tailieunhanh - Lecture Introductory econometrics for finance – Chapter 9: Modelling volatility and correlation

In this chapter, you will learn how to: Discuss the features of data that motivate the use of GARCH models, explain how conditional volatility models are estimated, test for ‘ARCH-effects’ in time series data, produce forecasts from GARCH models, contrast various models from the GARCH family,. | Chapter 9 Modelling volatility and correlation ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 1 An Excursion into Non-linearity Land • Motivation: the linear structural (and time series) models cannot explain a number of important features common to much financial data – leptokurtosis – volatility clustering or volatility pooling – leverage effects • Our “traditional” structural model could be something like: y = β1 + β2 x2 + . . . + βk xkt + u or more compactly y = X β + u. • We also assumed that ut ∼ N(0, σ 2 ). ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 2 A Sample Financial Asset Returns Time Series Daily S&P 500 Returns for August 2003 – August 2013 ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 3 Non-linear Models: A Definition • Campbell, Lo and MacKinlay (1997) define a non-linear data generating process as one that can be written yt = f (ut , ut−1 , ut−2 , . . .) where ut is an iid error term and f is a non-linear function. • They also give a slightly more specific definition as yt = g (ut−1 , ut−2 , . . .) + ut σ 2 (ut−1 , ut−2 , . . .) where g is a function of past error terms only and σ 2 is a variance term. • Models with nonlinear g (•) are “non-linear in mean”, while those with nonlinear σ(•)2 are “non-linear in variance”. ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 4 Types of non-linear models • The linear paradigm is a useful one. • Many apparently non-linear relationships can be made linear by a suitable transformation. • On the other hand, it is likely that many relationships in finance are intrinsically non-linear. • There are many types of non-linear models, . – ARCH / GARCH – switching models – bilinear models ‘Introductory Econometrics for Finance’ c Chris Brooks .

• Kinh tế học
• Modelling volatility
• GARCH models
• Econometrics for Finance
• Introductory Econometrics for Finance
• Financial data
• Financial modelling
• Indian stock markets
• BSE several statistical tests
• Stock Volatility in Top Industries
• He modelling of stock market volatility
• Measure of risk
• Stock futures
• Volatility modelling
• Icss test
• Structural breaks
• Futures trading
• Energy sector
• Journa science
• International journal of financial research
• Value at Risk modelling
• Warsaw Stock Exchange
• Realized volatility
• Regression model
• Purpose of econometrics
• Correlations and volatilities
• Multivariate general autoregressive conditional heteroscedasticity
• Kolmogorov smirnov test
• Value at risk diagnostics
• Maximum likelihood
• Daily returns
• Univariate time series modelling
• Stochastic processes
• Modelling long run relationship
• Stochastic non stationarity
• Teachers’ financial literacy
• Does it impact on financial behaviour
• Long term planning
• The Greater Jakarta
• Partial Least Square Structural Equation Modelling
• Financial literacy on financial behaviour among teachers
• Islamic banking
• Mathematical modeling
• Product musharaka Islamic financial risk
• Modelling and assessment
• Manage risks related
• Lecture Consumer finance
• Rational economic choice
• Financial decisions
• Modelling human financial behavior
• Optimal consumption
• strategic benchmark
• financial planner
• individual accounts
• modelling uncertainty
• parameter lognormal
• performance measurement
• Business Cycles
• Financial Crises
• Dynamic Economic Development
• Unfinished Research Agenda
• Cycle Modelling
• Business Cycle Theory
• Econometric model
• Classical linear regression model
• Multiple linear regression
• Multivariate models
• Simultaneous equations models
• Switching models
• Switching behaviour
• Panel data
• Fixed effects models
• Limited dependent variable models
• Linear probability model
• Evolution of analytical methods
• Real world inferential insights
• Miller and Modigliani
• Firm’s financial leverage
• Corporate bankruptcy model
• Indian Construction Industry
• Structural Equation Modelling
• Financial and non financial encouragements
• Employees of construction industry
• Help of structure questionnaires
• Modelling
• Virtual Crack Closure
• Technique and Finite
• Predicting the Delamination
• Growth Initiation
• Composite Structures
• Modelling Methodology
• Business Perspective
• Conceptual Data Model
• Role Model
• Financial Instruments
• Practical Risk
• ncreasingly
• economic development
• abandoning traditional
• recruiting large
• tax breaks
• financial incentives
• Introduction to Computing
• Financial Accounting Systems
• Booking Systems
• Management Applications
• Modelling and Simulation
• Reasons for using simulations
• Straight lines
• Statistical foundations
• Regression analysis
• Statistical distributions
• Simulation methods
• Random number generation