سرریز تلاطمات متغیر در طول زمان بین نرخ ارز و بازار سهام تهران؛ شواهد جدیدی از پاندمی کرونا

پژوهش حاضر با به‌کارگیری شاخص سرریز دیبولد-یلماز مبتنی بر تجزیه واریانس یک مدل خودرگرسیون برداری با پارامترهای متغیر در طول زمان (tvp-var) با استفاده از داده‌های روزانه به سنجش سرریزهای پویای تلاطمات میان دلار و شاخص سهام 8 صنعت بورسی مشتمل بر «شیمیایی»، «فلزات اساسی»، «فرآورده‌های نفتی»، «استخراج کانه‌های فلزی»، «کشاورزی»، «قند و شکر»، «سیمان» و «کاشی و سرامیک» دربازه مهرماه سال 1394 تا مهرماه سال 1402می‌پردازد. یافته‌ها حاکی از آن است که اتصالات کل که نماینده ریسک سیستمی شبکه مورد بررسی است در دوره پیش از همه‌گیری کووید-19 به طور متوسط حدوداً 50 درصد بوده است و دوره بعد از شیوع این بیماری اتصالات درون شبکه بسیار شدیدتر شده و حتی در برخی بازه‌های زمانی بالغ بر 70 درصد نیز بوده است. بالاترین ریسک منحصر به فرد به متغیر دلار (62/75 درصد) و در مقابل، کمترین ریسک منحصر به فرد به شاخص‌های صنایع فلزات اساسی (52/34) و کانه‌های فلزی (59/34) اختصاص دارد. در شبکه مورد بررسی متغیر دلار به طور متوسط از تلاطمات صنایع کالا‌محور صادراتی به ویژه فلزات اساسی متاثر می‌شود و صرفاً خالص انتقال‌دهنده تلاطمات به صنایع کوچک بورسی خصوصاً کاشی و سرامیک بوده است. در این سیستم صنعت فلزات اساسی به عنوان قوی‌ترین انتقال‌دهنده تلاطمات شناسایی می‌شود و صنعت کشاورزی و سرامیک نیز مهم‌ترین پذیرندگان شوک‌ها محسوب می‌شوند.

time-varying volatility spillovers between exchange rate and tehran stock exchange; new evidences of the covid-19 pandemic

this study employs the diebold-yilmaz spillover index within the framework of a time-varying parameter vector autoregressive model (tvp-var) to analyze the dynamic connectedness between exchange rates and the iranian stock market amidst the covid-19 pandemic. utilizing daily data spanning from october 2014 to october 2023, we examine the volatility spillovers between the us dollar and the stock indices of eight industries, including &chemicals&, &basic metals&, &petroleum products&, &extraction of metal ores&, &agriculture&, &sugar&, &cement&, and &ceramics&. our findings reveal that systemic risk, represented by total connectedness within the network, averaged approximately 50% before the onset of the covid-19 pandemic. however, following the emergence of the pandemic, network connections intensified significantly, surpassing 70% at times. the us dollar variable exhibits the highest idiosyncratic risk (75.62%), while the indices of basic metal industries (34.52%) and metal ores (34.59%) demonstrate the lowest idiosyncratic risk. analysis of the network dynamics indicates that volatility originating from export-oriented commodity industries, particularly basic metals, predominantly influences the us dollar variable, acting as a net transmitter of volatilities to smaller industries, notably ceramics. moreover, the basic metal industry emerges as the primary transmitter of volatilities within the network, with the agricultural and ceramic industries identified as significant recipients of shocks.introductionfinancial asset markets are subject to volatility at one point or another due to domestic or global political, economic, and social events. it is clear that major events such as the covid-19 pandemic can significantly alter the relationships between markets. in such circumstances, studying the dynamics of correlations and information flows between different assets and markets becomes important and provides investors, policymakers, and portfolio managers with deeper insights. in these circumstances, the two foreign exchange and stock markets react strongly to events and affect the economy. therefore, this article aims to answer the following questions:how do the dynamics of dollar rate volatility affect the returns of various stock market industry indices, and how do the dynamics of stock market industry index return volatility affect the dollar? how does the connectedness between the dollar rate and stock market industry indices change in the period before and after the covid-19 outbreak?methodthe present study uses a tvp-var approach. the method overcomes certain shortcomings of the connectedness criteria of standard var models, such as “the arbitrarily chosen rolling window size”, “missing observations”, and “parameters sensitive to outliers”.resultsan examination of the dynamic spillovers of return volatilities between the exchange rate and the stock index of 8 listed industries, including &chemicals&, &basic metals&, &petroleum products&, &metal ore&, &agriculture&, &sugar&, &cement& and &ceramics& during the period from october 2014 to october 2023, shows:the total connectedness index is around 53 percent, which indicates a relatively high systemic risk in the network.the dynamics of the total directional net connectedness index indicate that each variable has been a net transmitter of shocks in some periods and a net receiver of shocks in others. however, in the overall period review, the basic metals, cement, chemical, metal ore, and petroleum products industries act as transmitters of shocks, and the agricultural, ceramic and sugar industries and dollar act as receivers of shocks in the network.the dynamics of the total connectedness index during the period of study indicate a significant increase in this index after the covid-19 pandemic, with the highest figure for the index also being experienced after the outbreak of this disease.in the network, the basic metals industry is identified as the strongest transmitter of shocks, and the agricultural and ceramic industries are also the most important shock receivers. in addition, on average, the dollar is affected by the shocks of export-oriented commodity industries, especially basic metals, and has been a net transmitter of shocks to small-stock industries, especially ceramics.table 1. averaged dynamic connectedness usdchemicalspetroleumproductsbasic metalsmetal oreagriculturesugarcementceramicsfrom usd75.624.383.404.503.311.801.344.031.6124.38chemicals3.8936.3510.5415.8215.014.023.027.393.9463.65petroleumproducts1.8911.9344.4915.0711.012.203.235.944.2455.51basic metals2.8514.0612.1934.5219.713.013.236.903.5265.48metal ore2.9114.1210.0622.6334.593.233.376.063.0365.41agriculture2.626.014.764.305.1756.286.809.094.9843.72sugar1.926.444.554.684.086.1151.8711.498.8648.13cement2.136.616.558.946.316.838.7042.1911.7357.81ceramics2.665.174.555.644.754.989.3615.5847.3252.68to20.8768.7356.6181.5869.3532.1739.0666.4841.92476.77net-3.515.081.1016.103.94-11.55-9.078.67-10.7652.97npdc355870251     figure 1. dynamics of total connectedness index  figure 1. net pairwise directional connectedness conclusionthis research provides valuable insights for policymakers in formulating growth-stimulating policies and designing preventive measures against systemic risk. additionally, it offers investors an efficient tool for constructing optimal investment portfolios tailored to systemic risk considerations.acknowledgmentswe would like to thank the esteemed editorial board for their efforts in improving this article.