Afterthoughts of my MSc in Quantitative Finance

Officially end my exams on 28 Aug 15. Since I started a year back, I have learnt so much about finance, although I still think it’s not enough. In my opinion, I think there are 2 main school of thoughts in finance.  One track is about trying to come out with models and price a certain financial product. The other is about taking the historical data(prices) and see how it can or cannot be fitted into models and then decide whether the model can used for practical usage. Other subjects are either like tools to help us in these 2 subjects or further in depth studies in a slightly different direction for a different purpose.

To give some examples, my course details are as below.

Semester 1:

Asset Pricing (Track 2) – This is an especially hard topic and is probably a PhD level subject by itself. Requires solid grounding in economics to understand most of the topics.

Numerical Methods 1 (Tools) – Basic root finding, random number generation, finite differences, integrals

Derivatives (Track 1) – The crash course on derivatives. Talked about forwards, futures, treasury bonds, eurodollar futures, options valuations, interest rate swaps, binomial trees on options valuation.

Quantitative Analysis of Financial Market (Track 2) – Crash course on econometrics. Index calculations. Calculations and assumptions of linear regression, test for heteroscedasticity, test for serial autocorrelation, assumptions of normal distribution, time series analysis, ARMA, ARIMA models, volatility models.

Semester 2:

Fixed Income ( Track 1) – in depth studies of forwards and IRS. Valuations are done using discount factors. Change in Numeraire, short rate models, standard market models for pricing options, swaps options, caps and floorlets.

Numerical Methods 2 (Tool) – random number generation using Cholesky decomposition, valuation of options using Monte Carlo simulation, variance reduction techniques for both independent and dependent paths. Multinomial lattice tree for pricing options. discrete and fast fourier transform, explicit finite difference for PDE

Risk Analysis (Track 1) – VaR and Expected Shortfall calculations. parametric calculation. Historical simulation. EWMA calculations. VaR for derivatives. Portfolio level Risk Analysis. CVA modeling.

Econometrics ( Track 2) – Linear regression. MLE. volatility models like GARCH and xARCH, Multivariate GARCH, Contagion, Realized volatility for high frequency data. Macro News impact.

Semester 3:

Stochastic Calculus (Track 1) – usage of stochastic calculus. Brownian motion, Ito’s Lemma, derivation of options equations, reflection principles.

Quantitative Trading Strategies (Tools) – Pair trading strategy, Neural Network, timid vs bold play. Kelly’s criterion. Factor models.

Credit Risk Models (Track 1) – structured and reduced form model. collateralisation, CSA agreement. , CVA and FVA and DVA, expected exposure.  wrong way risk.

Bank Risk Management (Others) – different type of financial institutions, different kind of risk faced by financial institutions, different risk measurements for different type of risks. introduction to basel regulations.

Practical Guide to FICC (Others) – binary options, option replication. volatility smile. hedging,  different desks in financial institution when structuring a product.


To be honest, during the course itself, I felt that the assignments and mini tests were pretty heavy and I found myself always studying with almost no leisure time.  However now when I go for interviews, I still find myself lacking in knowledge. One of the main problem I face is that the interviewers really ask a breadth of questions that encompass all the knowledge in the modules. And it is not just breadth, they ask really deep questions too.  Also one of the other problem that I face is that for those positions that require programming, they usually would want C++ or Java skills which I didn’t pick up in this course. Most of my assignments were done in Matlab with the exception of Quant Trading which was done in Python.  So now I have to go and learn on my own for these.


Author: Zac

Think & Do

3 thoughts on “Afterthoughts of my MSc in Quantitative Finance”

  1. Congrats on completing the course. I completed something similar in 2009. Your syllabus sounds less theoretical and more practical than mine and the breadth of coverage seems good, but may be at the expense of depth. Interviewers love to keep asking tougher questions until you get stuck, but you should expect that in a competitive industry like finance. You seem pretty keen to learn so I’m sure you’ll be fine. Matlab and python are probably used more for actual quant research. C++ and java are often more techy, for instance converting the Matlab researchers output into production ready systems. Make sure you figure out which one you want. Good luck on the journey!


  2. Questions: 1) where did you go to school and do you believe that 1) you’re now a better/more profitable trader/analyst and 2) that the degree prepared you for a job as a “quant”?


    1. I went to Singapore Management University and got the MSc Quantitative Finance. Honestly I am not sure if I am a better trader now. If you are talking about being a bank trader where they trade interest swaps and structured products, then yes I have learnt a lot more about those products and how to price them. But if you are talking about being a prop trader and trading is about finding mispriced securities and flash opportunities that presents itself based on statistical properties, then I am afraid this course is rather preliminary and I have still a long way to go.


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