
تعداد نشریات | 25 |
تعداد شمارهها | 953 |
تعداد مقالات | 7,829 |
تعداد مشاهده مقاله | 13,071,861 |
تعداد دریافت فایل اصل مقاله | 9,250,379 |
سرریز پویای ریسک میان نرخ ارز، سهام، مسکن و سکه در ایران: شواهدی جدید از مقایسه دوران تحریم و غیرتحریم | ||
راهبرد مدیریت مالی | ||
مقاله 5، دوره 13، شماره 1 - شماره پیاپی 48، فروردین 1404، صفحه 93-116 اصل مقاله (733.01 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22051/jfm.2025.41262.2718 | ||
نویسندگان | ||
سهیل رودری1؛ سید هادی عربی2؛ ابوالفضل شاه آبادی3؛ امیدعلی عادلی* 2 | ||
1دکتری، گروه اقتصاد، دانشکده علوم اداری و اقتصادی، دانشگاه فردوسی، مشهد ایران | ||
2دانشیار، گروه اقتصاد، دانشکده علوم اقتصادی و اداری، دانشگاه قم، قم، ایران | ||
3استاد گروه اقتصاد، دانشکده علوم اجتماعی و اقتصادی، دانشگاه الزهرا، تهران، ایران | ||
چکیده | ||
نحوه ارتباط میان نرخ ارز، قیمت سهام، مسکن و سکه بهعنوان موارد مدنظر سرمایهگذار جهت مدیریت پرتفو همیشه یک بحث پیچیده بوده است و ارتباط میان آنها و تعیین علیت انتقال نوسانات میان آنها (دریافت و انتقال نوسانات) ممکن است در هر کشور و در دورههای زمانی گوناگون متفاوت باشد. براین اساس در پژوهش حاضر سرریز ریسک میان بازارهای ارز، مسکن، سکه طلا و سهام در دوره زمانی 1400:12-1385:01 بهصورت ماهانه با استفاده از الگوی خودرگرسیون برداری با پارامترهای متغیر در زمان دیابولد-ایلماز (DY-TVP-VAR) بررسی شده است. نتایج نشان میدهد داراییهای ارز و سکه طلا عوامل اصلی انتقال و دریافت نوسانات در شبکه مورد بررسی هستند. بازار مسکن فقط دریافتکننده ریسک و نوسانات ازداراییهای دیگر بوده است و بیشترین نوسان از ارز و سهام به مسکن منتقل شده است. همچنین بازار سهام نیز بیشترین نوسان را از ارز و سپس سکه دریافت نموده است. براساس نتایج، مسکن میتواند پوشش ریسک را برای سبد سرمایهگذاری به همراه داشته باشد و به عبارتی پناهگاه امن میباشد اما با توجه به اینکه در طی زمان نحوه ارتباط سکه با سایر داراییها متفاوت بوده است، انتخاب آن بایستی بر اساس سایر داراییهای موجود در سبد و همچنین شرایط سیاسی و اقتصادی صورت پذیرد و پناهگاه امن تحت هر شرایطی نیست. براین اساس در دوران تحریم و شرایطی که بازدهی داراییها اختلاف معنیدار با میانگین دارد، استفاده از الگوی DY-TVP-VAR میتواند برای سرمایهگذاران نتایج بهتری را جهت مدیریت سبد سرمایهگذاری به همراه داشته باشد. | ||
کلیدواژهها | ||
مسکن؛ نرخ ارز؛ سکه طلا؛ بازار سهام؛ الگوی DY-TVP-VAR | ||
عنوان مقاله [English] | ||
Dynamic Spillover of Risk Between Exchange Rates, Stocks, Housing, and Gold Coins in Iran: New Evidence from Comparing Sanction and Non-Sanction Periods | ||
نویسندگان [English] | ||
Soheil Roudari1؛ Seyed Hadi Arabi2؛ Abolfazl Shahabadi3؛ Omidali Adeli2 | ||
1PhD, Department of Economics, Faculty of Administrative and Economic Sciences, Ferdowsi University, Mashhad, Iran | ||
2Associate Professor, Department of Economics, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran | ||
3Professor, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran, | ||
چکیده [English] | ||
The relationship between the exchange rate, stock price, housing, and coin as the items considered by the investor for portfolio management has always been a complex discussion, and the relationship between them and determining the cause of the transfer of volatilities (receiver and transmitter of volatilities) may be different in each country and different periods. According to this, 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 autoregressive model with time-varying parameters of Diebold-Yilmaz (DY-TVP-VAR) have been investigated. The results show that currency and gold coins are the main drivers of transferring and receiving volatilities in the investigated network. The housing market has only received the risk and volatilities of other assets, and the most volatilities have been transferred from currency and stocks to housing. Also, the stock market has received the most volatilities from currency and then coins. Based on the results, housing can provide risk hedging for the investment portfolio, and in other words, it is a safe haven, but coins relationship with other assets has been different over time; its selection should be based on other assets in the portfolio as well as political and economic conditions, and 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. | ||
کلیدواژهها [English] | ||
Housing, Currency, Gold Coin, Stock Market, DY-TVP-VAR Model | ||
سایر فایل های مرتبط با مقاله
|
||
مراجع | ||
Akar, C. (2011). Dynamic relationships between the stock exchange, gold, and foreign exchange returns in Turkey. Middle Eastern Finance and Economics, 12, 109-115.
Aloui, R; Jabeur, S. B; & Mefteh-Wali, S. (2022). Tail-risk spillovers from China to G7 stock market returns during the COVID-19 outbreak: A market and sectoral analysis. Research in International Business and Finance, 62, 101709.
Amiri, S, Homayoni Far, M, Karimzadeh, M. & Fallahi, M, A (2014). Investigating dynamic correlation between major assets in Iran using DCC-GARCH method, Economic Research Quarterly (Sustainable Growth and Development), 15(2), 183-201. (In Persion)
Argha, L; Shahabadi, A. & Rudari, S (2018). Threshold effect of exchange rate growth on the efficiency of the industrial sector in Iran, Economic and Modeling Quarterly, 10 (4), 1-26. (In Persion)
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.
Ashna, M. & Lal Khazari, H (2019). Dynamic Correlation of Global Economic Policy Uncertainty Index with Volatility of Stock, Currency and Coin Markets in Iran: Application of M-GARCH Model of DCC Approach, Econometric Modeling Quarterly, 5(2), 147-172. (In Persion)
Balcilar, M; Gabauer, D; & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: new evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219.
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.
Dadmehr, M, Rahnamai Rudpashti, F, Nikumram, H. & Fallah Shams, M. (1400). Investigating contagion between monetary and financial markets in Iran, Economic and Modeling Quarterly, 12(2), 123-166. (In Persion)
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.
Falahi, F, Haqit, J, Sanobar, N. and Jahangiri, K (2013). Investigating the correlation between stock, currency and coin market volatility in Iran using the DCC-GARCH model, Research Journal of Economics, 14 (55), 123-147. (In Persion)
Farzanegan, E (2024). Investigating the Contagion Effect of Systemic Risk Among Main Industries in the Tehran Stock Exchange: A Sequence-Event- Based Network Approach. Financial Management Strategy, 12(1), 113-138. (In Persion) 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.
Gong, X; Xu, J; Zhou, Z; & Liu, T. (2022). Dynamic volatility connectedness between industrial metal markets. The North American Journal of Economics and Finance, 101814.
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.
Heydari, H. & Malabahrami, A (2009). Stock investment portfolio optimization based on multivariate GARCH models: Evidence from Tehran Stock Exchange, Financial Research Quarterly, 12(30), 35-56. (In Persion)
Hosseinyoun, N, Behnameh, M. & Ebrahimi Salari, T (2015). Investigating the transmission of return volatility between stock, gold and currency markets in Iran, Iran Economic Research Quarterly, 21(66), 123-150. (In Persion)
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.
Pazouki, N, Hamidian, A, Mohammadi, Sh. & Mahmoudi, V (2012). Using wavelet transformation to investigate the correlation of different exchange rates, oil price, gold price and Tehran stock exchange index in different time scales, Danesh Investment Quarterly, 2(7), 131-148. (In Persion)
Reboredo, J. C; Ugolini, A; & Hernandez, J. A. (2021). Dynamic spillovers and network structure among commodity, currency, and stock markets. Resources Policy, 74, 102266.
Saranj, A, & Rafiei, M. (2023). Explaining the Nonlinear Reaction of the Tehran Stock Exchange Price Index (Value-Weighted) to Oil Shocks Using the Markov Switching Model. Financial Management Strategy, 11(4), 1-24. (In Persion)
Sezavar, M, Khazaei, A. & Islamian, M (2018). Examining the conditional correlation between foreign exchange, gold, housing, stocks and oil markets in Iran's economy, Economic Strategy Quarterly, 8(29), 37-60. (In Persion)
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.
Yarovaya, L; Brzeszczyński, J; & Lau, C. K. M. (2016). Intra-and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures. International Review of Financial Analysis, 43, 96-114.
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. | ||
آمار تعداد مشاهده مقاله: 53 تعداد دریافت فایل اصل مقاله: 31 |