Roudari, Soheil (2024): Optimal Investment Portfolio and Time‑Varying Risk Hedging: New Evidence from Currency, Stock, Gold Coin, and Housing Markets.
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Abstract
One of the primary objectives of investors is to determine the optimal asset weights and the appropriate risk‑hedging strategies considering the holding period of each asset. Accordingly, this study examines and determines the optimal investment portfolio and the dynamic risk‑hedging relationships among currency, gold coin, housing, and stock markets over the period March 2006 to February 2023, employing a Time‑Varying Parameter Vector Autoregression (TVP‑VAR) model. The results indicate that stock and currency markets act as net transmitters, while gold coin and housing markets are net receivers of volatility within the examined network. The total connectedness index reveals that during periods of sanctions and the COVID‑19 pandemic, the interdependence among these markets intensified, thereby limiting diversification opportunities within the investment portfolio. Furthermore, cumulative returns under the Minimum Variance Portfolio (MVP) approach exceed those obtained under the Minimum Connectedness Portfolio (MCOP) framework. Based on the findings, the optimal combination involves holding stocks in the short run and housing in the long run. Out of one unit of investment under normal, bearish, and bullish market conditions, 0.97, 0.96, and 0.98 units, respectively, should be allocated to this combination. The study concludes that a static view of asset behavior is not appropriate for portfolio optimization. Instead, risk‑hedging and optimal asset weighting must be considered dynamically, reflecting economic, political, and health‑related conditions.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | Optimal Investment Portfolio and Time‑Varying Risk Hedging: New Evidence from Currency, Stock, Gold Coin, and Housing Markets |
| English Title: | Optimal Investment Portfolio and Time‑Varying Risk Hedging: New Evidence from Currency, Stock, Gold Coin, and Housing Markets |
| Language: | Persian |
| Keywords: | Hedging effectiveness, time‑varying optimal weights, asset markets |
| Subjects: | G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill |
| Item ID: | 126952 |
| Depositing User: | Dr Soheil Roudari |
| Date Deposited: | 22 Nov 2025 08:11 |
| Last Modified: | 22 Nov 2025 08:11 |
| References: | Adekoya, O. B., Akinseye, A. B., Antonakakis, N., Chatziantoniou, I., Gabauer, D., & Oliyide, J. (2022). Crude oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies. Resources Policy, 78, 1-15. Ahmed, A, Huo, R. (2021). Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China, Energy Economics, 93,1-14. Alshater, M. M., Alqaralleh, H., & El Khoury, R. (2023). Dynamic asymmetric connectedness in technological sectors. The Journal of Economic Asymmetries, 27, 1-15. Antonakakis, N., Chatziantoniou, I., and Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 1-15. Aroury, M.E.H. Lahiani, A. &khuong Nguyan D. (2015). World gold prices and stock returns in China: Insights for hedging and diversification strategies. Economic Modeling, 44, 273-282. Cao, G., & Xie, W. (2022). Asymmetric dynamic spillover effect between cryptocurrency and China's financial market: Evidence from TVP-VAR based connectedness approach. Finance Research Letters, 49, 103070. Frankel, J. A. (1992). Monetary and portfolio-balance models of exchange rate determination. In International economic policies and their theoretical foundations (pp. 793-832). Academic Press. Gkillas, K., Vortelinos, D. I., & Suleman, T. (2018). Asymmetries in the African financial markets. Journal of Multinational Financial Management, 45, 72-87. Karolyi, G. A. (1995). A multivariate GARCH model of international transmissions of stock returns and volatility: The case of the United States and Canada. Journal of Business & Economic Statistics, 13(1), 11-25. Li, X., Li, B., Wei, G., Bai, L., Wei, Y., & Liang, C. (2021). Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US. Resources Policy, 73, 102166. Liew, P. X., Lim, K. P., & Goh, K. L. (2022). The dynamics and determinants of liquidity connectedness across financial asset markets. International Review of Economics & Finance, 77, 341-358. Rehman, M. U., Vo, X. V., Ko, H. U., Ahmad, N., & Kang, S. H. (2023). Quantile connectedness between Chinese stock and commodity futures markets. Research in International Business and Finance, 64, 101810. Reboredo, J. C., Ugolini, A., & Hernandez, J. A. (2021). Dynamic spillovers and network structure among commodity, currency, and stock markets. Resources Policy, 74, 102266. Saiti, B., & Masih, M. (2016). The co-movement of selective conventional and Islamic stock indices: is there any impact on shariah compliant equity investment in China? International Journal of Economics and Financial Issues, 6(4), 1895-1905. Yunus, N. (2020). Time-varying linkages among gold, stocks, bonds and real estate. The Quarterly Review of Economics and Finance, 77, 165-185. Jiang,Y., Fu,Y., Ruan,W. (2019) Risk spillovers and portfolio management between precious metal and BRICS stock markets. Physica A, 534,120993. Nguyen, N. H., nguyen, H. D., VO, L. T. K., & tran, C. Q. K. (2021). The impact of exchange rate on exports and imports: Empirical evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 8(5), 61-68. Pavlova, A., & Rigobon, R. (2007). Asset prices and exchange rates. The Review of Financial Studies, 20(4), 1139-1180. Salisu, A, Isah, K, A, A (2019). Dynamic spillovers between stock and money markets in Nigeria: A VARMA-GARCH approach. Review of Economic Analysis ,11,255-283. Sathyanarayana, S., & Gargesa, S. (2018). An analytical study of the effect of inflation on stock market returns. IRA-International Journal of Management & Social Sciences, 13(2), 48-64. Su, J. B., & Kao, Y. S. (2022). How does the crisis of the COVID-19 pandemic affect the interactions between the stock, oil, gold, currency, and cryptocurrency markets?. Frontiers in public health, 10, 1-18. Samadi, A.H., Owjimehr, S. & Halafi, Z.N. (2021). The cross-impact between financial markets, Covid-19 pandemic, and economic sanctions: The case of Iran. Journal of Policy Modeling, 43(1), 34-55. Spencer, S., Bredin, D., & Conlon, T. (2018). Energy and agricultural commodities revealed through hedging characteristics: Evidence from developing and mature markets. Journal of Commodity Markets, 9, 1-20. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/126952 |

