Quantitative finance postgraduate personal statement example

Personal statement example

My interest in quantitative finance began in a second-year probability module, when we derived the distribution of a random walk and my lecturer mentioned almost in passing that a similar argument underpins the pricing of financial options. I had assumed that finance was mainly commercial judgement. Finding that it rested on measure theory and differential equations changed what I wanted to do with my degree.

I graduated last summer with a 2:1 in Mathematics. My strongest results were in probability, stochastic processes, numerical analysis and statistical inference, and I took an optional module in financial mathematics that introduced the Black-Scholes framework, delta hedging and risk-neutral valuation. For my final-year project I implemented Monte Carlo pricing in Python for European and Asian options, comparing plain simulation with antithetic variates and a control variate based on the geometric average. The work taught me more about care than about cleverness: my first results looked convincing until I realised I was reusing the same random seed across comparisons, which flattered the variance reduction. Rebuilding the experiment properly, documenting the convergence behaviour and being honest about the remaining discretisation bias in the path-dependent case was the most useful part of the project. I also read parts of Hull's Options, Futures and Other Derivatives alongside the module notes, which helped me connect the mathematics to the instruments actually traded.

Since graduating I have worked full-time as a customer service adviser at a building society. The mathematics involved is elementary, but the role has taught me things a degree did not. I explain fixed-rate mortgage terms, early repayment charges and the effect of rate changes to customers who are often anxious, and I have to be accurate and clear under time pressure. I have also become interested in how the society manages its own interest rate exposure, and I asked our branch manager enough questions about hedging and funding that she lent me an internal training booklet on balance sheet risk. It gave me a concrete sense of why term structure modelling matters outside a textbook.

Outside work I tutor GCSE maths on Saturday mornings at a community centre near my home, mostly with students retaking the exam. Explaining probability trees and compound interest repeatedly has sharpened my own understanding, and it has made me sceptical of explanations that sound elegant but cannot be checked with a simple example. At home I share responsibility for my two younger brothers while my mother works night shifts, which means most of my study happens in fixed evening blocks. Over the past year I have used those blocks to work steadily through a linear algebra refresher, to improve my Python by rewriting my dissertation code with proper testing, and to begin learning basic C++ since I understand it is still widely used for pricing libraries.

On the MSc I particularly want to study stochastic calculus rigorously, along with time series econometrics and computational methods for derivatives pricing and risk. I am interested in how models behave when their assumptions fail, and I would like a dissertation topic involving calibration or model risk rather than a purely theoretical derivation. In the longer term I hope to work in risk or model validation at a bank or asset manager, where mathematical work is combined with the obligation to explain and defend it to people who are not mathematicians.

I am applying now because a year of full-time work has confirmed rather than diluted my interest, and because I am ready to return to demanding mathematics with a clearer sense of what I want it for.

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