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Analyzing GARCH Models for S&P 500 Returns: Insights from Spring 2010

This study explores Generalized Autoregressive Conditional Heteroskedastic (GARCH) models applied to the S&P 500 index, focusing on return behaviors in Spring 2010. It examines first differences, log transformations, and log returns of the S&P 500. The analysis includes autocorrelation functions for both the log-transformed data and its residuals, employing ARIMA models (ARIMA(2,1,1)) and examining squared residuals with ARCH and GARCH processes. Findings offer insights into volatility patterns in financial time series.

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Analyzing GARCH Models for S&P 500 Returns: Insights from Spring 2010

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  1. GARCH Generalized Autoregressive Conditional Heteroskedastic Models UNR * STAT 758 * Spring2010

  2. Standard and Poor index (S&P500)

  3. Standard and Poor index (S&P500) : Returns (first difference)

  4. Standard and Poor index (S&P500): Log transform

  5. Standard and Poor index (S&P500): Log returns

  6. Autocorrelation function: S&P500

  7. Autocorrelation function: (log10(S&P500))

  8. Autocorrelation function: [(log10(S&P500))]2

  9. ARIMA(2,1,1) for log10 (S&P500): ACF of squared residuals

  10. ARCH(1) for ARIMA residuals: ACF of squared residuals

  11. ARCH(9) for ARIMA residuals: ACF of squared residuals

  12. GARCH(1,1) for ARIMA residuals: ACF of squared residuals

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