اثرات سرریز پویای ریسک میان نرخ ارز، سهام، مسکن و سکه در ایران: شواهدی جدید از مقایسه دوران تحریم و غیرتحریم

Roudari, Soheil and Arabi, Seyed Hadi and Shahabadi, abolfazl and Adeli, Omid Ali (2023): اثرات سرریز پویای ریسک میان نرخ ارز، سهام، مسکن و سکه در ایران: شواهدی جدید از مقایسه دوران تحریم و غیرتحریم. Published in: Journal of Financial Management Strategy , Vol. 13, No. 1 (12 April 2025): pp. 93-116.

Abstract

In the current study, the risk spillover between currency, housing, coin, and stock markets in the period of 1385:01- 1400:12 monthly using the vector autoregression model with time-varying parameters of Diebold-Yilmaz (DY-TVP-VAR) have been investigated. The results show that currency and coins assets are the main factors of volatilities in the studied asset markets. These two assets are not only transmitters of volatilities to other assets but also receivers of volatilities from other assets. The housing market has been the only receiver of the risk and volatilities of other markets, and the most volatilities have been transferred from currency and stocks to housing. According to the results, the most impact on the housing market was caused by currency, stocks, and coins. Also, the stock market has received the most impact from currency and coins. Based on the results, housing can provide risk hedging for the investment portfolio; in other words, it is a safe haven. Still, considering that over time, the number of coin fluctuations from other assets and the amount of transfer of fluctuations to other assets has been different, its selection should be based on other assets in the portfolio and political and economic conditions. It is not a safe haven under all conditions. Therefore, during the sanctions period and in conditions where the return on assets has a significant difference from the mean, using the DY-TVP-VAR model can bring better results for investors to manage their investment portfolios.

• Item Type: MPRA Paper
• English Title: Effects of dynamic risk spillover between exchange rates, stocks, housing and coins in Iran: New evidence from the comparison between sanctions and non-sanctions eras
• Language: Persian
• Keywords: Assets Market, Sanction, DY-TVP-VAR Model
• Subjects: G - Financial Economics > G0 - General > G01 - Financial Crises; 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

References

Akar, C. (2011). Dynamic relationships between the stock exchange, gold, and foreign exchange returns in Turkey. Middle Eastern Finance and Economics, 12, 109-115.

Asadi, M., Roubaud, D., & Tiwari, A. K. (2022). Volatility spillovers amid crude oil, natural gas, coal, stock, and currency markets in the US and China based on time and frequency domain connectedness. Energy Economics, 109, 105961.

Bakas, D., & Triantafyllou, A. (2018). The impact of uncertainty shocks on the volatility of commodity prices. Journal of International Money and Finance, 87, 96-111.

Bouri, E., Cepni, O., Gabauer, D., & Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International review of financial analysis, 73, 101646.

Branson, W.H. (1983), “Macroeconomic Determinants of Real Exchange Risk. In: Herring, R.J. (Ed.)”, Managing Foreign Exchange Risk, Cambridge University, Cambridge.

Chu, Y. (2009). Was it Really a Housing Bubble?. Available at SSRN 1353642.

Ciner, C., Gurdgiev, C., & Lucey, B. M. (2013). Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates. International Review of Financial Analysis, 29, 202-211.

Delgado, N. A. B., Delgado, E. B., & Saucedo, E. (2018). The relationship between oil prices, the stock market and the exchange rate: Evidence from Mexico. The North American Journal of Economics and Finance, 45, 266-275.

Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134.

Dornbusch, R., & Fischer, S. (1980). Exchange rates and the current account. The American economic review, 70(5), 960-971.

Early, B. R., & Cilizoglu, M. (2020). Economic sanctions in flux: Enduring challenges, new policies, and defining the future research agenda. International Studies Perspectives, 21(4), 438-477.

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.

Gavin, M. (1989). The stock market and exchange rate dynamics. Journal of international money and finance, 8(2), 181-200.

Gupta, J., Chevalier, A., & Sayekt, F. (2001). The causality between interest rate, exchange rate and stock price in emerging markets: The case of the Jakarta stock exchange. In Fuzzy Sets in Management, Economics and Marketing, 7(25) 145-163.

Hatipoglu, E., Considine, J., & AlDayel, A. (2022). Unintended Transnational Effects of Sanctions: A Global Vector Autoregression Simulation. Defence and Peace Economics, 33(5), 1-17.

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.

Liow, K. H., Song, J., & Zhou, X. (2021). Volatility connectedness and market dependence across major financial markets in China economy. Quantitative Finance and Economics, 5(3), 397-420.

Mensi, W., Hammoudeh, S., Al-Jarrah, I. M. W., Sensoy, A., & Kang, S. H. (2017). Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications. Energy Economics, 67, 454-475.

Nham, N., T. H. (2022). An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock, and cryptocurrencies during the COVID-19 health crisis. Technological Forecasting and Social Change, 83, 121909.

Pavlova, A., & Rigobon, R. (2007). Asset prices and exchange rates. The Review of Financial Studies, 20(4), 1139-1180.

Reboredo, J. C., Ugolini, A., & Hernandez, J. A. (2021). Dynamic spillovers and network structure among commodity, currency, and stock markets. Resources Policy, 74, 102266.

Shiller, R. J. (2007). Understanding recent trends in house prices and home ownership. Working paper 13553.

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.

Yunus, N. (2020). Time-varying linkages among gold, stocks, bonds and real estate. The Quarterly Review of Economics and Finance, 77, 165-185.

Zhao, H. (2010). Dynamic relationship between exchange rate and stock price: Evidence from China. Research in International Business and Finance, 24(2), 103-112.