Quantitative Finance and Financial Engineering MSc

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  • Objectives
    This course offers a good balance between mathematics and finance, and is designed so that you will find the mathematics element accessible. It is particularly suitable for students with a degree in Finance or Financial Economics or an equivalent training, as well as practical experience (non-compulsory) in finance, mathematics, or engineering usually via employment. It is also suited to those wishing to gain salary enhancements, or students wishing to pursue further and advanced studies in quantitative finance and financial engineering. The course provides an advanced knowledge and understanding of the main theoretical and applied concepts in quantitative finance and financial engineering, delivered from a genuinely international and multicultural perspective, and with a `current issues' approach to teaching. The course is particularly useful for careers that involve designing new financial instruments and managing trading in them.
  • Entry requirements
    Entry requirements: To gain a place on the MSc Quantitative Finance and Financial Engineering course you must have top ranking academic results. We are looking for a UK bachelor degree with first or upper second class honours (overall average 65%), or the overseas equivalent, with excellent results in finance and quantitative subjects (accounting is not considered to be finance). When assessing your academic record, we take into account your grade average, position in class, references and the standing of the institution where you studied your qualification. We particularly welcome applications from excellent candidates studying at institutions of high ranking and repute. You need to have studied or be studying a degree in finance or economics and have taken or be taking a significant number of modules in quantitative subjects, such as differential equations, econometrics or mathematical statistics in the final year of your degree. You will need excellent results in these subjects.
  • Academic title
    Quantitative Finance and Financial Engineering MSc
  • Course description
    MSc Quantitative Finance and Financial Engineering – Course structure

    All taught course units are 15 credits.

    Semester 1

    • Foundation of Finance Theory
    This course provides a foundation in the most important models in finance: general noarbitrage
    relationships (forward parity, put-call parity, MM theorem, the law of one price),
    stock valuation models (APT, CAPM, TSP) and option pricing models.

    • Stochastic Calculus
    The course content includes: Wiener process; continuous local martingales; the quadratic
    variation process; Ito’s integral with respect to a continuous semi-martingale; the Levy
    characterisation theorem; the martingale representation theorem; optimal prediction of the
    maximum process; Bassel process; the Ornstein-Uhlenbeck process; branching diffusion;
    Brownian bridge; the Shiryaev process; the sequential testing equation; the quickest detection
    equation; the existence and uniqueness of solutions in the case of Lipschitz coefficients.

    • VBA/C++ with Finance Applications*
    This course covers Microsoft's Visual Basic (VBA) language in conjunction with Excel's user
    interface and its formulas and calculation capabilities, to deliver a powerful and flexible trading
    tool. The coverage on C++ is introductory and does not assume prior knowledge of
    programming. You learn how to incorporate C++ modules into Excel through the dynamic
    linked library. All example programmes are based on finance applications.

    • Derivative Securities*
    This course covers the valuation and application of financial derivatives instruments, and the
    use of no-arbitrage arguments and risk neutral valuation for the relative pricing of financial

    * If you can produce a transcript that shows you have studied option pricing and/or VBA/C++
    programming previously, you may apply to the course director/coordinator to swap these
    modules for one or more of the following modules:

    • International Macroeconomics and Global Capital Markets
    This course examines major issues in the macroeconomic relations between countries.
    These include: evidence of globalisation in capital markets from parity conditions; the intertemporal
    approach to current account dynamics; the fundamental determinants of the real
    exchange rate; the sustainability of current account deficits, with special reference to the US
    experience; capital account liberalisation; alternative measures of international capital
    mobility, and the Feldstein-Horioka puzzle; economic growth, theory and policy.

    • Martingales with Applications to Finance
    The course content includes: probability, measures and random variables; integration with
    respect to a probability measure; price processes, self-financing portfolios and value
    processes; arbitrage opportunities and equivalent martingale measures; market
    completeness; options and option pricing; stopping times and the optional sampling theorem.

    • Portfolio Investment
    This course provides an advanced coverage of the main principles of investment analysis and
    portfolio management; it examines the steps involved in constructing an investor’s optimal
    portfolio, how to revise this portfolio to ensure it remains optimal, and how to measure the
    performance of this portfolio.

    Semester 2

    • Financial Econometrics
    This course covers OLS, ML and GMM estimation methods, univariate time series analysis
    and various topical issues such as ARCH, Vector Autoregressive Models, unit roots, error
    correction, co-integration and non-linear time series models.

    • Interest Rate Derivatives
    This course unit provides you with foundations of interest rate models and the conceptual
    framework for valuing interest rate derivatives. The unit covers interest rates and bond
    prices, single period and multiperiod interest rates instruments, interest rate derivatives,
    interest rate models, spot rate models and forward models.

    Two course units from:

    • Computational Finance
    This course covers computational methods, including Monte Carlo and Lattice methods for
    option pricing, finite difference methods for parabolic PDEs with emphasis on Crank Nicolson
    methods for parabolic systems, point and line relaxation and PSOR methods, and quadrature

    • Mathematical Modelling of Finance
    The course content includes: no-arbitrage valuation of options and futures; models for the
    movements of stock prices, Brownian motion and geometric Brownian motion; stochastic and
    deterministic processes; basics of stochastic calculus and Ito's lemma; derivation of the
    Black–Scholes PDE and the assumptions behind it; formulating the mathematical problem,
    determining boundary conditions and deriving the solution of the heat conduction equation
    using the Dirac delta function; extension to assets paying dividends, early exercise and free
    boundary problems.

    • Real Options in Corporate Finance
    This course evaluates strategy and management value in property, power, resources, R&D,
    football, dot.coms, telcos, banking and consulting. The course surveys the real options that
    practitioners have identified in these industries.

    • Credit Risk Modelling
    This course unit provides students with advanced approaches to quantitative credit risk
    modeling and management in the context of the Basel II and Solvency II regulatory

    Research dissertation (60 credits)

    You carry out an original piece of research on a subject relating to the course. Our MSc
    dissertation topics align with the research interests of leading financial institutions from
    the City of London and internationally. Senior members of these organisations propose
    several of the dissertation topics, which, subject to approval, members of academic staff
    supervise. Successful completion can require consultations with officers of the financial
    institution, leading to a final presentation of findings.

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