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VOLATILITY FORECASTING

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VOLATILITY FORECASTING Steven Poher Ramzi Rached Ricardo Uribe Dongting Zheng Global Investment Management AGENDA Objective Background Information Forecasting Models ... – PowerPoint PPT presentation

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Title: VOLATILITY FORECASTING


1
VOLATILITY FORECASTING
  • Steven Poher
  • Ramzi Rached
  • Ricardo Uribe
  • Dongting Zheng

Global Investment Management
2
AGENDA
  • Objective
  • Background Information
  • Forecasting Models
  • Data set
  • Methodology
  • Results
  • Conclusion

3
OBJECTIVE
  • Objective
  • To establish a variance forecasting model
  • Why?
  • Important for risk managers (VaR)
  • Used to price options
  • Volatility Return investment decision

4
BACKGROUND INFORMATION
  • Realized / observed volatility is measured by
    squared returns
  • Volatility displays a positive correlation with
    its own past
  • Simple Model
  • PB Equal weights on the past m observations

5
FORECASTING MODELS
  • More flexible model ? Simple GARCH or GARCH (1,1)
  • Extended to Local and Global Instruments
  • Models to be tested for this project
  • GARCH (1,1)
  • GARCH (1,1) Local
  • GARCH (1,1) Global
  • GARCH (1,1) Local Global

6
DATA SET
  • Source DataStream
  • Period 3/27/1998 - 3/28/2008 (10 years)
  • Granularity 1 day

Country Index
NIKKEI 225
CAC 40
DAX 30
FTSE 100
SP 500
7
DATA SET
  • Local Instruments
  • Change in Exchange Rates
  • EUR / USD / JPY / GBP
  • Change in short-term interest rates
  • T-Bill (US) / BTAN (FR)
  • Global Instruments
  • Change in Short-term Eurodollar rate
  • Change in the Term Structure spread

8
METHODOLOGY
  • Using EXCEL, test our 4 models for each of our 5
    markets
  • Use Maximum Likelihood Estimation (MLE) to
    estimate ? / ? / ? / ? EXCEL Solver
  • Test the models using a regression of Squared
    Returns vs. Forecasted Variance
  • Discuss the statistical significance of the
    regression / Select the best model for a given
    country

9
METHODOLOGY - EXAMPLE
10
RESULTS
  • Best models for each country

Country Model R2
GARCH Change in Term Structure Spread (G) 1.21
GARCH Change in / (L) 14.12
GARCH Change in / (L) Change in Term Structure Spread (G) 15.56
GARCH (1,1) Change in / (L) Change in ST Eurodollar (G) 14.23
GARCH 10.58
11
RESULTS
  • Best model for German Market
  • R2 of 15.56
  • Final equation
  • Simple GARCH
  • Change in / Exchange (L)
  • Change in Term Structure Spread (G)

12
RESULTS
13
CONCLUSION
  • No universal model
  • Different countries different models
  • Good proxy for DE / Bad for JP
  • GARCH could also be extended
  • Leverage effects
  • Day-of-week effects
  • Jumps
  • Economic intuition reality check

14
QUESTIONS?
  • THANK YOU
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