Volatility, Global Proxy Index, V-A-R: Empirical Study on Pakistan And China Stock Exchanges
Keywords:Global proxy index, PSX, SSE, Log-GARCH (1, 1), ARMA-GARCH (1, 1), FHS, V-a-R @ 5%
This study postulates that propose global proxy index is a significant conduit to evaluate the shocks in volatile stock markets i.e. PSX and SSE, alike. The two separate models i.e. Log-GARCH (1, 1) and ARMA-GARCH (1, 1) have been used along with the value at risk (V-a-R) @ 5% criteria for choosing best-fitted model. The study results showed Log-GARCH (1, 1) model proves to the best. This study results are not driven by political-level risks and thus independent study can be conducted to evaluate the detrimental consequences on investment opportunities under volatile environments.
Piroozfar, G. (2009). Forecasting Value at Risk with Historical and Filtered Historical Simulation Methods. UUDM Project Report.
Korner, K.F., Kneafsey, K.P., & Claessens. (1995). Forecasting volatility in commodity markets. Journal of Forecasting, 77-95.
Mark R. Manfredo and Raymond M. Leuthold. (1998). Agricultural Applications of Value-at-Risk Analysis: A Perspective. OFOR, 98-104.
Bank, D. (2013). Annual Report. DZ BANK Group.
E. F. Fama and G. W. Schwert, “Asset returns and inflation,” J. financ. econ., vol. 5, no. 2, pp. 115–146, Nov. 1977.
Jorion, P. (2007). Value at risk : the new benchmark for managing financial risk. Newyark : John Wiley & Sons.
Angelidis, T. D. (2005). Modeling Rist for long and short term trading positions. The journal of Risk Management, 226-238.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimation of the Vaiance of United Kingdom Inflation. Econometrica , 987-1008.
J. S. Butler & Barry Schachter. (1997). Estimating Value-at-Risk with a Precision measure by Combining kernel Estimation with Historical Simulation. 1-24.
Black, F. (1976). Studies of Stock Price Volatility Changes. Journal of Business and economics, 177-181.
Angabini, A. W. (2011). GARCH Models and the Financial-Crsis-A study of the Malaysian Stock Market. The international Journal of Applied Economics and Finance, 226-236.
Christoffersen, P. (2006). Value-at-Risk Models. journal of finance, 1-14.
J. E. Cavanaugh, “Unifying the derivations for the Akaike and corrected Akaike information criteria,” Stat. Probab. Lett., vol. 33, no. 2, pp. 201–208, Apr. 1997.
Badík, P. (2005).use of the method for measuring market risks and calculating capital aadequacy. NARODA BANKA SLOVENSKA, 17-21.
Barone-Adesi, G., & Giannopoulos, K. (2001). Non parametric V-a-R techniques. myths and realities. economnic notes, 167-181.
Beder, T. S. (1994). V-a-R: Seductive but Dangerous. Financial analyst journal, 12-26.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 307-327.
Engle, R. (2001). The use of ARCH/GARCH models in applied econometrics. . The Journal of Economic Perspectives, 157-168.
G.Cera, E. Cera, and Gerdi Lito. (2013). A GARCH Model approach to Calculate the Value at Risk of Albanian LEK Exchange rate. European Scientific journal, 250-260.
Helmut Mausserand Dan Rosen. (1999). Beyound V-a-R: From Measuring To Managing Risk. In Computational intelligence for financial Engineering , 163-178.
Hendricks, D. (1996). Evaluation of Value-at-Risk Models Using Historical Data. Economic Policy Review Federal Reserve Bank of New York, 39-67.
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