Financial Engineering

2019/2020

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

Department of Finance

Course type:

Elective course

When:

2 year, 1 module

Course Syllabus

Abstract

The course is based on the students’ knowledge and skills that were obtained from such disciplines as Investment Analysis, Financial Econometrics, Financial Markets and Institutions. Also it is built on the basis of the Monte Carlo methods for simulation and forecasting.

Learning Objectives

• The aim of the course is to master the techniques of financial engineering for the fixed income instruments, stocks and FX markets, volatility modeling, and interest rate, currency and credit default swaps.

Expected Learning Outcomes

• Students know pricing and features of derivatives securities, key principles of financial markets modeling using Monte Carlo methods.
• Students have the abilities to measure and hedge the different sources of financial risks and to construct structural products.
• Students choose adequate mathematical models for the forecasting prices of financial assets.
• Students understand the principles of decision-making in a derivative markets.
• Students makes calculation on the basis of Monte Carlo methods for security pricing, risk measurement and forecasting.
• Students have skills in modeling and forecasting the volatility using GARCH and Markov Switching.
• Students have skills in modeling and forecasting the dynamics of interest-rate structure.
• Students have skills in modeling and forecasting the stock and FX prices using linear and nonlinear models.
• Students understand principles of interest-rate, FX and CD swaps engineering.
• Students are prepared for taking ethical and reasonable, data-driven decisions on hedging on financial markets, timely and in a persuasive manner.

Course Contents

• Topic 1. Institutional aspects of derivative markets
  Futures and options: pricing and risks. Using of derivatives to hedge market risks. Problems of forecasting the prices of financial assets under hedging: volatile volatility, asymmetry, nonlinearity on average. General rules for constructing a hedged asset.
• Topic 2. Monte Carlo methods in financial engineering
  The principles of imitation prediction. Construction of the simulation model. Algorithmization in R. The model of a random walk with constant volatility and its possibilities for hedging and speculation. The effectiveness of martingale and anti-martingale strategies. The use of Monte Carlo methods in predicting the ARIMA model with constant volatility.
• Topic 3. Volatility engineering
  Measuring volatility. Historical and implied volatility. Volatility models: ARCH, GARCH, varieties of GARCH. Clustering of volatility. Volatility spillover. Nonlinear volatility models: threshold model and regime switching model. Properties of volatility in the design of hedge assets.
• Topic 4. Fixed income markets engineering
  Spot and forward interest rates. Fixed income instruments pricing: yield to maturity, duration and convexity. Measurement of interest rate risk. Engineering of fixed income instruments portfolio. Estimation the impact of the default risk growth on VAR of the bond portfolio.
• Topic 5. Stock and FX markets engineering
  Nonlinearity in mean in the dynamics of stock and FX prices. Reasons for the emergence of temporary trends in prices. Threshold models. Regime switching models. Structural breaks. Price’s forecasting using nonlinear models. The influence of secular stagnation on world stock market indices. Modeling risks of the stocks and FX portfolio.
• Topic 6. Interest-rate, FX and CD swaps engineering
  Modeling the time structure of interest rates. Impact of expectations on the yield curve. Models of dynamics of interest rate. Uncertainty in the dynamics of interest rates. The role of monetary policy in the engineering of the bond market. Interest, currency and CD swaps for managing risks.

Assessment Elements

• In-class activitity
• Midterm
• Individual project
• Final exam

Interim Assessment

• Interim assessment (1 module)
  0.5 * Final exam + 0.1 * In-class activitity + 0.2 * Individual project + 0.2 * Midterm

Bibliography

Recommended Core Bibliography

• Brandimarte, P. (2014). Handbook in Monte Carlo Simulation : Applications in Financial Engineering, Risk Management, and Economics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=800911

Recommended Additional Bibliography

• Kosowski, R., & Neftci, S. N. (2015). Principles of Financial Engineering (Vol. Third edition). Amsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=516142