Browsing by Subject "Jumps"

Now showing 1 - 2 of 2

Embargo
Higher-order moment connectedness between stock and commodity markets and portfolio management
(Elsevier Ltd, 2024-01-17) Mensi, Walid; Ko, Hee-Un; Şensoy, Ahmet; Kang, Sang Hoon
This study examines the spillover in high -order moments for major stock markets in Europe, Japan, the UK, and the US (STOXX50, FTSE100, SP500, and NIKKEI225), and two representative commodities (Brent crude oil and gold futures) using 5 -min data from January 1, 2020, to May 31, 2022. The results show that spillovers vary across order moments, which are larger for realized volatility and jumps than for realized skewness and kurtosis. Moreover, gold is a net receiver of spillovers for all order moments, whereas oil switches from a net receiver of spillovers under both realized volatility and jumps to a net transmitter of spillovers in realized skewness and kurtosis spillovers. The US stock market is a net transmitter of spillovers in all realized moments, whereas other stock markets shift from net receivers to net contributors based on the moments. Furthermore, spillovers in high-order moments vary over time, and their trends behave differently over time. The spillovers in high -order moments increase during different phases of the COVID-19 and Ukraine -Russia wars. These findings have significant implications for fund allocations and financial risk management.
Open Access
Jump forecasting in foreign exchange markets a high frequency analysis
(Wiley, 2023-01-30) Uzun, S.; Şensoy, Ahmet; Nguyen, D. K.
Using tick data for 14 emerging and developed market currencies covering the period from January 2018 until April 2021, we first detect jumps by Lee and Mykland methodology then apply various machine learning algorithms to forecast out of sample jump occurrences and their direction. Our results show that the arrival and the direction of intraday jumps in the foreign exchange market can be predicted with these algorithms combined with liquidity metrics and technical indicators, even for the Covid pandemic period where volatility in the foreign exchange market is very high. Among all the methods considered, multilayer perceptron has the highest average accuracy for jump prediction overall, followed by support vector machine and random forest methodologies with slightly less average accuracy results. Results are robust to alternative sampling schemes. Accordingly, central bankers can adjust liquidity injection timing with these jump prediction models in the foreign exchange markets where they can try to minimize jump strength if not completely eliminate its occurrence. For investors, having information regarding jump occurrence timings gives an opportunity to hedge against foreign exchange risks more efficiently.