Güntay, Levent

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Fraud Detection In the Bitcoin Exchange Market
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Namlı, Hüseyin; Güntay, Levent
The trading volume and financial assets of Bitcoin are growing up, while the popularity of Bitcoin world increasing continuously in recent years. In parallel, the market becomes an attraction center for malicious people.
Risk Parameter Calculation Using Princpal Component Analysis of Yield Curves: the Case of Borsa İstanbul Fixed Income Market
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Konuk, Hayrettin; Güntay, Levent
To enable a trustworthy clearing operation, clearinghouses require conservative margins to avoid the risk of incurring a loss in case one counterparty defaults. When margin requirements for fixed income instruments are calculated, yield curves of each instrument are stressed using their first three principal components. All instruments in an account are then evaluated against each stressed yield curve and the margin requirement is calculated as the difference of the combined value of these instruments calculated with the worst of the stressed yield curves between their combined values calculated with related unstressed curves. The aim of this project is to construct a tool for applying principle component analysis (PCA) on daily zero coupon yield curve of Turkish Treasury Securities. The analysis employs a yield curve panel data set obtained consisting historical zero coupon yield curves. The data set includes interest rates of 60 different maturities varying between overnight and 15 years and 1250 daily observations between December 2010 and December 2015. The result of this analysis provides a method that could be run at the end of each clearing day to determine the major components of the yield curve such as level/height, slope and curvature that describes at least 95% of the variation in interest changes and subject to stress shocks
Citation - WoS: 21
Citation - Scopus: 21
Testing for Systemic Risk Using Stock Returns
(2016) Kupiec, Paul; Güntay, Levent
The literature proposes several stock return-based measures of systemic risk but does not include a classical hypothesis tests for detecting systemic risk. Using a joint null hypothesis of Gaussian returns and the absence of systemic risk, we develop a hypothesis test statistic to detect systemic risk in stock returns data. We apply our tests on conditional value-at-risk (CoVaR) and marginal expected shortfall (MES) estimates of the 50 largest US financial institutions using daily stock return data between 2006 and 2007. The CoVaR test identifies only one institution as systemically important while the MES test identifies 27 firms including some of the financial institutions that experienced distress in the past financial crisis. We perform a simulation analysis to assess the reliability of our proposed test statistics and find that our hypothesis tests have weak power, especially tests using CoVaR. We trace the power issue to the inherent variability of the nonparametric CoVaR and MES estimators that have been proposed in the literature. These estimators have large standard errors that increase as the tail dependence in stock returns strengthens.

Güntay, Levent

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