Simulation of Financial Markets

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Module Title : Simulation of Financial Markets

  • Type of Module:

PC (Prescribed Core Module)

Χ

PS (Prescribed Stream Module)

ES (Elective Stream Module)

E (Elective Module)

  • Level of Module

POSTGRADUATE

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1st



  • Year of Study

2nd



  • Semester

8



  • Number of credits allocated

  • Name of lecturer / lecturers : NIKOLAOS S. THOMAIDIS

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  • Description :

The purpose of this module is to teach students the basics of simulation models and techniques while demonstrating their application in a series of financial engineering tasks (risk analysis, portfolio management, derivatives pricing, etc). Teaching of algorithmic methods and theoretical concepts is supplemented by computer classes, through which tutees can become acquainted with popular commercially-available software packages for financial modeling.

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  • Prerequisites :
  • Acquaintance with probability theory and statistics.
  • Good grasp of MS Excel.
  • Knowledge of basic computer programming principles.

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  • Module Contents ( Syllabus) :

§ Simulation methods: general principles, applications of Monte-Carlo techniques in financial engineering, advantages/disadvantages compared to analytical approaches.

§ Review of basic probability and statistics: random variables and probability distributions, density measures, the normal distribution, multivariate probability densities, the multivariate Gaussian distribution, statistics, estimators and their properties, law of large numbers, central limit theorem, confidence intervals.

§ Simulation of financial scenarios: univariate and multivariate models.

§ Analysis of financial risks: the J.P.Morgan/RiskMetrics framework, calculating Value-at-Risk (VaR) for bundles of securities, application in stock, bond and foreign-exchange portfolios, historical simulation.

§ Pricing of derivative securities: arbitrage and risk-neutral pricing, valuation of options, pricing of exotic and path–dependent derivatives.

§ Special topics in Monte Carlo simulation: variance-reduction techniques, control variates, antithetic variates, importance sampling.

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  • Recommended Reading :

Α) Principal Reference :

§ Teaching notes (in Greek)

§ Zapranis A (2009). Managing financial risks with Matlab. Klidarithmos (in Greek).

§ Jorion, Ph. (2007). Value at Risk – The New Benchmark for Managing Financial Risk. McGraw Hill.

§ Glasserman, P. (2000). Monte Carlo Methods in Financial Engineering, Springer.

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Β) Additional References :

§ Jaeckel, P. (2002). Monte Carlo Methods in Finance. Cambridge. Wiley.

§ Hull, J. Options, Futures, and Other Derivatives. Prentice Hall.

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  • Teaching Methods :
  • Transparencies, lecture notes, homework exercises and tutorial (problem-solving) sessions.
  • Computer laboratory sessions.

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  • Assessment Methods :
  • Final paper-based examination (weighting in the overall course grade 70%).
  • Take-home lab exercises (weighting in the overall course grade 30%).

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  • Language of Instruction :

Greek

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  • Module Objective (preferably expressed in terms of learning outcomes and competences):

§ Learning simulation principles and techniques.

§ Demonstrating how to design a computer-based simulation system suitable for real-life applications.

§ Presenting modern approaches to the analysis and management of financial risks.

§ Acquaintance with the J.P. Morgan/RiskMetrics framework used by most modern financial institutions and banks.

§ Learning how to use popular software packages (MS Excel, Matlab, etc) for risk analysis and the valuation of derivative assets.

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