Yazar "Eroglu, Burak Alparslan" seçeneğine göre listele

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    A mixed-frequency VAR application to studying joint dynamics of foreign investor trading and stock market returns
    (Physica-Verlag Gmbh & Co, 2024) Eroglu, Burak Alparslan; Ikizlerli, Deniz; Uelku, Numan
    We present the first application of the mixed-frequency VAR (MF-VAR) method in the market microstructure literature, studying the interaction between stock market returns and foreign investors' trading. MF-VAR allows us to use daily investor trading data together with higher-frequency return series and uncover novel intraday patterns in the feedback trading behavior and the information content of trading. Using data from Korea, we find that foreign investors chase opening-hour returns, and their trading has the ability to forecast subsequent days' late-hour returns. This pattern suggests that foreign investors selectively respond to the information incorporated during opening hours. Over the years, foreign investors' response to intraday returns has become more prompt, and the predictive ability of their trading has disappeared. A specific test made feasible by the MF-VAR method does not support the global private information hypothesis.
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    Bounded unit root processes with non-stationary volatility
    (Taylor & Francis Inc, 2023) Gogebakan, Kemal Caglar; Eroglu, Burak Alparslan
    This article concerns the unit root testing under nonstandard conditions for a time series process, such as having an innovation process with non-stationary variance and being limited inside an interval. These conditions are investigated separately in the unit root literature and shown to cause problems, such as size distortions. In this article, we consider the presence of both conditions in the unit root tests simultaneously. The simulation results indicate that the previous methods fail to provide satisfactory inference performance under the simultaneous presence of these conditions. To alleviate this issue, we propose a robust unit root testing mechanism and derive this procedure's asymptotic properties.
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    Pairs trading with wavelet transform
    (Routledge Journals, Taylor & Francis Ltd, 2023) Eroglu, Burak Alparslan; Yener, Haluk; Yigit, Taner
    We show that applying the wavelet transform to S & P 500 constituents' prices generates a substantial increase in the returns of the pairs-trading strategy. Pairs trading strategy is based on finding prices that move together, but if there is shared noise in the asset prices, the co-movement, on which one base the trades, might be caused by this common noise. We show that wavelet transform filters away the noise, leading to more profitable trades. The most notable change occurs in the parameter estimation stage, which forms the weights of the assets in the pairs portfolio. Without filtering, the parameters estimated in the training period lose relevance in the trading period. However, when prices are filtered from common noise, the parameters maintain relevance much longer and result in more profitable trades. Particularly, we show that more precise parameter estimation is reflected on a more stationary and conservative spread, meaning more mean reversion in opened pairs trades. We also show that wavelet filtering the prices reduces the downside risk of the trades considerably.
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    Regulated seasonal unit root process
    (Walter De Gruyter Gmbh, 2022) Eroglu, Burak Alparslan; Pehlivan, Ayse Ozgur
    Unfortunately, time series problems do not appear in data singly. We focus on the joint occurrence of nonstationarity, seasonality and bounded data. Seasonal unit root tests and bounded unit root tests already exist in the literature, yet when all these issues are combined their performance needs improvement. That is why we offer a testing procedure for bounded seasonal unit root processes. The combination of these tests is not straightforward as the nonlinearity coming from bounds causes the limiting distribution of the proposed test statistic to be multivariate Brownian motion while the others have univariate distributions. The simulation exercises reveal that the existing tests, which ignores the presence of bounds or seasonality, suffer significant size problems. Our statistic removes the size distortions and also maintain satisfactory power performance.