The Dynamic Impact of Gold and Oil Uncertainty on XU100, CDS, and Exchange Rate in Türkiye: A Wavelet Analysis
PDF File

Keywords

Uncertainty
Gold
Oil
Wavelet analysis
GVZ
OVX

How to Cite

The Dynamic Impact of Gold and Oil Uncertainty on XU100, CDS, and Exchange Rate in Türkiye: A Wavelet Analysis. (2025). Information Technology in Economics and Business, 2(2), 38-43. https://doi.org/10.69882/adba.iteb.2025072

Abstract

Geopolitical tensions and macroeconomic fluctuations in global markets have significantly influenced investor behavior and the direction of financial markets. In this turbulent environment, strategic commodities such as gold and oil have emerged as prominent safe-haven assets. The price volatility of these assets is considered a key indicator of market uncertainty and is measured through implied volatility indices, namely the GVZ (Gold Volatility Index) and OVX (Oil Volatility Index). In this respect the relationship between these indices and financial indicators becomes particularly critical during periods of economic distress. This study examines the effects of GVZ and OVX indices on the main financial variables in Türkiye, namely XU100 index, USD/TRY exchange rate and CDS spreads, in the time-frequency domain using wavelet analysis method. The analyses are conducted through wavelet power spectrum, wavelet coherence, and phase difference techniques. The findings reveal that GVZ exerts strong and persistent influences on exchange rates and CDS spreads, particularly over medium to long term horizons, often acting as a leading indicator. OVX also demonstrates a leading role, with more pronounced effects in the short to medium term. In contrast, the XU100 index exhibits a weaker and more fragmented response to these uncertainties, mostly limited to short-term episodes. In conclusion, implied volatility indices represent significant indicators for both investment decisions and macroeconomic policymaking, particularly in economies like Türkiye that are vulnerable to external shocks. This study underscores the necessity of analyzing uncertainty financial market interactions within a time frequency framework and offers meaningful policy implications for uncertainty management.

PDF File

References

Adebayo, T. S., D. Kirikkaleli, and E. B. Agyekum, 2021. Time-frequency relationship between energy consumption and economic growth: Evidence from wavelet coherence analysis. Environmental Science and Pollution Research, 28: 58145–58157.

Alola, A. A. and D. Kirikkaleli, 2019. Renewable energy consumption in EU-28 countries: Policy toward pollution mitigation and economic sustainability. Energy Policy, 132: 352–360.

Aloui, R., S. Hammoudeh, and D. K. Nguyen, 2015. A time–frequency approach for assessing the relationships between oil, gold and stock markets. Applied Economics, 47: 4083–4099.

Alqahtani, A. and J. Chevallier, 2020. Dynamic spillovers between Gulf Cooperation Council’s stocks, VIX, oil and gold volatility indices. Journal of Risk and Financial Management, 13: 69.

Arı, A., B. Kumcuoğlu, and N. Güngör, 2008. Dalgacık dönüşüm ve görüntü sıkıştırma uygulamaları. İstanbul Üniversitesi Mühendislik Fakültesi Dergisi, 1: 1–6.

Bodart, V. and B. Candelon, 2009. Evidence of interdependence and contagion using a frequency domain framework. Emerging Markets Review, 10: 140–150.

Bouri, E., A. Jain, P. C. Biswal, and D. Roubaud, 2017. Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices. Resources Policy, 52: 201–206.

Chen, H., L. Liu, and X. Li, 2018. The predictive content of CBOE crude oil volatility index. Physica A: Statistical Mechanics and its Applications, 492: 837–850.

Crowley, P. M., 2007. A guide to wavelets for economists. Journal of Economic Surveys, 21: 207–267.

Dutta, A., 2017. Oil price uncertainty and clean energy stock returns: New evidence from crude oil volatility index. Journal of Cleaner Production, 164: 1157–1166.

Gençay, R., F. Selçuk, and B. Whitcher, 2001. An introduction to wavelets and other filtering methods in finance and economics. Academic Press.

Gokmenoglu, K. K. and N. Fazlollahi, 2015. The interactions among gold, oil, and stock market: Evidence from S&P500. Procedia Economics and Finance, 25: 478–488.

Goupillaud, P., A. Grossmann, and J. Morlet, 1984. Cycle-octave and related transforms in seismic signal analysis. Geoexploration, 23: 85–102.

Graps, A., 1995. An introduction to wavelets. IEEE Computational Science and Engineering, 2: 50–61.

Jain, A. and P. C. Biswal, 2017. Uncovering frequency domain causality between gold and the stock markets of China and India: Evidence from implied volatility indices. Finance Research Letters, 23: 23–30.

Ji, Q., B.-Y. Liu, and J. Cunado, 2016. Risk spillovers and dynamic correlation between oil prices and stock market sectors. Economic Modelling, 59: 377–390.

Kalmaz, D. B. and D. Kirikkaleli, 2019. Modeling CO2 emissions in an emerging market: Empirical finding from ARDL-based bounds and wavelet coherence approaches. Environmental Science and Pollution Research, 26: 5210–5220.

Li, X. and S.-M. Yoon, 2022. Asymmetric and frequency-dependent connectedness between energy and stock markets. Resources Policy, 76: 102617.

Luo, X. and S. Qin, 2017. Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility index. Finance Research Letters, 20: 29–34.

Maghyereh, A. I. and B. Awartani, 2016. The connectedness between crude oil and financial markets: Evidence from implied volatility indices. Journal of Commodity Markets, 4: 56–69.

Mariani, M. S., M. Boloş, D. Streimikiene, and F. Kialka, 2020. The impact of oil price shocks on green bond markets: A wavelet coherence analysis. Resources Policy, 68: 101744.

Rua, A. and L. C. Nunes, 2009. International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16: 632–639.

Shahbaz, M., A. K. Tiwari, and M. I. Tahir, 2015. Analyzing time–frequency relationship between oil price and exchange rate in Pakistan through wavelets. Journal of Applied Statistics, 42: 690–704.

Wen, F., X. Yang, and D. Song, 2018. Forecasting oil market risk using pre-crisis predictors: A regime-switching approach. Energy Economics, 74: 783–795.

Xiao, J., M. Zhou, F. Wen, and F. Wen, 2018. Asymmetric impacts of oil price uncertainty on Chinese stock returns under different market conditions: Evidence from oil volatility index. Energy Economics, 74: 777–786.

Zhao, L., E. Zivot, and J. Wang, 2004. Wavelet analysis in financial markets. University of Washington.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.