This study aims to examine the relationship between foreign exchange market volatility and the volume of customer service contacts at a financial brokerage firm, using VT Markets as a case study
Abstract
This study aims to examine the relationship between foreign exchange (Forex) market volatility and the volume of customer service contacts at a financial brokerage firm, using VT Markets as a case study during the year 2024, and to analyze the market volatility structural dynamics using the GARCH(1,1) model. A quantitative research approach was conducted utilizing daily time series data over 366 days encompassing major currency pairs and assets, including EUR/USD, GBP/USD, USD/JPY, and XAU/USD. The conditional variance extracted from the GARCH(1,1) model served as the primary independent variable representing market volatility, alongside socio-demographic and macroeconomic control factors, while daily contact volumes via Live Chat and email acted as the dependent variable. Spearman's Rank Correlation was implemented for preliminary association screening, followed by Ordinary Least Squares (OLS) Multiple Linear Regression as the primary inferential tool for hypothesis testing. The multiple regression results revealed that Forex market volatility exerts a positive and statistically significant impact on customer service contact volume at the 1% significance level. The regression framework demonstrated robust explanatory power, accounting for 71.9% of the total variance in daily contact volume (Adjusted R² = 0.719). Furthermore, critical economic news announcements, average client income, and national import-export values displayed significantly positive relationships with service demand. These empirical findings substantiate the core hypotheses and strongly align with the Information Seeking under Uncertainty theory, demonstrating that heightened market obscurity shifts investors towards aggressive information gathering and institutional support. Pragmatically, these insights facilitate scientific workforce capacity planning and the strategic establishment of volatility thresholds to trigger automated support mechanisms (AI Chatbots) and FAQ workflows during peak operational demand periods.
References
Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2003). Micro effects of macro announcements: Real-time price discovery in foreign exchange. American Economic Review, 93(1), 38-62. https://doi.org/10.1257/000282803321455151
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Chen and Li. (2022). The impact of market volatility on customer service demand in Chinese brokerage firms. Finance Research Letters, 46, 102415.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Hassan and Ozturk. (2025). Modeling realized volatility in forex and crypto markets using Realized GARCH. Journal of Financial Econometrics, 23(2), 244–262.
Kahneman and Tversky. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
Rahman, M. (2024). Hybrid GARCH–XGBoost approach for volatility and customer behavior forecasting. Computational Economics, 64(1), 55–74.
Zeithaml et al. (2018). Services Marketing: Integrating Customer Focus Across the Firm (7th ed.). McGraw-Hill Education.
Zhang et al. (2021). Forecasting market volatility and optimizing customer service resources: Evidence from forex trading platforms. Applied Economics Letters, 28(15), 1324–1328.
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