Browsing by Author "Iren, Perihan"
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Article Citation Count: 17Behavior of foreign investors in the Malaysian stock market in times of crisis: A nonlinear approach(Elsevier Science Bv, 2019) Omay, Tolga; Iren, Perihan; EconomicsThis study investigates the response to crisis of foreign investors versus domestic investors in the Malaysian stock market. The econometric-modeling involves a nonlinear approach which allows for investor responses to differ in up and down markets. Specifically, the smooth-transition autoregressive (STAR-STGARCH) family of models and generalized impulse response function (GIRF) analysis are employed. The 1997 Asian Crisis is analyzed using daily data for the period 1995-2003, and the 2008 Global Financial Crisis for a period extended to 2015 with allowance for structural breaks. The results indicate that foreign investors exhibited herding behavior during the Asian Crisis and responded to the shock more quickly than domestic investors, but that foreigners did not act differently from their domestic counterparts during the Global Financial Crisis. These findings suggest that even as foreign capital flows may be desirable for economic growth, they can be unstable and may increase volatility during crises that are locally rooted. (C) 2018 Elsevier Inc. All rights reserved.Article Citation Count: 1Controlling Heterogeneous Structure of Smooth Breaks in Panel Unit Root and Cointegration Testing(Springer, 2023) Omay, Tolga; Iren, Perihan; EconomicsThis study aims to show the consequences of a restrictive homogeneity assumption of frequency in heterogeneous panel unit root and cointegration testing with Flexible Fourier Form. For this purpose, we use a simple panel unit root and residual based cointegration test with Flexible Fourier Form in a heterogeneous frequency setting using a bootstrap algorithm. The power of the test statistics and empirical analysis results indicate that failing to take into account a heterogeneous frequency may lead to misleading inferences, thereby leading to misspecified tests and erroneous conclusions concerning the stochastic behavior of the data in the panel sample.Article Citation Count: 2The effects of energy-intensive meat production on CO2 emissions: evidence from extended environmental Kuznets framework(Springer Heidelberg, 2022) Omay, Tolga; Omay, Tolga; Bor, Özgür; Aktan, Ceyda; EconomicsThis study documents the positive relationship between meat production and CO2 emissions by utilizing the environmental Kuznets framework. Relationships between energy consumption, economic growth, meat production, and the levels of CO2 are tested using 6 different variables (CO2 emissions, GDP, energy consumption, forest area, total meat, and total livestock). Data for the study is related to the G7 countries and covers the period between 1961 and 2016. The analysis of the data is then conducted using a panel threshold model. Moreover, the extended EKC model does not only consider the income as the state variable but also examines the nonlinear structure inherited in other explanatory variables as a state variable. In this way, we have seen the nonlinear effects of other variables' evolution over time on carbon emission. The overall results indicate that the production of meat significantly increases CO2 emissions.Article Citation Count: 8Market development and market efficiency: evidence based on nonlinear panel unit root tests(Routledge Journals, Taylor & Francis Ltd, 2019) Omay, Tolga; Iren, Perihan; Omay, Tolga; EconomicsThis study tests the weak form market efficiency of 32 European stock markets. Utilizing monthly data from June 2006 to June 2017, six different, newly developed nonlinear panel root tests were applied in three different groups of European markets: Frontier, Emerging and Developed. The results show that there is a meaningful relationship between different levels of economic development and the weak form market efficiency. Considering the nonlinear structure of the stock market indices, use of linear models might lead to wrong conclusions regarding market efficiency. Using several nonlinear panel root tests, the results of this study shed more light on the true data generating process of the stock market indices and more appropriately model market efficiency.