Mastering Market Turbulence: Volatility Modeling with GARCH and Extensions Training Course

Introduction

In today's dynamic financial markets, understanding and accurately forecasting volatility is paramount for effective risk management, derivatives pricing, portfolio optimization, and strategic decision-making. Volatility, often characterized by periods of calm followed by bursts of turbulence, is not constant over time, making traditional econometric models insufficient. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and its extensions have emerged as indispensable tools for capturing these time-varying characteristics, enabling more precise risk assessments and more informed investment strategies.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of GARCH models and their advanced extensions. From mastering the fundamental concepts of conditional heteroskedasticity and the structure of the basic GARCH model, to exploring asymmetric effects, long memory, and multivariate specifications, you will gain the expertise to model and forecast financial volatility with confidence. This empowers you to apply cutting-edge econometric techniques to real-world financial data, enhancing your analytical capabilities in areas such as financial risk management, quantitative trading, and macroeconomic forecasting.

Target Audience

  • Financial analysts and quantitative researchers.
  • Risk managers and portfolio managers.
  • Traders and investment strategists.
  • Econometricians and statisticians working with financial time series data.
  • Academics and graduate students (Master's and PhD) in finance, economics, or statistics.
  • Professionals involved in derivatives pricing and hedging.
  • Data scientists interested in advanced time series modeling in finance.
  • Central bank analysts and financial stability experts.

Duration: 10 days

Course Objectives

Upon completion of this training course, participants will be able to:

  • Understand the concept of volatility clustering and conditional heteroskedasticity in financial time series.
  • Grasp the theoretical foundations and structure of the ARCH and GARCH models.
  • Analyze various extensions of the GARCH model, including those capturing asymmetry and long memory.
  • Comprehend the process of estimating GARCH models using maximum likelihood and quasi-maximum likelihood.
  • Evaluate the performance of volatility forecasts using appropriate metrics and diagnostic tests.
  • Develop practical skills in applying GARCH models and their extensions to real-world financial data using statistical software.
  • Navigate the challenges of model selection, specification, and interpretation in volatility modeling.
  • Formulate robust volatility forecasts for applications in risk management, portfolio optimization, and derivatives pricing.

Course Content

  1. Introduction to Financial Time Series and Volatility
  • Stylized facts of financial returns: fat tails, volatility clustering, leverage effect
  • Concept of volatility as a measure of risk
  • Limitations of traditional constant variance models (e.g., ARIMA)
  • Introduction to Conditional Heteroskedasticity
  • Overview of software for time series and volatility modeling (e.g., R, Python, Stata, EViews)
  1. Autoregressive Conditional Heteroskedasticity (ARCH) Models
  • The basic ARCH(q) model: specification and intuition
  • Conditions for stationarity and positive conditional variance
  • Estimation of ARCH models: Maximum Likelihood Estimation (MLE)
  • Interpreting ARCH parameters and variance persistence
  • ARCH-LM test for conditional heteroskedasticity
  1. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Models
  • The GARCH(p,q) model: generalization of ARCH
  • Advantages of GARCH over ARCH: parsimony and better fit for long-memory processes
  • Stationarity conditions for GARCH models
  • Estimation of GARCH models and parameter interpretation
  • Forecasting volatility with GARCH(1,1)
  1. Extensions for Asymmetric Volatility
  • The "leverage effect": negative shocks having a larger impact on volatility than positive shocks
  • Exponential GARCH (EGARCH) model: modeling log-variance and asymmetry
  • GJR-GARCH (Glosten-Jagannathan-Runkle GARCH) model: threshold effects
  • Asymmetric Power ARCH (APARCH) model: flexible asymmetry and power transformations
  • News Impact Curves: visualizing asymmetric responses
  1. Integrated and Fractional GARCH Models
  • Integrated GARCH (IGARCH): persistence of volatility shocks
  • Components GARCH: separating short-run and long-run volatility components
  • Fractional Integrated GARCH (FIGARCH): modeling long memory in volatility
  • Hyperbolic GARCH (HYGARCH): capturing hyperbolic decay of dependence
  • Implications for long-term volatility forecasting and risk management
  1. GARCH-in-Mean (GARCH-M) and Exogenous Variables
  • Allowing conditional variance to affect the conditional mean of returns
  • Risk-return trade-off in financial markets
  • Incorporating exogenous explanatory variables into the GARCH variance equation
  • Economic and financial interpretations of GARCH-M models
  • Applications in asset pricing and portfolio selection
  1. Model Specification, Estimation, and Diagnostics
  • Lag order selection for ARCH and GARCH models: information criteria (AIC, BIC)
  • Distributional assumptions for innovations: Normal, Student's t, Skewed Student's t
  • Maximum Likelihood Estimation (MLE) vs. Quasi-Maximum Likelihood Estimation (QMLE)
  • Residual diagnostics: Ljung-Box test on squared standardized residuals, Jarque-Bera test for normality
  • Robustness checks and stability of estimates
  1. Volatility Forecasting and Evaluation
  • In-sample vs. out-of-sample volatility forecasts
  • Rolling window forecasting and recursive forecasting
  • Forecast evaluation metrics: RMSE, MAE, QLIKE
  • Comparing competing volatility models: Diebold-Mariano test
  • Applications of volatility forecasts in Value-at-Risk (VaR) and Expected Shortfall (ES)
  1. Introduction to Multivariate GARCH (MGARCH) Models
  • Why multivariate volatility modeling? Covariance and correlation dynamics
  • Challenges in MGARCH modeling: positive definiteness, high dimensionality
  • Unconditional vs. conditional correlation
  • Simple MGARCH models: Diagonal VECH, Constant Conditional Correlation (CCC)
  • Dynamic Conditional Correlation (DCC) GARCH: modeling time-varying correlations
  1. Advanced Topics and Practical Applications
  • Volatility and jumps: incorporating jump processes into GARCH models
  • Realized Volatility and GARCH: combining high-frequency data with GARCH
  • Stochastic Volatility (SV) models vs. GARCH: conceptual differences
  • GARCH applications in option pricing and hedging strategies
  • Implementing GARCH models for real-time risk management and trading decisions.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Mastering Market Turbulence: Volatility Modeling With Garch And Extensions Training Course in Kenya
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