https://github.com/majorlift/volatility-modeling-python-datasci
Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."
https://github.com/majorlift/volatility-modeling-python-datasci
arima-forecasting data-science data-vizualization financial-engineering garch-model granger-causality jupyter-notebook numpy pandas pyplot python3 regression-models research-paper risk-modelling scipy-stats seaborn statsmodels time-series-analysis value-at-risk volatility-modeling
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Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."
- Host: GitHub
- URL: https://github.com/majorlift/volatility-modeling-python-datasci
- Owner: MajorLift
- Created: 2021-08-13T19:23:40.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2025-01-15T17:46:08.000Z (9 months ago)
- Last Synced: 2025-04-03T21:23:17.545Z (6 months ago)
- Topics: arima-forecasting, data-science, data-vizualization, financial-engineering, garch-model, granger-causality, jupyter-notebook, numpy, pandas, pyplot, python3, regression-models, research-paper, risk-modelling, scipy-stats, seaborn, statsmodels, time-series-analysis, value-at-risk, volatility-modeling
- Language: HTML
- Homepage:
- Size: 4.08 MB
- Stars: 21
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality
Undergraduate Thesis published by the Seoul National University Department of Economics (2020). (**[Read here](https://github.com/MajorLift/volatility-modeling-python-datasci/blob/master/Thesis.pdf)**)
> **Keywords**: VaR(Value at Risk), ARIMA-GARCH model, Risk management
## Motivation
Comparative analysis of international economies during two periods of elevated volatility: the Great Recession of 2008 and the Coronavirus Recession.
## Dataset
Intraday returns (January 2007 - April 2020)
- S&P500
- SSE Composite Index
- Chinese Yuan to USD exchange rate
> Source: Yahoo Finance
## Libraries
- NumPy
- Pandas
- Statsmodels
- SciPy
- Seaborn
- Matplotlibs
## Methodology
- Volatility Forecasting:
- **Skewed Student’s t ARIMA-GARCH model**
- Augmented Dickey-Fuller Test for Stationarity
- Jarque-Bera Test of Normality
- Box-Ljung Test of Autocorrelation
- Breusch-Pagan Test for Heteroskedasticity
- **Parametric Value-at-Risk (VaR)**
- Risk Spillover: **Granger Causality**
## Conclusion
While a considerable degree of risk spillover is observed between the US and Chinese economies throughout the date range, its predictive power is shown to markedly diminish during the two Recession periods.
## References
- Box, G; Jenkins, G. (1970), “Time Series Analysis: Forecasting and Control”, San Francisco:
Holden-Day.
- Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, April, 31:3, pp. 307–27.
- Granger, C. W. J. (1969), “Investigating Causal Relations by Econometric Models and Cross- Spectral Methods,” Econometrica 37, 424-438.
- Granger, C.W.J. (1980), “Testing for Causality: A Personal View,” Journal of Economic Dynamics and Control 2, 329-352.
- Hamilton, J.D. (1994), “Time Series Analysis”, Taylor & Francis US.
- Hansen, B. (1994), “Autoregressive Conditional Density Estimation,” International Economic Review 35, 705-730.
- Lee, S. and B. Hansen (1994), “Asymptotic Theory for the GARCH(1,1) Quasi-maximum Likelihood Estimator,” Econometric Theory.
- Morgan, J.P. (1996), “Risk Metrics–Technical Document”, 4rd Edition, Morgan Guaranty Trust Company: New York.