GARCH Processes with Skewed and Leptokurtic Innovations: Revisiting the Johnson SU Case

2012 ◽  
Author(s):  
Jean-Guy Simonato
Keyword(s):  
Garch Processes

DAY-OF-THE-WEEK EFFECT IN US BIOTECHNOLOGY STOCKS — DO POLICY CHANGES AND ECONOMIC CYCLES MATTER?

2016 ◽  
Vol 11 (02) ◽  
pp. 1650008
Author(s):  
SWARN CHATTERJEE ◽  
AMY HUBBLE
Keyword(s):  
Stock Returns ◽  
Small Firm ◽  
Economic Cycles ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Small Investors ◽  
Garch Processes ◽  
Day Of The Week ◽  
Firm Effect ◽  
Daily Returns

This study examines the presence of the day-of-the-week effect on daily returns of biotechnology stocks over a 16-year period from January 2002 to December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI), we find that the stock returns were the lowest on Mondays, and compared to the Mondays the stock returns were significantly higher on Wednesdays, Thursdays, and Fridays. The day-of-the-week effect on returns of biotechnology stocks remained significant even after controlling for the Fama–French and Carhart factors. Moreover, the results from using the asymmetric generalized autoregressive conditional heteroskedastic (GARCH) processes reveal that momentum and small-firm effect were positively associated with the market risk-adjusted returns of the biotechnology stocks during this period. The findings of our study suggest that active portfolio managers need to consider the day of the week, momentum, and small-firm effect when making trading decisions for biotechnology stocks. Implications for portfolio managers, small investors, scholars, and policymakers are included.

Whittle estimation in multivariate CCC-GARCH processes

2018 ◽  
Vol 48 (15) ◽  
pp. 3921-3940
Author(s):  
Abdelouahab Bibi ◽  
Karima Kimouche
Keyword(s):  
Whittle Estimation ◽  
Garch Processes

Outliers in GARCH Processes

2012 ◽  
pp. 337-358 ◽  
Author(s):  
Luiz Hotta ◽  
Ruey Tsay
Keyword(s):  
Garch Processes

scholarly journals Functional ARCH and GARCH Models: A Yule-Walker Approach

2020 ◽  
Author(s):  
Sebastian Kühnert
Keyword(s):  
Financial Time Series ◽  
Sufficient Conditions ◽  
Dimensional Subspace ◽  
Stationary Solutions ◽  
Finite Dimensional Subspace ◽  
Estimation Errors ◽  
Linear Processes ◽  
Finite Dimensional ◽  
Garch Processes ◽  
Asymptotic Upper Bound

Conditional heteroskedastic financial time series are commonly modelled by ARCH and GARCH. ARCH(1) and GARCH processes were recently extended to the function spaces C[0,1] and L2[0,1], their probabilistic features were studied and their parameters were estimated. The projections of the operators on finite-dimensional subspace were estimated, as were the complete operators in GARCH(1,1). An explicit asymptotic upper bound of the estimation errors was stated in ARCH(1). This article provides sufficient conditions for the existence of strictly stationary solutions, weak dependence and finite moments of ARCH and GARCH processes in various Lp[0,1] spaces, C[0,1] and other spaces. In L2[0,1] we deduce explicit asymptotic upper bounds of the estimation errors for the shift term and the complete operators in ARCH and GARCH and for the projections of the operators on a finite-dimensional subspace in ARCH. The operator estimaton is based on Yule-Walker equations. The estimation of the GARCH operators also involves a result concerning the estimation of the operators in invertible, linear processes which is valid beyond the scope of ARCH and GARCH. Through minor modifications, all results in this article regarding functional ARCH and GARCH can be transferred to functional ARMA.

Distributional analysis of empirical volatility in GARCH processes

2008 ◽  
Vol 138 (11) ◽  
pp. 3578-3589 ◽  
Author(s):  
Lajos Horváth ◽  
Piotr Kokoszka ◽  
Ričardas Zitikis
Keyword(s):  
Distributional Analysis ◽  
Garch Processes

scholarly journals Comparison Results for GARCH Processes

2014 ◽  
Vol 51 (3) ◽  
pp. 685-698
Author(s):  
Fabio Bellini ◽  
Franco Pellerey ◽  
Carlo Sgarra ◽  
Salimeh Yasaei Sekeh
Keyword(s):  
Stochastic Orders ◽  
Stochastic Comparison ◽  
Convex Order ◽  
Comparison Results ◽  
Garch Processes ◽  
Garch Process

We consider the problem of stochastic comparison of general GARCH-like processes for different parameters and different distributions of the innovations. We identify several stochastic orders that are propagated from the innovations to the GARCH process itself, and we discuss their interpretations. We focus on the convex order and show that in the case of symmetric innovations it is also propagated to the cumulated sums of the GARCH process. More generally, we discuss multivariate comparison results related to the multivariate convex and supermodular orders. Finally, we discuss ordering with respect to the parameters in the GARCH(1, 1) case.

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