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Discuss In Forums || Updated Oct 2009 City University - MSc Quantitative Finance The Summary: The City University MSc in Quantitative Finance program is designed to develop the specialised quantitative skills required to implement theory in different areas of quantitative finance. More specifically, it aims to prepare graduates for career paths in financial institutions that require advanced technical skills in quantitative analysis, financial research, quantitative asset management, derivatives structuring, financial programming, quantitative strategies implementation and risk management.
The MSc in Quantitative Finance provides a rigorous understanding of the theory of Asset Pricing, Risk Analysis and Fixed Income with relevant applications using C++, Visual Basic, Matlab, Mathematica and other industry standard software.
Some possible destinations for graduates are as economists or quantitative specialists in fund management, structured finance, derivatives and risk management.
Course Structure: To satisfy the requirements of the degree programme students must complete:
Two Induction Weeks The MSc Quantitative Finance begins with two compulsory induction weeks, focused on:
Four core modules (30 hours each)Asset PricingThis course introduces students to the basic concepts used for pricing and analysing financial securities, focusing on spot markets. The efficiency of financial markets is discussed together with the question of whether stock prices are predictable. The importance of the risk and its trade off with return will be analysed in depth. The course is academically rigorous in outlining theoretical models but also focuses on the practical applications and empirical finding. Numerical Methods 1: FoundationsThis module is based on the C/C++ language which students will learn during the lab sessions and will cover Root finding and non-linear sets of equations; Solution of linear systems; Interpolation and extrapolation; Integration of functions; Partial differential equation; Generation of random number. DerivativesThe course will develop an in depth understanding of forwards, futures and swaps and their application in risk management situation. The course covers stock index futures, commodity forward and futures, interest rate derivatives, portfolio insurance, credit derivatives and embedded derivatives for corporate applications. Foundations of EconometricsThe course aims at introducing students to the technical issues in statistical analysis of financial data such as estimation of time series models, forecasting of financial and economic data, and the modelling of asset prices volatility. The course will be based on standard econometric packages like PC Give.
Four core modules (30 hours each)Asset PricingThis course introduces students to the basic concepts used for pricing and analysing financial securities, focusing on spot markets. The efficiency of financial markets is discussed together with the question of whether stock prices are predictable. The importance of the risk and its trade off with return will be analysed in depth. The course is academically rigorous in outlining theoretical models but also focuses on the practical applications and empirical finding. Numerical Methods 1: FoundationsThis module is based on the C/C++ language which students will learn during the lab sessions and will cover Root finding and non-linear sets of equations; Solution of linear systems; Interpolation and extrapolation; Integration of functions; Partial differential equation; Generation of random number. DerivativesThe course will develop an in depth understanding of forwards, futures and swaps and their application in risk management situation. The course covers stock index futures, commodity forward and futures, interest rate derivatives, portfolio insurance, credit derivatives and embedded derivatives for corporate applications. Foundations of EconometricsThe course aims at introducing students to the technical issues in statistical analysis of financial data such as estimation of time series models, forecasting of financial and economic data, and the modelling of asset prices volatility. The course will be based on standard econometric packages like PC Give. Four core modules (30 hours each)Fixed Income SecuritiesThis module will acquaint students with the main modelling streams used in fixed income securities. It will also enable students to use models in this area of practical applications and equip students with the fundamental modelling techniques underpinning the subject. Numerical Methods 2: Applications in FinanceThis module builds on Numerical Methods 1 and focuses on applications to finance. Students will learn how to generate stochastic processes; Monte Carlo Simulations; Trees; Pricing American options; Applications in risk management. This module again integrates a programming language and is lab based. Risk AnalysisThe aim of this module is to develop a solid background for evaluating, managing and researching financial risk. To this end students will learn to analyse and quantify risk according to current best practice in the markets, as implemented in the RiskMetrics and CreditMetrics methodologies. The module also looks briefly at operational risk. Econometrics of Financial MarketsThis module will cover recent advances in the field of financial econometrics, with particular emphasis on high frequency finance and data types, linear time series models and forecasting, GMM and maximum likelihood estimation methods in finance. Further, students will gain exposure to the most recent literature related to modelling return distributions and volatility, focussing on seasonal and realised volatility dynamics, volatility processes/conditional volatility models, correlation, dynamic correlations and multivariate risks.
Four core modules (30 hours each)Fixed Income SecuritiesThis module will acquaint students with the main modelling streams used in fixed income securities. It will also enable students to use models in this area of practical applications and equip students with the fundamental modelling techniques underpinning the subject. Numerical Methods 2: Applications in FinanceThis module builds on Numerical Methods 1 and focuses on applications to finance. Students will learn how to generate stochastic processes; Monte Carlo Simulations; Trees; Pricing American options; Applications in risk management. This module again integrates a programming language and is lab based. Risk AnalysisThe aim of this module is to develop a solid background for evaluating, managing and researching financial risk. To this end students will learn to analyse and quantify risk according to current best practice in the markets, as implemented in the RiskMetrics and CreditMetrics methodologies. The module also looks briefly at operational risk. Econometrics of Financial MarketsThis module will cover recent advances in the field of financial econometrics, with particular emphasis on high frequency finance and data types, linear time series models and forecasting, GMM and maximum likelihood estimation methods in finance. Further, students will gain exposure to the most recent literature related to modelling return distributions and volatility, focussing on seasonal and realised volatility dynamics, volatility processes/conditional volatility models, correlation, dynamic correlations and multivariate risks. Five electives (18 hours each) OR One elective and a Business Research Project ElectivesYou may choose from a wide variety of electives. For example:
Research Methods moduleThis compulsory module trains students to undertake independent research either in the context of a single organisation or by using third-party sources. It provides the necessary tools and skills to initiate, research and write up a business project and includes training in research methodology, availability of data sources, project writing, time-management and presentation skills. These skills will be invaluable to students in their future career whether or not they choose to complete a project.
Global Derivatives View: Somewhat different from the City University's MSc Financial Mathematics, this program takes on a more finance oriented perspective rather than a more quantitatively oriented course. We feel that this program would be more suitable for those looking for a research or trading role upon graduation, with course focus on programming topics, econometric analysis and coverage of basic numerical methods / finance theory, although not too suitable for those looking for a die hard quantitative experience.
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