In Ernst & Young, when I was doing loan reviews for a large commercial bank, I always wondered how they made right decisions of giving credit. They chose qualified borrowers to lend money to, so that debts were more likely to be repaid on time to reduce the credit risk as far as possible while making profits. But how? Driven by curiosity, I consulted risk analysts of the bank and was told that they measured credit risk based on VaR (Value at Risk). They demonstrated VaR value by constructing CreditMetrics - a quantitative model introduced by J.P. Morgan in 1997. Mathematical methods run through the modeling process. A transition matrix, which reflects the probability for obligors moving from one credit rating to another within a time horizon, acts as the main input data of the model. Based on the model output, they gave clients different credit ratings and those who with better scores can get higher credit limits and lower interest rates. The new rating depends only on the previous rating at the last time point and not on the more distant history, which means they modeled rating transition as a discrete-time Markov chain. Data processing method towards VaR is Monte Carlo Simulations, which, unlike traditional methods, depends on stochastic processes and probability distributions instead of historical observations. Hearing those familiar words which I heard time to time in university lectures, I realized that mathematical knowledge plays important roles in financial work. The combination of finance and mathematics is the trend of the market. This is why I am applying for MSc in Financial Mathematics at [University Name].

Challenges make up my life as a mathematics student. Day after day digging into problems and finding a way out, kindling my logical reasoning and problem-solving ability. The most impressive part for me is R, the coding language. Initially, it was challenging to build models and do linear regression smoothly since the R-studio suspended immediately for even a wrong punctuation. To be proficient in it, I re-ran all examples shown in class and watched over 15 hours-long teaching videos. In addition to self-study, I took every opportunity to consult the professor during office hours and do teamwork with classmates. I found modeling can quickly deal with large and messy data sets to identify useful information and seek patterns. Eventually, I finished a research analyzing survival factors of 619 Lymphoma patients with 5 Cox regression models completely based on R, which got a grade A and received praise from two professors. I say yes to challenges, always do and always will.

My academic goal is to combine my technical skills with real-world finance problems. Nowadays, processing mathematical modeling in programming, such as R and C++, is the key to evaluate VaR during credit risk [University Name]agement, and prevalent in the analysis of bond and portfolio. In undergraduate level, I trained my data processing skills mainly based on pure statistical data; now, I hope to apply modeling and programming to market data. The course The Foundations of Interest Rate and Credit Risk Theory at [University Name], which focuses on various kinds of modeling related to financial data, will give me a comprehensive understanding on the relationship between mathematics and finance.

The course Stochastic Processes in this programme attracts me the most. Stochastic Optimal Control based on stochastic calculus of Itô has become especially prevalent in modern finance. In undergraduate econometrics lessons, we discussed how to find the optimal division of wealth between two kinds of assets by forming stochastic differentiation equation obeying Brownian motion. Through after-school research, I found stochastic optimal control was widely used in entire financial area, for instance, the pricing of financial derivatives. Therefore, in postgraduate level, I hope to continue studying stochastic modeling and its application in real-world problems. This course at [University Name], which gives a comprehensive introduction with several small topics and emphasizes on applications in finance, perfectly aligns with my academic interest.

I always knew that I wanted to work with numbers. Multi-background in mathematics and economics gives me strong quantitative skills and commercial awareness. To identify the precise direction I headed to, I took an internship in the investment banking department of Huatai United Securities. One day, our team was given the task of formulating a plan for the privatization of a large public company. My leader believed in the strength of my quantitative academic background, therefore asked me to take charge of valuation measuring and joined in the data capture calculations in the proposal for the method of privatization. To find valuable patterns from historical data of other companies, I used R to do data analysis. Combining with data regarding the positions of the company's major shareholders, I calculated the parameters required for the valuation, and provided an accurate and valuable basis for the subsequent valuation. Ultimately, our team completed the presentation for the full 52-page proposal and presented the final package to the client company's chair[University Name] and [University Name]agement team, who praised our dedication and skills. This intense and exciting experience helped me determine my future career goal.

Upon graduation, I aspire to be an analyst in the investment banking department of a securities company, to help business conduct Initial Public Offerings, Mergers and Acquisitions, and financing. Cooperating with lawyers and auditors in daily work constantly broaden my horizons, extending the boundaries of my thinking. To complete an IPO and an M&A in first three years will be the best starting point of my career to accumulate knowledge on business market rapidly. My ambition is to make significant contribution to domestic financial market, since, Chinese market, comparing to other sophisticated markets, is much different as it’s comprised of [University Name]y individual investors. My goal is to provide valuable investment advice to those individual investors.

Studying this programme at [University Name] will help me achieve this goal. Due to Covid-19, higher unemployment rate rises global imbalances. China-United States trade war also shakes the world’s stock market. The elective course International Finance focused on those key issues. It helps me understand this fast-changing business world, so that I can make more accurate assessments when giving investors suggestions. The second elective course I hope to take is Quantitative Methods for Finance and Risk Analysis, which introduces popular quantitative techniques in finance including VaR, and statistical methods toward them. Building on my undergraduate statistics knowledge, this course can enhance my numerical skills in real-world application. Also, I am interested in the training of R through this course, which helps me deal with financial data easier in the future work. The last elective course I plan to take is Quantifying Risk and Modelling Alternative Markets. Similarly, credit risk models and risk measures are introduced, teaching me how to control investment risk in real assets. Through the postgraduate study, I will get vitally important quantitative abilities required for work and sufficient knowledge reserve of finance related to the world capital market, and this will be the most important step of achieving my academic and career goal.

I am ready.