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AC.F324: Quantitative Finance


Department: Accounting and Finance NCF Level: FHEQ/QCF/NQF6//RQF6
Study Level: Part II (final year) Credit Points: 15.0
Start Date: 09-10-2017 End Date: 18-05-2018
Available for Online Enrolment?: Y Enrolment Restriction: Fully available to all students
Module Convenor: Professor I Nolte

Syllabus Rules and Pre-requisites

  • Prior to AC.F324, the student must have successfully completed:
  • The student must take 1 modules from the following group:

Curriculum Design: Outline Syllabus

  • Lecture 1, Introduction.

    Overview of the course. Examples of time series of asset prices and returns. Volatility clustering.
    APDVP reading: Chapters 1 and 2.

    Lectures 2, 3, 4 & 5, Random variables and stochastic processes.

    Probability concepts ? random variables, density functions, expectations, independence, and conditional distributions.Stochastic processes ? autocorrelations, uncorrelated processes, autoregressive, moving-average and integrated components.
    Examples of ARMA models for financial returns.

    APDVP reading: Chapter 3.
    Additional reading:
        - For elementary probability: P. Newbold, W.L. Carlson and B.M. Thorne, Statistics for Business and Economics, Prentice-Hall, 2003 (fifth edition), parts of Chapters 4, 5 and 6.
        - For ARMA models: Sections 2.1 to 2.6 of Tsay, Chapter 2 of Mills and Chapters 3 and 4 of Chatfield are relevant.

    Lectures 6 & 7, Statistical characteristics of returns from financial assets.

    The common properties of time series of financial returns. Their means, standard deviations and distributions. Calendar properties. Correlations between returns on different days. Autocorrelations of absolute returns and squared returns.

    APDVP reading: Chapters 4.
    Additional reading: Return distributions are discussed in Mills, chapter 5.

    Lectures 8, 9 & 10, Expected returns using time series information.

    The random walk hypothesis and its relationship to market efficiency. Testing for a random walk process using the variance-ratio test. Methods that use trading rules to assess the predictability of returns and the efficiency of markets. The moving-average trading rule and conclusions from its use.

    APDVP reading: Chapters 5 and 7.

    Lectures 11, 12 & 13, Modelling changes in volatility using time series information.

    Reasons for changes in volatility. The autoregressive conditional heteroscedasticity (ARCH) framework. Statistical properties, computational methods, hypothesis tests, examples. Forecasting future volatility using previous returns.

    APDVP reading: Chapters 8, 9 and 10.
    Additional reading: Sections 3.1 to 3.6 of Tsay and Chapter 4 of Mills are relevant.

    Lecture 14, High-frequency analysis of market prices.

    Typical data and methods for prices recorded every five minutes. The impact of scheduled news. Measures of realised volatility. Insights from the additional information provided by high-frequency data.

    APDVP reading: Chapter 12.
    Additional reading: Section 5.3 of Tsay.

    Lectures 15, 16 & 17, Volatility expectations implied by options prices.

    Review of option pricing theory. The definition of implied volatility and computational methods. Typical patterns in implied volatility as either the time to expiry or the exercise price varies. Forecasting volatility using option prices and comparisons with time series forecasts.

    APDVP reading: Chapters 14 and 15.
    Additional reading: Hull, J., 2000, Options, Futures and Other Derivatives (fourth edition), chapter 17, Prentice-Hall, or 2003 (fifth edition), chapter 15.

    Lectures 18 & 19, Probability distributions implied by options prices.

    Methods that use several option prices to estimate a probability density for the asset price when the options expire. Mixtures of lognormal distributions. Examples for the UK equity market.

    APDVP reading: Chapter 16.

    Lecture 20

    This lecture session may not be required ? further information will be provided later.

Curriculum Design: Single, Combined or Consortial Schemes to which the Module Contributes

  • Available to all those who have completed AcF 214 (M or L).  This may be studdents on any of the AcF administered degrees (except Accounting and Management studies) and mamny other degree throughout LUMS (and indeed elsewhere).

  • 65% Exam
  • 35% Coursework

Assessment: Details of Assessment

  • This module helps you to understand how econometric models can be used to learn about the future behaviour of the prices of financial assets by using information on the history of asset prices and the prices of derivative securities.

    It describes time series models for financial market prices and shows how these models can be applied by banks and investors. It covers random walk tests and forecasting price volatility for financial asset prices.

Educational Aims: Subject Specific: Knowledge, Understanding and Skills

  • -

    To explain how econometric methods can be used to learn about the future behaviour of the prices of financial assets by using the information in the history of asset prices and in the prices of derivative securities.

     

    - To provide students with practical experience of analysing market prices.

Educational Aims: General: Knowledge, Understanding and Skills

  • The course is designed as an introduction to econometric methods for the analysis and research of financial assets and capital markets relationships. The focus will be on the analysis of financial data and econometric methods for modelling of financial time series, risk management and forecasting. The key objectives are to

     

    • Explain how econometric methods be used to learn about the behaviour of financial assets
    • Endow students with practical experience of analysing financial data useful for research and practical work in the finance industry

Learning Outcomes: Subject Specific: Knowledge, Understanding and Skills

  • After completing the course students should :
    - Understand the important features of time series of market prices,
    - Appreciate the relevance of efficient market theory to predicting prices,
    - Be familiar with the latest methods for forecasting price volatility,
    - Be able to use option prices to make statements about the distributions of future asset prices,
    - Have acquired experience of applying computational methods to market data.

Learning Outcomes: General: Knowledge, Understanding and Skills

  • The course is designed as an introduction to econometric methods for the analysis and research of financial assets and capital markets relationships. The focus will be on the analysis of financial data and econometric methods for modelling of financial time series, risk management and forecasting. The key objectives are to

     Explain how econometric methods be used to learn about the behaviour of financial assets

    • Endow students with practical experience of analysing financial data useful for research and practical work in the finance industry

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