## Computational Finance

Financial modelling is a skill that helps you build a finance career. This module will equip you with an understanding of the most important methods in computational finance and, in particular, their implementation using Excel. These include optimal portfolio construction, calculation of the efficient frontier, dynamic portfolio management, asset price modelling, optimised volatility and correlation estimation, risk measurement and forecasting, Monte Carlo simulation and scenario generation, and pricing of financial derivatives. You will gain an insight into financial data, processing them by means of instructor-led demonstrations and Excel-based exercises for the participants, interpreting results, and performing model validation.

The course is suitable for students with some prior finance knowledge, basic mathematical skills and some familiarity with probability distributions. Basic understanding of Excel is also recommended. Building on these, you will learn how to implement various financial models and become competent in using Excel to this end. The module is well-suited for prospective entrants to the finance industry in areas such as quantitative analysis, investment analysis, financial and real asset research, portfolio construction and management, credit risk research, but also students with an interest in further studies (at Cass or other top UK universities) in quantitative finance, financial engineering and mathematical finance.

Computational Finance and Financial Modelling is co-delivered by Dr Ioannis Kyriakou, a researcher with interest in stochastic asset modelling and numerical methods in finance, and Dr Panos Pouliasis, who will share with you his expertise in energy/commodities finance.

## Course Content

• Practical portfolio analysis: efficient frontier calculation techniques, optimal risky portfolios and optimization using different risk measures.
• Asset price and volatility modelling: definitions, stylized facts and distributional properties.
• Volatility and correlation: estimation, forecasting implementations and interpretation.
• Density and tail forecasting, management of adverse price movements, dynamic portfolio management. Examples of these techniques in Excel.
• One-dimensional and multi-dimensional models. Analysis of price trajectories: tranquillity and / or existence of jumps.
• Inverse problems: model calibration and implied volatility profiles based on basic financial contracts.
• Numerical implementation in Excel.
• Monte Carlo simulation. Applications in Excel in asset price path generation, investments, pricing of one-asset and multi-asset contracts, expected exposure for defaultable contracts.

On successful completion of this module, you will be expected to be able to:

### Knowledge & Understanding

• Formulate financial and investment objectives mathematically and apply analytical skills to evaluate portfolio construction and solve portfolio management problems
• Investigate different types of market price data and their observed properties
• Select appropriate asset price and volatility models, estimate and validate them using relevant techniques in Excel
• Understand the principles of Monte Carlo methods and their application in price generation, investments, portfolio management and valuation of financial contracts
• Make decisions via relevant practical case studies: e.g., the amount of assets required for construction of well-diversified portfolios, estimation of loss from investing in a portfolio over a given time horizon, study of the impact of uncertainty on the investment decision

### Skills

• Build on theory, formulate and understand different financial models and methods, as opposed to simply treating them as black boxes
• Develop an understanding of the model building process
• Implement and calibrate models using real data
• Provide assessment of empirical results
• Develop important applied software skills in financial modelling for use in a quantitative finance professional environment or further related studies

### Values & Attitudes

• Demonstrate awareness of the assumptions and ideas underlying different financial models
• Demonstrate an appreciation of the strength and limitation of financial models and methods
• Learn from the data and support statements on the basis of empirical evidence
• Have an attitude of careful documentation and communication of analysis results

## Module Leaders: Dr Ioannis Kyriakou and Dr Panos Pouliasis

Ioannis Kyriakou is a Senior Lecturer at Cass. He holds a PhD in Finance. Previously he worked for Lloyd’s Treasury and Investment Management on Lloyd’s Investment Risk Model for measuring the market and credit risks under the Solvency II Directive. His teaching duties relate to Numerical Methods in Finance, Financial Derivatives, and Probability and Statistics. His research agenda encompasses stochastic asset modelling and development of efficient methodologies for valuation of exotic derivatives in freight and commodity markets.

Dr Ioannis Kyriakou’s Online Profile

Panos Pouliasis is a Senior Lecturer in Energy/Commodities and Finance at Cass. He holds a PhD in Finance. He joined Cass originally as a researcher at The Costas Grammenos International Centre for Shipping, Trade and Finance. Currently, he is in the Faculty of Finance where he lectures on Finance, International Business and Financial Markets, Quantitative Methods, Commodity Derivatives and Structured Equity/Energy Derivatives. His research interests relate to commodity and shipping risk management, volatility-correlation modelling and forecasting, and financial econometrics.

Dr Panos Pouliasis’s Online Profile

## Eligibility

The course is open to current undergraduates and recent graduates with some prior finance knowledge, basic mathematical skills and some familiarity with probability distributions. Basic understanding of Excel is also recommended. Building on these, you will learn how to implement various financial models and become competent in using Excel to this end. The module is well-suited for prospective entrants to the finance industry in areas such as quantitative analysis, investment analysis, financial and real asset research, portfolio construction and management, credit risk research, but also students with an interest in further studies (at Cass or other top UK universities) in quantitative finance, financial engineering and mathematical finance.

Students who have not studied a degree programme taught in English before are required to have an overall IELTS score of at least 6.5 (with a minimum of 6.5 in writing).