REFERENCES


Aiolfi, M. and Timmermann, A., 2004. Structural breaks and the performance of forecast
combinations. Unpublished, Bocconi University.

Andersen, T., and Bollerslev, T., 1998. Answering the skeptics: Yes, standard volatility
models do provide accurate forecasts. International Economic Review,
39, pp. 885-906.

Andersen T.G., Bollerslev, T., 1997. Heterogeneous Information Arrivals and Return
Volatility Dynamics: Uncovering the Long-Run in High Frequency
Returns. The Journal of Finance, 52, pp. 975-1005.

Andersen T.G., Bollerslev, T. and Diebold, F.X., 2006. Roughing It Up: Including Jump
Components in the Measurement, Modeling and Forecasting of Return
Volatility. Review of Economics and Statistics, 89, pp. 701-720.

Andersen T. G., Bollerslev, T., Frederiksen, P. and Nielsen, M.O., 2010. Continuous-
time models, realized volatilities, and testable distributional implications
for daily stock returns. Journal of Applied Econometrics, 25, pp. 233-
261.

Andersen T.G., Bollerslev, T., Diebold, F.X. and Labys, P., 2003. Modeling and
Forecasting Realized Volatility. Econometrica, 71, pp. 529–626.

Andersen T.G., Dobrev, D. and Schaumburg, E., 2011. A Functional Filtering and
Neighborhood Truncation Approach to Integrated Quarticity Estimation.
National Bureau of Economic Research Working Papers.

Andersen T. G., Dobrev, D. and Schaumburg, E., 2012. Jump-robust volatility
estimation using nearest neighbor truncation. Journal of Econometrics,
169, pp. 75-93.

Barndorff-Nielsen, O.E. and Shephard, N., 2002. Estimating Quadratic Variation Using
Realised Volatility. Journal of Applied Econometrics, 17, pp. 457-477.

Barndorff-Nielsen, O.E. and Shephard, N., 2004. Power and Bipower Variation with
Stochastic Volatility and Jumps. Journal of Financial Econometrics, 2,
pp. 1-37.

Blair, J.B., Poon, S.H., and Taylor, S.J., 2001. Forecasting S&P100 Volatility: The
Incremental Information Content of Implied Volatilities and High
Frequency Index Returns. Journal of Econometrics, 105, pp. 5-26.

Cheong, C.W., 2013. The Computational of Stock Market Volatility from the Perspective
of Heterogeneous Market Hypothesis. Eco. Comp. & Econ. Cyber. Stu.
& Res., 47, pp. 247-260.

Cheong, C.W., Isa, Z., Abu Hassan S.M.N., 2007. Modelling Financial Observable-
Volatility using Long Memory Models. Applied Financial Economics
Letters, 3, pp.201-208.

Corsi, F., 2009. A Simple Approximate Long Memory Modelo Realized Volatility. Journal
of Financial Econometrics, 7, pp. 174–196.

Corsi, R., Mittnik, S., Pigorsch, C., Pigorsch, U., 2008. The Volatility of Realized
Volatility. Econometric Reviews, 27, pp. 46-78.

Dacorogna, M., Muller, U., Dav, R., Olsen, R. and Pictet, O.V., 1998. Modelling Short-
Term Volatility with Garch and Harch models. Nonlinear Modelling of
High Frequency Finacial Time Series, pp. 161-176.

Dacorogna M., Muller, U., Olsen, R. and Pictet, O.V., 2001. Defining efficiency in
heterogeneous markets. Quantitative Finance, 1, pp. 198-201.

Dufour J.M. and Kurz-Kim J.R., 2014. Heavy Tails and Stable Paretian Distributions in
Econometrics. Journal of Econometrics, 181, pp. 1-2.

Fama E., (1998). Market Efficiency, Long-term Returns, and Behavioral Finance.
Journal of Financial Economics, 49, pp. 283-306.

Granger, C.W.J. and Ramanathan, R., 1984. Improved methods of combining forecasts.
Journal of Forecasting, 3, pp. 197-204.

Hansen, P.R. and Lunde, A., 2006. Realized Variance and Market Microstructure Noise.
Journal of Business and Economic Statistics, 24, pp. 127-218.

Jorion P., 2006. Value-at-Risk: The new benchmark for controlling market risk. Third
Edition. McGraw-Hill, Chicago.

Lux, T. and Marchesi, M., 1999, Scaling and Criticality in A Stochastic Multi-Agent Model
of Financial Market. Nature, 397, pp. 498-500.

Lo, A., 2005. Reconciling Efficient Markets with Behavioral Finance: The Adaptive
Markets Hypothesis. Journal of Investment Consulting, 7, pp. 21-44.

Malkiel, B.G., 2003. The Efficient Market Hypothesis and Its Critics. The Journal of
Economic Perspectives, 17, pp. 59-82.

Mandelbrot, B., 2008. How Fractals Can Explain What's Wrong with Wall Street.
Scientific American.

Muller, U., Dacorogna, M., Dav, R., Pictet, O., Olsen, R. and Ward, J., 1993. Fractals
and Intrinsic Time - A Challenge To Econometricians. XXXIXth
International AEA Conference on Real Time Econometrics,
Luxembourg.

Muller, U., Dacorogna, M., Dav, R., Olsen, R., Pictet, O. and von Weizsacker, J., 1997.
Volatilities of Different Time Resolutions - Analysing the Dynamics of
Market Components. Journal of Empirical Finance, 4, pp. 213-239.

Patton, A.J., 2011. Volatility Forecast Comparison Using Imperfect Volatility Proxies.
Journal of Econometrics, 160, pp. 246-256.

Peters, E.E., 1994. Fractal market analysis: Applying Chaos Theory to Investment and
Economics. John Wiley & Sons, Inc.

Shiller, R.J., 2006. Tools for Financial Innovation: Neoclassical versus Behavioral
Finance. Financial Review, 41, pp. 1-8.

Timmermann, A., 2006. Chapter 4 Forecast Combinations. Handbook of Economic
Forecasting, 1, pp. 135-196.

Tsay, R.S., 2005. Analysis of Financial Time Series. John Wiley & Sons.