Factors Affecting Investment Funds Investing in Different Asset Classes

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Mirjana Veselinović
Dejan Živkov
Suzana Balaban

Abstract

This paper examines how global financial and macroeconomic factors transmit shock and volatility spillovers to investment funds investing in different asset classes. Using daily data from January 2015 to December 2025, the authors analyse three U.S. funds representing bond, commodity, and equity exposures (PIMCO, USCI, and XLK) and five global factors: 1-month and 10-year U.S. interest rates, the S&P 500, Brent crude oil, and gold. The asymmetric TGARCH models are first estimated to obtain standardized residuals and conditional variances, after which a two-regime Markov switching framework is applied to capture the differences between high- and low-volatility periods. The results show strong regime- and fund-specific spillovers. S&P 500 shocks strongly affect XLK in both regimes, while oil and gold shocks dominate USCI during turbulent periods. Volatility spillovers are most pronounced for USCI and XLK, whereas PIMCO remains relatively insulated. These findings provide regime-aware implications for investors and fund managers.

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Alexakis, C., Niarchos, N., Patra, T., & Poshakwale, S. (2005). The dynamics between stock returns and mutual fund flows: Empirical evidence from the Greek market. International Review of Financial Analysis, 14(5), 559–569.

Alsubaiei, B.J., Calice, G., & Vivian, A. (2023). How does oil market volatility impact mutual fund performance? International Review of Economics and Finance, 89(PA), 1601–1621.

Amar, R., Candelon, B., Lecourt, C., & Xun, W. (2019). Country factors and the investment decision-making process of sovereign wealth funds. Economic Modelling, 81, 72–85.

Assefa, T. A., Esqueda, O. A., & Mollick, A. V. (2017). Stock returns and interest rates around the world: A panel data approach. Journal of Economics and Business, 89, 20–35.

Ausloos, M., Zhang, Y., & Dhesi, G. (2020). Stock index futures trading impact on spot price volatility: The CSI 300 studied with a TGARCH model. Expert Systems with Applications, 160, 113688.

Babalos, V., & Balcilar, M. (2017). Does institutional trading drive commodities prices away from their fundamentals? Evidence from a nonparametric causality in quantiles test. Finance Research Letters, 21, 126–131.

Bali, T. G., Brown, S. J., & Caglayan, M. O. (2014). Macroeconomic risk and hedge fund returns. Journal of Financial Economics, 114(1), 1–19.

Boubaker, S., Karim, S., Naeem, M. A., & Sharma, G. D. (2023). Financial markets, energy shocks, and extreme volatility spillovers. Energy Economics, 126, Article 107031.

Çepni, O., Christou, C., & Gupta, R. (2023). Forecasting national recessions of the United States with state-level climate risks: Evidence from model averaging in Markov-switching models. Economics Letters, 227, 111121.

Ciarlone, A., & Miceli, V. (2016). Escaping financial crises? Macro evidence from sovereign wealth funds’ investment behaviour. Emerging Markets Review, 27, 169–196.

Dekker, P., Vivar, L.M., Wedow, M., & Weistroffer, C. (2024). Liquidity buffers in open-end corporate bond funds during the COVID 19 market turmoil. International Review of Financial Analysis, 87, 101909.

Đekić, M., Gavrilović, M., Roganović, M., & Gojković, R. (2017). The role of investment funds in countries with transition economies. Economic Analysis, 50(1–2), 1–12.

Fiszeder, P., Fałdziński, M., & Molnár, P. (2023). Investor attention to oil prices and the comovement of ETFs: A multivariate volatility approach. Energy Economics, 118, 106643.

Jiang, Z., Ozcelebi, O., Lü, Z., El Khoury, R., & Yoon, S.-M. (2026). Global bond fund responses to financial uncertainty: Evidence from volatility, geopolitical risk, and digital currency shocks. Global Finance Journal, 53, 101227.

Koo, M., & Muslu, V. (2023). Fund flows and asset valuations of bond mutual funds: Effect of side by side management. Journal of Banking and Finance, 154, 106961.

Korenak, B., & Stakić, N. (2021). Beyond the returns - the U.S. mutual funds value and growth style weighted sector portfolios investment performance attribution. Economic Analysis, 54(2), 1–19.

Krause, T., & Tse, Y. (2013). Volatility and return spillovers in Canadian and U.S. industry ETFs. International Review of Economics and Finance, 25, 244–259.

Lee, B.-S., Paek, M., Ha, Y., & Ko, K. (2015). The dynamics of market volatility, market return, and equity fund flow: International evidence. International Review of Economics and Finance, 35, 214–227.

Leite, P. (2024). Performance and investment styles of international multi-asset funds during market crises. Empirica, 51, 783–805.

Liu, Y., & Hu, J. (2025). Sovereign wealth fund performance during the COVID 19 pandemic: Regional and strategic perspectives. Digital Finance, 3(1), 100047.

Musawa, N., & Mwaanga, C. (2017). The impact of commodity prices, interest rate and exchange rate on stock market performance: Evidence from Zambia. Journal of Financial Risk Management, 6(3), 300–313.

Pinto-Ávalos, F., Bowe, M., & Hyde, S. (2024). Revisiting the pricing impact of commodity market spillovers on equity markets. Journal of Commodity Markets, 33, 100369.

Qian, L., Zeng, Q., & Li, T. (2022). Geopolitical risk and oil price volatility: Evidence from Markov-switching model. International Review of Economics and Finance, 81(C), 29–38.

Sabiruzzaman, M., Huq, M. M., Beg, R. A., & Anwar, S. (2010). Modeling and forecasting trading volume index: GARCH versus TGARCH approach. The Quarterly Review of Economics and Finance, 50(2), 141–145.

Shah, W. U., Missaoui, I., Younis, I., & Liu, X. (2025). Evaluating market downturn connectedness between S&P 500 index funds, gold, and oil markets. Journal of Futures Markets, 45(9), 1278–1297.

Shi, Y. (2022). A closed-form estimator for the Markov switching in mean model. Finance Research Letters, 44, 102107.

Stützle, M. (2020). Persistence of averages in financial Markov switching models: A large deviations approach. Physica A: Statistical Mechanics and Its Applications, 553, 124237.

Valadkhani, A., & Marashdeh, H. (2026). Regime-dependent causality between Chinese and U.S. equity markets: Evidence from Markov switching models. Research in International Business and Finance, 83, Article 103285.

Wu, H., Li, P., Cao, J., & Xu, Z. (2024). Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model. Energy Economics, 134, 107588.

Wu, H. (2025). Cultural biases in the investment decision-making process of institutional investors. International Review of Financial Analysis, 106, 104576.

Xu, Y., Guan, B., Lu, W., & Heravi, S. (2024). Macroeconomic shocks and volatility spillovers between stock, bond, gold and crude oil markets. Energy Economics, 136, 107750.

Zhang, X., & Zhang, T. (2022). Barrier option pricing under a Markov regime switching diffusion model. The Quarterly Review of Economics and Finance, 86, 273–280.