no the math fin or fin egg at york doesn't have machine learning or practical econometrics whatsoever. They will not teach you to build your own trading system and trade. The reason for this is most people at such universities are career professors. They would want you to rote learn their own written books and vomit (thus giving them more credibility) while they spend their time doing research at the uni (and get grants). Also given the coronavirus thing; and the uncertainty, I would strong recommend against making any brash decision. If you need any more advice, just DM me.

Last edited by archer2151; 1 year ago

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#9

I am currently doing York's online MSc Marhrmatical Finance. I am happy to answer any questions if it helps.My BSc is in Economics, it was really hard for me at the beginning to really understand the probability theory based on measure theory. It seemed extremely theoretical at first, however, when you get to stochastic calculus, you realise why its needed. You REALLY understand whats going on because of the solid probability theory given.Stochastic Calculus is heavily studied and this is something that is interesting for someone that would like to work with derivatives pricing. There are some market makers that require stochastic calculus and SDEs knowledge to get an interview.I am still going through the course, so not 100% sure of how everything goes. There is a Cplusplus module in the second year which I believe is just like the Cplusplus course from quantnet (even the book used is from Daniel Duffy).

Last edited by Newuser_; 1 year ago

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#10

(Original post by

I am currently doing York's online MSc Marhrmatical Finance. I am happy to answer any questions if it helps.My BSc is in Economics, it was really hard for me at the beginning to really understand the probability theory based on measure theory. It seemed extremely theoretical at first, however, when you get to stochastic calculus, you realise why its needed. You REALLY understand whats going on because of the solid probability theory given.Stochastic Calculus is heavily studied and this is something that is interesting for someone that would like to work with derivatives pricing. There are some market makers that require stochastic calculus and SDEs knowledge to get an interview.I am still going through the course, so not 100% sure of how everything goes. There is a Cplusplus module in the second year which I believe is just like the Cplusplus course from quantnet (even the book used is from Daniel Duffy).

**Newuser_**)I am currently doing York's online MSc Marhrmatical Finance. I am happy to answer any questions if it helps.My BSc is in Economics, it was really hard for me at the beginning to really understand the probability theory based on measure theory. It seemed extremely theoretical at first, however, when you get to stochastic calculus, you realise why its needed. You REALLY understand whats going on because of the solid probability theory given.Stochastic Calculus is heavily studied and this is something that is interesting for someone that would like to work with derivatives pricing. There are some market makers that require stochastic calculus and SDEs knowledge to get an interview.I am still going through the course, so not 100% sure of how everything goes. There is a Cplusplus module in the second year which I believe is just like the Cplusplus course from quantnet (even the book used is from Daniel Duffy).

I actually have a mixed feeling about this program. Many companies I know nowadays also seek candidates who have training background in machine learning and ai beside traditional math finance.

Are there any videos or live sessions though? I assume you have the opportunity to skype with your tutor every week to go over materials?

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#11

Can anyone add any detailed information on the pre-sessional course? I am interested to know a detailed list of topics covered. I have looked on their website and there is a syllabus, i am looking for more detail.

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#12

**Newuser_**)

I am currently doing York's online MSc Marhrmatical Finance. I am happy to answer any questions if it helps.My BSc is in Economics, it was really hard for me at the beginning to really understand the probability theory based on measure theory. It seemed extremely theoretical at first, however, when you get to stochastic calculus, you realise why its needed. You REALLY understand whats going on because of the solid probability theory given.Stochastic Calculus is heavily studied and this is something that is interesting for someone that would like to work with derivatives pricing. There are some market makers that require stochastic calculus and SDEs knowledge to get an interview.I am still going through the course, so not 100% sure of how everything goes. There is a Cplusplus module in the second year which I believe is just like the Cplusplus course from quantnet (even the book used is from Daniel Duffy).

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#13

Here is the response I received from the programme director when asking about the syllabus of the pre sessional course:The syllabus for the pre-sessional course is deliberately indicative only. Because of the diverse background of candidates for the MSc in Mathematical Finance ,the emphasis on particular sections of the material needs to be adjusted individually for each candidate. This is possible because the pre-sessional course is based on individual one-to-one tuition via online conferencing software (Skype). Within the broad framework of the indicative syllabus, each student covers whatever is necessary for them to embark on the MSc in Mathematical Finance

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#14

(Original post by

Here is the response I received from the programme director when asking about the syllabus of the pre sessional course:The syllabus for the pre-sessional course is deliberately indicative only. Because of the diverse background of candidates for the MSc in Mathematical Finance ,the emphasis on particular sections of the material needs to be adjusted individually for each candidate. This is possible because the pre-sessional course is based on individual one-to-one tuition via online conferencing software (Skype). Within the broad framework of the indicative syllabus, each student covers whatever is necessary for them to embark on the MSc in Mathematical Finance

**danielryre**)Here is the response I received from the programme director when asking about the syllabus of the pre sessional course:The syllabus for the pre-sessional course is deliberately indicative only. Because of the diverse background of candidates for the MSc in Mathematical Finance ,the emphasis on particular sections of the material needs to be adjusted individually for each candidate. This is possible because the pre-sessional course is based on individual one-to-one tuition via online conferencing software (Skype). Within the broad framework of the indicative syllabus, each student covers whatever is necessary for them to embark on the MSc in Mathematical Finance

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#15

(Original post by

Did they ask you to do the pre sessional course? If so, how helpful was it? And if I may ask did you take any real analysis or proof based math courses during your undergrad?

I actually have a mixed feeling about this program. Many companies I know nowadays also seek candidates who have training background in machine learning and ai beside traditional math finance.

Are there any videos or live sessions though? I assume you have the opportunity to skype with your tutor every week to go over materials?

**danielryre**)Did they ask you to do the pre sessional course? If so, how helpful was it? And if I may ask did you take any real analysis or proof based math courses during your undergrad?

I actually have a mixed feeling about this program. Many companies I know nowadays also seek candidates who have training background in machine learning and ai beside traditional math finance.

Are there any videos or live sessions though? I assume you have the opportunity to skype with your tutor every week to go over materials?

I will try to answer all the questions I have seen in this topic.

I did the pre-sessional course and it was very helpful. Prior to that, since my background was in Economics, I studied calculus (Thomas' Calculus entire book), linear algebra, basic probability, differential equations, etc by myself in an attempt to fill the gap. This was all helpful, however, I did not study anything proof-based and this is exactly where the pre-sessional kicks in. It has some topic on real analysis and probability theory (the syllabus is very spot on). It is worth noting that besides the pre-sessional, I did end up studying a few things outside the syllabus, by choice, as it would help A LOT with the MSc content and I had support from the tutor for that as well.

The pre-sessional was very helpful because it was my first contact with mathematical proofs. The tutor is EXTREMELY helpful and they reply very quickly (this is also true during the MSc. I am impressed by how fast I get answers on my questions). There are live sessions and you can skype with your tutor during the pre-sessional. During the MSc you have forums (which are quickly replied to) and you can also skype your supervisor or email any of the professors (they are all very open, friendly and quick to reply).

The most challenging aspects for me with regards to the MSc were the lack of experience with mathematical proofs and the fact that I was not used to dealing with things in such an abstract setting.

Someone here has asked what would be the things you would ideally know to start the MSc. In my opinion, here they are: the topics in the pre-sessional syllabus, measure theory (with focus on probability theory) and basic notions of finance.

With a non-math background, the measure theory/probability theory bit was the hardest on for me to start understanding. They do have an excellent book for it (its called "probability through problems"), which is what I went through that helped me immensely. It's just a different way of thinking about things and it took me a while to get used to it.

In terms of the content of the MSc, I am very happy with what it includes. It is very rigorous, mathematically speaking, and it prepares you in a way that you will be comfortable enough to understand/apply recent developments in mathematical finance. Basically, it gives you the skill set necessary for you to be able to learn whatever it is in this field that interests you or your employer. That's why some may say its too theoretical and I do not disagree. For instance, you can learn stochastic calculus through CQF also. The difference is the depth of knowledge.

Bear in mind that what firms look for quants (obviously depends on the specific quant role) is the ability to understand what is being currently developed in the field and apply that in the context of what the firm wants to do. In my opinion that requires depth of knowledge.

For those of you who wish there would be some Machine Learning, I do agree this is something that the quant field is looking more at and I also wished there was some of it in the programme. However, I don't think its possible to encompass in one MSc programme the level of detail necessary in stochastic calculus, derivatives modelling, c++ for computational finance and machine learning for you to be very knowledgeableat all of those. So I'd say, pick one to be very good at first, then develop the other one.

The choice of where to start depends more on what interests you. I have always worked with derivatives and am really interested in the topic, so Mathematical Finance comes as the natural choice. I am interested in market making, derivatives pricing, risk modelling, etc. So Stoch calc makes more sense for that than machine learning models.

For those of you who are most interested in building trading algorithms, doing quant research etc, then maybe machine learning is more appealing.

That being said, I have been working in the market for a considerable amount of time now and can guarantee that both skill sets are in demand.

Quant market makers, structurers, derivatives traders, quant risk, quant trading (with derivatives) = tend to use more stoch calc-related skills

Quant research, algorithmic trading, quant trading (usually non-derivatives) = tend to use more machine learning-related skills

For ALL of those, please study R, Python, C++ (is a must if quant developer roles is of interest), Java, etc.

Ideally you will know one prototyping language and one deployment language, for example, R and C++.

Also, please note that although some job postings do specify required skills like solving SDEs or Bayesian stats, etc. A lot of the quant job postings require a maths, physics, stats, etc MSc/PhD but do not specify (stoch calc vs machine learning needed). But 99.95% of them do require some coding.

I hope that helps. I am happy to answer additional questions.

Bear in mind this reflects my opinion and thoughts, all based on my personal experience.

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#16

(Original post by

Hi,

I will try to answer all the questions I have seen in this topic.

I did the pre-sessional course and it was very helpful. Prior to that, since my background was in Economics, I studied calculus (Thomas' Calculus entire book), linear algebra, basic probability, differential equations, etc by myself in an attempt to fill the gap. This was all helpful, however, I did not study anything proof-based and this is exactly where the pre-sessional kicks in. It has some topic on real analysis and probability theory (the syllabus is very spot on). It is worth noting that besides the pre-sessional, I did end up studying a few things outside the syllabus, by choice, as it would help A LOT with the MSc content and I had support from the tutor for that as well.

The pre-sessional was very helpful because it was my first contact with mathematical proofs. The tutor is EXTREMELY helpful and they reply very quickly (this is also true during the MSc. I am impressed by how fast I get answers on my questions). There are live sessions and you can skype with your tutor during the pre-sessional. During the MSc you have forums (which are quickly replied to) and you can also skype your supervisor or email any of the professors (they are all very open, friendly and quick to reply).

The most challenging aspects for me with regards to the MSc were the lack of experience with mathematical proofs and the fact that I was not used to dealing with things in such an abstract setting.

Someone here has asked what would be the things you would ideally know to start the MSc. In my opinion, here they are: the topics in the pre-sessional syllabus, measure theory (with focus on probability theory) and basic notions of finance.

With a non-math background, the measure theory/probability theory bit was the hardest on for me to start understanding. They do have an excellent book for it (its called "probability through problems"), which is what I went through that helped me immensely. It's just a different way of thinking about things and it took me a while to get used to it.

In terms of the content of the MSc, I am very happy with what it includes. It is very rigorous, mathematically speaking, and it prepares you in a way that you will be comfortable enough to understand/apply recent developments in mathematical finance. Basically, it gives you the skill set necessary for you to be able to learn whatever it is in this field that interests you or your employer. That's why some may say its too theoretical and I do not disagree. For instance, you can learn stochastic calculus through CQF also. The difference is the depth of knowledge.

Bear in mind that what firms look for quants (obviously depends on the specific quant role) is the ability to understand what is being currently developed in the field and apply that in the context of what the firm wants to do. In my opinion that requires depth of knowledge.

For those of you who wish there would be some Machine Learning, I do agree this is something that the quant field is looking more at and I also wished there was some of it in the programme. However, I don't think its possible to encompass in one MSc programme the level of detail necessary in stochastic calculus, derivatives modelling, c++ for computational finance and machine learning for you to be very knowledgeableat all of those. So I'd say, pick one to be very good at first, then develop the other one.

The choice of where to start depends more on what interests you. I have always worked with derivatives and am really interested in the topic, so Mathematical Finance comes as the natural choice. I am interested in market making, derivatives pricing, risk modelling, etc. So Stoch calc makes more sense for that than machine learning models.

For those of you who are most interested in building trading algorithms, doing quant research etc, then maybe machine learning is more appealing.

That being said, I have been working in the market for a considerable amount of time now and can guarantee that both skill sets are in demand.

Quant market makers, structurers, derivatives traders, quant risk, quant trading (with derivatives) = tend to use more stoch calc-related skills

Quant research, algorithmic trading, quant trading (usually non-derivatives) = tend to use more machine learning-related skills

For ALL of those, please study R, Python, C++ (is a must if quant developer roles is of interest), Java, etc.

Ideally you will know one prototyping language and one deployment language, for example, R and C++.

Also, please note that although some job postings do specify required skills like solving SDEs or Bayesian stats, etc. A lot of the quant job postings require a maths, physics, stats, etc MSc/PhD but do not specify (stoch calc vs machine learning needed). But 99.95% of them do require some coding.

I hope that helps. I am happy to answer additional questions.

Bear in mind this reflects my opinion and thoughts, all based on my personal experience.

**Newuser_**)Hi,

I will try to answer all the questions I have seen in this topic.

I did the pre-sessional course and it was very helpful. Prior to that, since my background was in Economics, I studied calculus (Thomas' Calculus entire book), linear algebra, basic probability, differential equations, etc by myself in an attempt to fill the gap. This was all helpful, however, I did not study anything proof-based and this is exactly where the pre-sessional kicks in. It has some topic on real analysis and probability theory (the syllabus is very spot on). It is worth noting that besides the pre-sessional, I did end up studying a few things outside the syllabus, by choice, as it would help A LOT with the MSc content and I had support from the tutor for that as well.

The pre-sessional was very helpful because it was my first contact with mathematical proofs. The tutor is EXTREMELY helpful and they reply very quickly (this is also true during the MSc. I am impressed by how fast I get answers on my questions). There are live sessions and you can skype with your tutor during the pre-sessional. During the MSc you have forums (which are quickly replied to) and you can also skype your supervisor or email any of the professors (they are all very open, friendly and quick to reply).

The most challenging aspects for me with regards to the MSc were the lack of experience with mathematical proofs and the fact that I was not used to dealing with things in such an abstract setting.

Someone here has asked what would be the things you would ideally know to start the MSc. In my opinion, here they are: the topics in the pre-sessional syllabus, measure theory (with focus on probability theory) and basic notions of finance.

With a non-math background, the measure theory/probability theory bit was the hardest on for me to start understanding. They do have an excellent book for it (its called "probability through problems"), which is what I went through that helped me immensely. It's just a different way of thinking about things and it took me a while to get used to it.

In terms of the content of the MSc, I am very happy with what it includes. It is very rigorous, mathematically speaking, and it prepares you in a way that you will be comfortable enough to understand/apply recent developments in mathematical finance. Basically, it gives you the skill set necessary for you to be able to learn whatever it is in this field that interests you or your employer. That's why some may say its too theoretical and I do not disagree. For instance, you can learn stochastic calculus through CQF also. The difference is the depth of knowledge.

Bear in mind that what firms look for quants (obviously depends on the specific quant role) is the ability to understand what is being currently developed in the field and apply that in the context of what the firm wants to do. In my opinion that requires depth of knowledge.

For those of you who wish there would be some Machine Learning, I do agree this is something that the quant field is looking more at and I also wished there was some of it in the programme. However, I don't think its possible to encompass in one MSc programme the level of detail necessary in stochastic calculus, derivatives modelling, c++ for computational finance and machine learning for you to be very knowledgeableat all of those. So I'd say, pick one to be very good at first, then develop the other one.

The choice of where to start depends more on what interests you. I have always worked with derivatives and am really interested in the topic, so Mathematical Finance comes as the natural choice. I am interested in market making, derivatives pricing, risk modelling, etc. So Stoch calc makes more sense for that than machine learning models.

For those of you who are most interested in building trading algorithms, doing quant research etc, then maybe machine learning is more appealing.

That being said, I have been working in the market for a considerable amount of time now and can guarantee that both skill sets are in demand.

Quant market makers, structurers, derivatives traders, quant risk, quant trading (with derivatives) = tend to use more stoch calc-related skills

Quant research, algorithmic trading, quant trading (usually non-derivatives) = tend to use more machine learning-related skills

For ALL of those, please study R, Python, C++ (is a must if quant developer roles is of interest), Java, etc.

Ideally you will know one prototyping language and one deployment language, for example, R and C++.

Also, please note that although some job postings do specify required skills like solving SDEs or Bayesian stats, etc. A lot of the quant job postings require a maths, physics, stats, etc MSc/PhD but do not specify (stoch calc vs machine learning needed). But 99.95% of them do require some coding.

I hope that helps. I am happy to answer additional questions.

Bear in mind this reflects my opinion and thoughts, all based on my personal experience.

Do you think that the presessional prepares you specifically for the York Mathematical Finance MSc, or rather as a standalone is it a good grounding for *any* further financial maths study (eg. self study with books such as Stefanicas primer or the excellent texts of York Uni on Cambridge Press, or CQF). Is the presessional a good course to do in and of itself, even if you don't make it on to the MSc and pursue another route forward?

Thanks again. Very helpful.

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#17

**Newuser_**)

Hi,

I will try to answer all the questions I have seen in this topic.

I did the pre-sessional course and it was very helpful. Prior to that, since my background was in Economics, I studied calculus (Thomas' Calculus entire book), linear algebra, basic probability, differential equations, etc by myself in an attempt to fill the gap. This was all helpful, however, I did not study anything proof-based and this is exactly where the pre-sessional kicks in. It has some topic on real analysis and probability theory (the syllabus is very spot on). It is worth noting that besides the pre-sessional, I did end up studying a few things outside the syllabus, by choice, as it would help A LOT with the MSc content and I had support from the tutor for that as well.

The pre-sessional was very helpful because it was my first contact with mathematical proofs. The tutor is EXTREMELY helpful and they reply very quickly (this is also true during the MSc. I am impressed by how fast I get answers on my questions). There are live sessions and you can skype with your tutor during the pre-sessional. During the MSc you have forums (which are quickly replied to) and you can also skype your supervisor or email any of the professors (they are all very open, friendly and quick to reply).

The most challenging aspects for me with regards to the MSc were the lack of experience with mathematical proofs and the fact that I was not used to dealing with things in such an abstract setting.

Someone here has asked what would be the things you would ideally know to start the MSc. In my opinion, here they are: the topics in the pre-sessional syllabus, measure theory (with focus on probability theory) and basic notions of finance.

With a non-math background, the measure theory/probability theory bit was the hardest on for me to start understanding. They do have an excellent book for it (its called "probability through problems"), which is what I went through that helped me immensely. It's just a different way of thinking about things and it took me a while to get used to it.

In terms of the content of the MSc, I am very happy with what it includes. It is very rigorous, mathematically speaking, and it prepares you in a way that you will be comfortable enough to understand/apply recent developments in mathematical finance. Basically, it gives you the skill set necessary for you to be able to learn whatever it is in this field that interests you or your employer. That's why some may say its too theoretical and I do not disagree. For instance, you can learn stochastic calculus through CQF also. The difference is the depth of knowledge.

Bear in mind that what firms look for quants (obviously depends on the specific quant role) is the ability to understand what is being currently developed in the field and apply that in the context of what the firm wants to do. In my opinion that requires depth of knowledge.

For those of you who wish there would be some Machine Learning, I do agree this is something that the quant field is looking more at and I also wished there was some of it in the programme. However, I don't think its possible to encompass in one MSc programme the level of detail necessary in stochastic calculus, derivatives modelling, c++ for computational finance and machine learning for you to be very knowledgeableat all of those. So I'd say, pick one to be very good at first, then develop the other one.

The choice of where to start depends more on what interests you. I have always worked with derivatives and am really interested in the topic, so Mathematical Finance comes as the natural choice. I am interested in market making, derivatives pricing, risk modelling, etc. So Stoch calc makes more sense for that than machine learning models.

For those of you who are most interested in building trading algorithms, doing quant research etc, then maybe machine learning is more appealing.

That being said, I have been working in the market for a considerable amount of time now and can guarantee that both skill sets are in demand.

Quant market makers, structurers, derivatives traders, quant risk, quant trading (with derivatives) = tend to use more stoch calc-related skills

Quant research, algorithmic trading, quant trading (usually non-derivatives) = tend to use more machine learning-related skills

For ALL of those, please study R, Python, C++ (is a must if quant developer roles is of interest), Java, etc.

Ideally you will know one prototyping language and one deployment language, for example, R and C++.

Also, please note that although some job postings do specify required skills like solving SDEs or Bayesian stats, etc. A lot of the quant job postings require a maths, physics, stats, etc MSc/PhD but do not specify (stoch calc vs machine learning needed). But 99.95% of them do require some coding.

I hope that helps. I am happy to answer additional questions.

Bear in mind this reflects my opinion and thoughts, all based on my personal experience.

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#18

(Original post by

Thanks for the detailed reply.

Do you think that the presessional prepares you specifically for the York Mathematical Finance MSc, or rather as a standalone is it a good grounding for *any* further financial maths study (eg. self study with books such as Stefanicas primer or the excellent texts of York Uni on Cambridge Press, or CQF). Is the presessional a good course to do in and of itself, even if you don't make it on to the MSc and pursue another route forward?

Thanks again. Very helpful.

**browniesr**)Thanks for the detailed reply.

Do you think that the presessional prepares you specifically for the York Mathematical Finance MSc, or rather as a standalone is it a good grounding for *any* further financial maths study (eg. self study with books such as Stefanicas primer or the excellent texts of York Uni on Cambridge Press, or CQF). Is the presessional a good course to do in and of itself, even if you don't make it on to the MSc and pursue another route forward?

Thanks again. Very helpful.

As a standalone, it kind of depends on 3 things, in my opinion:

1) how deep you are going to study Math Fin

You can learn stochastic calculus as it is exposed in Wilmott's book. It is not very rigorous mathematically speaking. It is more intuitive, so the pre-sessional would not add much. However, there is a lot going on in the background that you are not aware of and when your interviewer asks you what is the quadratic variation of brownian motion and asks you to prove it, you will not even understand the question properly. This question was asked to an acquaintance of mine in a real interview. So it depends on what your goals are in terms of depth of knowledge. The contents of the pre-sessional are pre-requisites to understanding more complex concepts which are directly applied to math fin.

2) Your current maths knowledge

The contents of the pre-sessional are available in books, so you can learn by yourself if you can. I was not at a level that I could learn by myself. The pre-sessional helps in the sense that you have contact with the tutor, which is extremely helpful and because it is friendly in the sense that it starts from a point that is not extremely complicated, so you can build your knowledge step by step. That being said, it was still challenging to start the MSc as I felt there was still a small jump in complexity (specifically in probability theory/measure theory).

3) No time "wasted"

It focuses only on the stuff that is needed for math fin, so there is no time "wasted" with things that will not be needed. This is also true for the MSc. For instance, measure theory is a huge field. They teach you what is relevant for math fin only, so no time is wasted with stuff you will not need.

Bear in mind that a lot of math fin is a bit niche, so it can be very hard to find useful information online at the level that someone that is learning can understand. When you find something, they are usually questions/answers in forums which are written in such a way that is super hard to understand (at least for me it was). The answers are usually the same thing you read in the book, which you didn't understand, which is what prompted you to search for info online in the first place. So, usually, not very helpful. I am aware of Stefanica's primers but I don't have them, so I'm not sure if it falls under this category of "things that are initially exposed at a very complex way, so it doesn't really build your knowledge step by step".

Not sure if the above was helpful. I hope so.

danielryre

You are provided with:

1) Lecture notes (books, really): I like them a lot. Takes you step by step, from simple to complex, with examples, proofs, references, etc. Focuses only on what is relevant for math fin. This is the main thing I use to study/learn

2) Lecture slides with embedded audios: has the same content as the notes, sometimes with different examples etc. To be honest, I don't really use these as the lecture notes are really enough for me

3) Exercise lists: exercises to help you put to practice what you have learned. They are not easy and good luck finding answers online (you won't, trust me). You need to solve them yourself and this is where you really really learn, in my opinion. Basically, you won't manage to solve the exercises if you didn't really understand what is in the lecture notes, so this is the real way to check if you really learned.

There is a period for solving the exercises, where you can ask questions in the forums, by email, on skype sessions with your supervisor, etc. Then after handing over your solutions, you get it back with commentaries from your supervisor along with the worked solutions (Step by step so you can follow the rationale). After that, you have more time to study the solutions, go to the forums, speak to the professors, etc. And then you have the assessments.

In terms of how many hours per module, it's hard for me to say since I didn't really pay attention to that. I'd say it was very intense in the beginning, which is where I was learning to think mathematically. Afterwards, it became easier, so less time was required. The lecture notes are not huge, but they are dense. By that, I mean that you don't have thousands of pages to read, but, since they really optimised the content (they only include what is relevant), every word in the notes matter.

The assignments/exams are super fair. By my experience so far, if you manage to solve the exercise lists and really understand each step of the solutions, you are good to go. But bear in mind, the exercise lists are really challenging.

One thing that I like about the exercises (and exams) is that they make sure you are always solving relevant problems. By that, I mean that you may be solving a problem in an exercise list and, although completely unaware of it, the setting of the problem actually describes an up-and-out call option, for instance. You don't really know that, but that is the case.

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The presessional addresses topics that are relevant for mathematical finance, regardless of where you will study.

As a standalone, it kind of depends on 3 things, in my opinion:

1) how deep you are going to study Math Fin

You can learn stochastic calculus as it is exposed in Wilmott's book. It is not very rigorous mathematically speaking. It is more intuitive, so the pre-sessional would not add much. However, there is a lot going on in the background that you are not aware of and when your interviewer asks you what is the quadratic variation of brownian motion and asks you to prove it, you will not even understand the question properly. This question was asked to an acquaintance of mine in a real interview. So it depends on what your goals are in terms of depth of knowledge. The contents of the pre-sessional are pre-requisites to understanding more complex concepts which are directly applied to math fin.

2) Your current maths knowledge

The contents of the pre-sessional are available in books, so you can learn by yourself if you can. I was not at a level that I could learn by myself. The pre-sessional helps in the sense that you have contact with the tutor, which is extremely helpful and because it is friendly in the sense that it starts from a point that is not extremely complicated, so you can build your knowledge step by step. That being said, it was still challenging to start the MSc as I felt there was still a small jump in complexity (specifically in probability theory/measure theory).

3) No time "wasted"

It focuses only on the stuff that is needed for math fin, so there is no time "wasted" with things that will not be needed. This is also true for the MSc. For instance, measure theory is a huge field. They teach you what is relevant for math fin only, so no time is wasted with stuff you will not need.

Bear in mind that a lot of math fin is a bit niche, so it can be very hard to find useful information online at the level that someone that is learning can understand. When you find something, they are usually questions/answers in forums which are written in such a way that is super hard to understand (at least for me it was). The answers are usually the same thing you read in the book, which you didn't understand, which is what prompted you to search for info online in the first place. So, usually, not very helpful. I am aware of Stefanica's primers but I don't have them, so I'm not sure if it falls under this category of "things that are initially exposed at a very complex way, so it doesn't really build your knowledge step by step".

Not sure if the above was helpful. I hope so.

danielryre

You are provided with:

1) Lecture notes (books, really): I like them a lot. Takes you step by step, from simple to complex, with examples, proofs, references, etc. Focuses only on what is relevant for math fin. This is the main thing I use to study/learn

2) Lecture slides with embedded audios: has the same content as the notes, sometimes with different examples etc. To be honest, I don't really use these as the lecture notes are really enough for me

3) Exercise lists: exercises to help you put to practice what you have learned. They are not easy and good luck finding answers online (you won't, trust me). You need to solve them yourself and this is where you really really learn, in my opinion. Basically, you won't manage to solve the exercises if you didn't really understand what is in the lecture notes, so this is the real way to check if you really learned.

There is a period for solving the exercises, where you can ask questions in the forums, by email, on skype sessions with your supervisor, etc. Then after handing over your solutions, you get it back with commentaries from your supervisor along with the worked solutions (Step by step so you can follow the rationale). After that, you have more time to study the solutions, go to the forums, speak to the professors, etc. And then you have the assessments.

In terms of how many hours per module, it's hard for me to say since I didn't really pay attention to that. I'd say it was very intense in the beginning, which is where I was learning to think mathematically. Afterwards, it became easier, so less time was required. The lecture notes are not huge, but they are dense. By that, I mean that you don't have thousands of pages to read, but, since they really optimised the content (they only include what is relevant), every word in the notes matter.

The assignments/exams are super fair. By my experience so far, if you manage to solve the exercise lists and really understand each step of the solutions, you are good to go. But bear in mind, the exercise lists are really challenging.

One thing that I like about the exercises (and exams) is that they make sure you are always solving relevant problems. By that, I mean that you may be solving a problem in an exercise list and, although completely unaware of it, the setting of the problem actually describes an up-and-out call option, for instance. You don't really know that, but that is the case.

**Newuser_**)The presessional addresses topics that are relevant for mathematical finance, regardless of where you will study.

As a standalone, it kind of depends on 3 things, in my opinion:

1) how deep you are going to study Math Fin

You can learn stochastic calculus as it is exposed in Wilmott's book. It is not very rigorous mathematically speaking. It is more intuitive, so the pre-sessional would not add much. However, there is a lot going on in the background that you are not aware of and when your interviewer asks you what is the quadratic variation of brownian motion and asks you to prove it, you will not even understand the question properly. This question was asked to an acquaintance of mine in a real interview. So it depends on what your goals are in terms of depth of knowledge. The contents of the pre-sessional are pre-requisites to understanding more complex concepts which are directly applied to math fin.

2) Your current maths knowledge

The contents of the pre-sessional are available in books, so you can learn by yourself if you can. I was not at a level that I could learn by myself. The pre-sessional helps in the sense that you have contact with the tutor, which is extremely helpful and because it is friendly in the sense that it starts from a point that is not extremely complicated, so you can build your knowledge step by step. That being said, it was still challenging to start the MSc as I felt there was still a small jump in complexity (specifically in probability theory/measure theory).

3) No time "wasted"

It focuses only on the stuff that is needed for math fin, so there is no time "wasted" with things that will not be needed. This is also true for the MSc. For instance, measure theory is a huge field. They teach you what is relevant for math fin only, so no time is wasted with stuff you will not need.

Bear in mind that a lot of math fin is a bit niche, so it can be very hard to find useful information online at the level that someone that is learning can understand. When you find something, they are usually questions/answers in forums which are written in such a way that is super hard to understand (at least for me it was). The answers are usually the same thing you read in the book, which you didn't understand, which is what prompted you to search for info online in the first place. So, usually, not very helpful. I am aware of Stefanica's primers but I don't have them, so I'm not sure if it falls under this category of "things that are initially exposed at a very complex way, so it doesn't really build your knowledge step by step".

Not sure if the above was helpful. I hope so.

danielryre

You are provided with:

1) Lecture notes (books, really): I like them a lot. Takes you step by step, from simple to complex, with examples, proofs, references, etc. Focuses only on what is relevant for math fin. This is the main thing I use to study/learn

2) Lecture slides with embedded audios: has the same content as the notes, sometimes with different examples etc. To be honest, I don't really use these as the lecture notes are really enough for me

3) Exercise lists: exercises to help you put to practice what you have learned. They are not easy and good luck finding answers online (you won't, trust me). You need to solve them yourself and this is where you really really learn, in my opinion. Basically, you won't manage to solve the exercises if you didn't really understand what is in the lecture notes, so this is the real way to check if you really learned.

There is a period for solving the exercises, where you can ask questions in the forums, by email, on skype sessions with your supervisor, etc. Then after handing over your solutions, you get it back with commentaries from your supervisor along with the worked solutions (Step by step so you can follow the rationale). After that, you have more time to study the solutions, go to the forums, speak to the professors, etc. And then you have the assessments.

In terms of how many hours per module, it's hard for me to say since I didn't really pay attention to that. I'd say it was very intense in the beginning, which is where I was learning to think mathematically. Afterwards, it became easier, so less time was required. The lecture notes are not huge, but they are dense. By that, I mean that you don't have thousands of pages to read, but, since they really optimised the content (they only include what is relevant), every word in the notes matter.

The assignments/exams are super fair. By my experience so far, if you manage to solve the exercise lists and really understand each step of the solutions, you are good to go. But bear in mind, the exercise lists are really challenging.

One thing that I like about the exercises (and exams) is that they make sure you are always solving relevant problems. By that, I mean that you may be solving a problem in an exercise list and, although completely unaware of it, the setting of the problem actually describes an up-and-out call option, for instance. You don't really know that, but that is the case.

I particularly get your "no time wasted" comment. I work full time in the markets, now for a few months i have time on my hands due to coronavirus- so i want to maximise my study time and minimise my costs (within reason - i am willing to invest some cash to get up the learning curve ASAP). Right now, i am learning typical UK first year undergrad maths in quite some depth (i am guided by a tutor who knows advanced maths but not financial engineering). I would like to provide some feedback to my tutor so she spends most of our time on the stuff which is most useful to FM, which i will study next after refreshing my maths with the tutor. eg. We have been through a lot of Analysis "epsilon delta" type proofs, which i was thinking might not be so useful to FM, and we should focus on things like Newtons Method, Linear Algebra, Probability, etc which I know will be useful. I'm hoping that the York presessional syllabus is going to be an FM centric version of what i am studying - less "time wasted" in the maths refresher necessary for FM study later in the summer. I only discovered the York offering two months into my current maths tutoring.

Last edited by browniesr; 1 year ago

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**Newuser_**)

The presessional addresses topics that are relevant for mathematical finance, regardless of where you will study.

As a standalone, it kind of depends on 3 things, in my opinion:

1) how deep you are going to study Math Fin

You can learn stochastic calculus as it is exposed in Wilmott's book. It is not very rigorous mathematically speaking. It is more intuitive, so the pre-sessional would not add much. However, there is a lot going on in the background that you are not aware of and when your interviewer asks you what is the quadratic variation of brownian motion and asks you to prove it, you will not even understand the question properly. This question was asked to an acquaintance of mine in a real interview. So it depends on what your goals are in terms of depth of knowledge. The contents of the pre-sessional are pre-requisites to understanding more complex concepts which are directly applied to math fin.

2) Your current maths knowledge

The contents of the pre-sessional are available in books, so you can learn by yourself if you can. I was not at a level that I could learn by myself. The pre-sessional helps in the sense that you have contact with the tutor, which is extremely helpful and because it is friendly in the sense that it starts from a point that is not extremely complicated, so you can build your knowledge step by step. That being said, it was still challenging to start the MSc as I felt there was still a small jump in complexity (specifically in probability theory/measure theory).

3) No time "wasted"

It focuses only on the stuff that is needed for math fin, so there is no time "wasted" with things that will not be needed. This is also true for the MSc. For instance, measure theory is a huge field. They teach you what is relevant for math fin only, so no time is wasted with stuff you will not need.

Bear in mind that a lot of math fin is a bit niche, so it can be very hard to find useful information online at the level that someone that is learning can understand. When you find something, they are usually questions/answers in forums which are written in such a way that is super hard to understand (at least for me it was). The answers are usually the same thing you read in the book, which you didn't understand, which is what prompted you to search for info online in the first place. So, usually, not very helpful. I am aware of Stefanica's primers but I don't have them, so I'm not sure if it falls under this category of "things that are initially exposed at a very complex way, so it doesn't really build your knowledge step by step".

Not sure if the above was helpful. I hope so.

danielryre

You are provided with:

1) Lecture notes (books, really): I like them a lot. Takes you step by step, from simple to complex, with examples, proofs, references, etc. Focuses only on what is relevant for math fin. This is the main thing I use to study/learn

2) Lecture slides with embedded audios: has the same content as the notes, sometimes with different examples etc. To be honest, I don't really use these as the lecture notes are really enough for me

3) Exercise lists: exercises to help you put to practice what you have learned. They are not easy and good luck finding answers online (you won't, trust me). You need to solve them yourself and this is where you really really learn, in my opinion. Basically, you won't manage to solve the exercises if you didn't really understand what is in the lecture notes, so this is the real way to check if you really learned.

There is a period for solving the exercises, where you can ask questions in the forums, by email, on skype sessions with your supervisor, etc. Then after handing over your solutions, you get it back with commentaries from your supervisor along with the worked solutions (Step by step so you can follow the rationale). After that, you have more time to study the solutions, go to the forums, speak to the professors, etc. And then you have the assessments.

In terms of how many hours per module, it's hard for me to say since I didn't really pay attention to that. I'd say it was very intense in the beginning, which is where I was learning to think mathematically. Afterwards, it became easier, so less time was required. The lecture notes are not huge, but they are dense. By that, I mean that you don't have thousands of pages to read, but, since they really optimised the content (they only include what is relevant), every word in the notes matter.

The assignments/exams are super fair. By my experience so far, if you manage to solve the exercise lists and really understand each step of the solutions, you are good to go. But bear in mind, the exercise lists are really challenging.

One thing that I like about the exercises (and exams) is that they make sure you are always solving relevant problems. By that, I mean that you may be solving a problem in an exercise list and, although completely unaware of it, the setting of the problem actually describes an up-and-out call option, for instance. You don't really know that, but that is the case.

Thanks

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