Browsing by Author "Uzun, Sevcan"

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    Commonality in FX liquidity: High-frequency evidence
    (Elsevier, 2020-06) Şensoy, Ahmet; Uzun, Sevcan; Lucey, B. M.
    We test the existence and reveal the main properties of commonality in liquidity for the foreign exchange (FX) markets at the high-frequency level. Accordingly, commonality in FX liquidity exists even at the high-frequency level and it has been gradually increasing over the last few years. Moreover, commonality increases significantly before (after) ECB (Fed) monetary policy announcements. Finally, commonality in FX liquidity has a significant positive impact on the commonality in FX return series, indicating that an increase in the intraday systematic liquidity risk might trigger a negative aggregate liquidity-return spiral in the FX markets.
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    Essays on foreign exchange
    (2022-09) Uzun, Sevcan
    This thesis investigates the foreign exchange market dynamics by using high fre-quency data. There is a vast literature on currency markets. However, we aimed to bring a new light on the foreign exchange market dynamics by investigating high frequency data for a set of mostly traded currencies, that includes both developed and emerging market currencies. In the first chapter, we focus on the commonality in liquidity in the foreign exchange market where we are able to contribute to the literature by using a comprehensive data set (14 currencies) with high frequency analysis. Our findings indicate that commonality in liquidity exist for foreign ex-change markets even beyond crisis periods and also monetary policy meetings of Federal Reserve (FOMC) have significant effect on commonality in liquidity. In the second chapter, we study the foreign exchange market for a large data set (14 currencies) where we analyzed the predictability of jumps in the foreign exchange market. We showed that different machine learning methodologies can be used for jump prediction as well as prediction of the direction of jumps in foreign exchange market where Multi Layer Perceptron (MLP), Support Vector Machine (SVM) and Random Forest methodologies have the highest accuracy rates. In our analysis, we are able to predict the occurrence of jumps as well as the direction of jumps in the foreign exchange market using state of art machine learning methodologies for high frequency data even for the Covid Pandemic period where volatility in the foreign exchange market is very high.