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Journal articles on the topic 'Volatility linkages'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Olabisi, Michael. "Input–Output Linkages and Sectoral Volatility." Economica 87, no. 347 (November 29, 2019): 713–46. http://dx.doi.org/10.1111/ecca.12327.

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2

Ding, Ashley. "Information and volatility linkages across energy and financial markets." Australian Journal of Management 44, no. 4 (July 26, 2019): 594–613. http://dx.doi.org/10.1177/0312896219862320.

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This study examines information and volatility linkages across energy and financial markets. In a world economy so connected, the impacts of climate change are likely to be transmitted through interlinked global markets. Hence, uncovering and understanding the interaction across these markets is a fundamental concern during the energy transition as it helps to understand how to strengthen incentives to facilitate energy investments. Based on the relation between information flows and volatility, this study employs a simple correlation approach based on implied volatility measures and the trading model of Fleming et al. to measure the common information linkages, as gauged by the correlation of return volatilities. The results suggest that volatility linkages across these markets are strong due to common information sharing and cross-market hedging. JEL Classification: G12, G14
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3

Liow, Kim Hiang, and Felix Schindler. "Linkages between office markets in Europe: a volatility spillover perspective." Journal of Property Investment & Finance 35, no. 1 (February 6, 2017): 3–25. http://dx.doi.org/10.1108/jpif-02-2016-0010.

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Purpose Using a data set comprising 16 European office markets provided by the DTZ Research Institute from Q1 2003 to Q4 2013, the purpose of this paper is to measure the strength of the unconditional transmission of volatility in the returns to direct property between 16 European office markets with the objective of determining the degree of unconditional spillover between markets. Design/methodology/approach To examine volatility spillovers across the 16 office markets, the authors adopted the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) by measuring cross-office market volatility transmission in asset pricing through estimates of several “volatility spillover indices.” Findings Volatility spillovers are important and time-varying across the leading office markets, with cross-market volatility interaction being bi-directional and of relative endogenous nature for many markets. The London office market is the “volatility leader” and has exerted significant net volatility influence on the other markets. Additionally, the volatility spillovers between business cycle fluctuations and asset market cycle volatilities are linked across some European economies. Research limitations/implications Evidence of co-integration among the domestic volatility spillover cycles implies the presence of unobserved common shocks and might not be good news for international investors who pursue diversification strategies in European office real estate markets. Originality/value No previous study has addressed formally the measurement and assessment of the nature and intensity of volatility spillovers across direct office markets on such a broad range of European office markets. The relevance of the topic has been even increasing over the previous years as more and more investors seek for flexibility and participation in the investment process and asset management.
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Nikkinen, Jussi, Petri Sahlström, and Sami Vähämaa. "Implied volatility linkages among major European currencies." Journal of International Financial Markets, Institutions and Money 16, no. 2 (April 2006): 87–103. http://dx.doi.org/10.1016/j.intfin.2004.12.007.

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ZHU, HONGQUAN, ZUDI LU, SHOUYANG WANG, and ABDOL S. SOOFI. "CAUSAL LINKAGES AMONG SHANGHAI, SHENZHEN, AND HONG KONG STOCK MARKETS." International Journal of Theoretical and Applied Finance 07, no. 02 (March 2004): 135–49. http://dx.doi.org/10.1142/s0219024904002414.

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In this paper, we test for causal relationship between China's stock markets by using returns and a measure of volatility for the Shanghai Composite index, the Shenzhen Composite Subindex, and the Hong Kong Hang Seng Index. We also show that the stock index series are nonstationary and that cointegrating vectors and error correction models do not exist for the series. Based on these tests, for the return series, we conclude that Shenzhen Granger caused Shanghai before 1994. For the volatility data, we find that there exists a positive feedback relationship between Shanghai and Shenzhen stock markets, and that Hong Kong volatility Granger causes Shanghai volatility, but not vice versa.
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Äijö, Janne. "Implied volatility term structure linkages between VDAX, VSMI and VSTOXX volatility indices." Global Finance Journal 18, no. 3 (January 2008): 290–302. http://dx.doi.org/10.1016/j.gfj.2006.11.003.

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7

Sholihah, Fathimah, and Nunung Kusnadi. "Dampak Pengembangan Biofuels terhadap Volatilitas Harga Beberapa Komoditas Pangan di Pasar Dunia." Jurnal Agro Ekonomi 37, no. 2 (April 20, 2020): 157. http://dx.doi.org/10.21082/jae.v37n2.2019.157-170.

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<strong>English</strong><br />Agricultural product based biofuels are the connecting points of the linkages between the global agricultural commodity, energy, and financial markets. Since the global energy markets and financial markets are volatile in nature, rapid expansion of biofuels industry results in increasing volatility of agricultural commodity prices, particularly food prices. The aims of this research is to review price volatility of some food commodities (wheat, corn and soybean) in in the world markets and to analyze the impact of global biofuels development on the price volatility. The price volatility is analyzed using the ARIMA and ARCH GARCH methods. The results show that prices of food commodities have been more volatile since the United States of America imposed the Renewable Fuel Standard Mandate-2 policy in 2007. The Corn and soybean price volatilities are higher than rice and wheat. The stronger are their linkages with biofuels development, the higher are their price volatilities. Increasing food price volatility and level should be considered as challenges and opportunities for accelerating food production growth through technological innovation and land expansion toward the achievement food self-sufficiency such that the national food security system is resilient against global market disturbances.<br /><br /><br /><strong>Indonesian</strong><br />Biofuels berbahan baku hasil pertanian menjadi komoditas penghubung antara pasar komoditas pertanian dengan pasar energi, dan selanjutnya dengan pasar finansial dunia. Oleh karena pasar energi dan pasar finansial dunia rentan gejolak maka pengembangan biofuel secara besar-besaran berdampak pada peningkatan volatilitas harga komoditas pertanian, utamanya komoditas pangan pokok. Penelitian bertujuan untuk meninjau volatilitas harga jagung, gandum, beras dan kedelai di pasar dunia serta untuk menganalisis dampak pengembangan biofuels terhadap volatilitas harga tersebut. Analisis volatilitas harga dilakukan dengan metode ARIMA dan ARCH GARCH. Penelitian menunjukkan bahwa harga komoditas pangan lebih volatil setelah Amerika Serikat menerapkan kebijakan Renewable Fuels Standard Mandate-2 tahun 2007. Volatilitas harga jagung dan kedelai lebih tinggi daripada beras dan gandum. Semakin besar keterkaitan komoditas dengan pengembangan biofuels maka semakin besar pula volatilitas harga komoditas tersebut. Peningkatan volatilitas dan level harga tersebut dapat dipandang sebagai tantangan dan peluang untuk memacu peningkatan produksi pangan melalui pengembangan teknologi dan ekstensifikasi lahan pertanian guna meningkatkan kemandirian pangan sehingga sistem ketahanan pangan nasional lebih tahan menghadapi gejolak pasar global.
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8

Iqbal, Muhammad Junaid, Afsheen Abrar ., Nagina Jamil ., Abid Ali Shah ., and AhsanulHaqSatti . "Volatility Linkages between Equity Markets of Pakistan, India, Singapore and Hong Kong: A GARCH BEKK Approach." Journal of Economics and Behavioral Studies 4, no. 1 (January 15, 2012): 47–54. http://dx.doi.org/10.22610/jebs.v4i1.301.

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The purpose of current study is to explore the volatility linkages between four Asian equity markets, which arePakistan (Karachi Stock Exchange), India (Bombay Stock Exchange), Hong Kong (Hang Sang Index) and Singapore (Strait Time Index). We estimate Multivariate GARCH BEKK model using weekly returns from January 2000 to August 2011.Direct evidences of linkages are found among all markets with respect to conditional mean returns and volatility.Own volatility spillover is found greater than cross volatility spillover in all emerging and developed economies.The insinuation of this study is that overseas investors may take advantage from the decrease of uncertainty by accumulating the stocks in the emerging markets to their investment portfolio.
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Sharma, Gagan, Parthajit Kayal, and Piyush Pandey. "Information Linkages Among BRICS Countries: Empirical Evidence from Implied Volatility Indices." Journal of Emerging Market Finance 18, no. 3 (August 21, 2019): 263–89. http://dx.doi.org/10.1177/0972652719846315.

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In this article, we examine the information linkages of the forward-looking measure of volatility, the volatility index (VIX), for underlying equity market indices of BRICS countries—Brazil, Russia, India, China and South Africa. A study of the information transmission process confirmed a long-run equilibrium relationship between pairs of BRICS countries. The multivariate generalised autoregressive conditional heteroscedasticity (MGARCH) model revealed strong intertemporal linkages between sample VIX. Return and volatility spill-over matrix show the varying degree of connectedness of BRICS VIX across the study period. This study contributes to the international finance literature and has important implications for investors, portfolio managers, policymakers and academia. JEL Classification: C58, F36, G11, G14, G15
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Ahmed, Abdullahi D., and Rui Huo. "China–Africa financial markets linkages: Volatility and interdependence." Journal of Policy Modeling 40, no. 6 (November 2018): 1140–64. http://dx.doi.org/10.1016/j.jpolmod.2018.05.002.

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11

Taghizadeh-Hesary, Farhad, Ehsan Rasoulinezhad, and Naoyuki Yoshino. "Energy and Food Security: Linkages through Price Volatility." Energy Policy 128 (May 2019): 796–806. http://dx.doi.org/10.1016/j.enpol.2018.12.043.

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12

López Cabrera, Brenda, and Franziska Schulz. "Volatility linkages between energy and agricultural commodity prices." Energy Economics 54 (February 2016): 190–203. http://dx.doi.org/10.1016/j.eneco.2015.11.018.

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13

Fleischer, Petra. "Volatility and Information Linkages Across Markets and Countries." Australian Journal of Management 28, no. 3 (December 2003): 251–72. http://dx.doi.org/10.1177/031289620302800302.

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14

Baklaci, Hasan F., Ömür Süer, and Tezer Yelkenci. "Volatility Linkages Among Gold Futures in Emerging Markets." Emerging Markets Finance and Trade 52, no. 1 (August 14, 2015): 1–9. http://dx.doi.org/10.1080/1540496x.2015.1062292.

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15

Chow, Hwee Kwan. "Volatility Spillovers and Linkages in Asian Stock Markets." Emerging Markets Finance and Trade 53, no. 12 (July 20, 2017): 2770–81. http://dx.doi.org/10.1080/1540496x.2017.1314960.

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16

Etienne, Xiaoli Liao, Andrés Trujillo-Barrera, and Seth Wiggins. "Price and volatility transmissions between natural gas, fertilizer, and corn markets." Agricultural Finance Review 76, no. 1 (May 3, 2016): 151–71. http://dx.doi.org/10.1108/afr-10-2015-0044.

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Purpose – The purpose of this paper is to investigate the price and volatility transmission between natural gas, fertilizer (ammonia), and corn markets, an issue that has been traditionally ignored in the literature despite its significant importance. Design/methodology/approach – The authors jointly estimate a vector error correction model for the conditional mean equation and a multivariate generalized autoregressive heteroskedasticity model for the conditional volatility equation to investigate the interactions between natural gas, ammonia, and corn prices and their volatility. Findings – The authors find significant interplay between fertilizer and corn markets, while only a mild linkage in prices and volatility exist between those markets and natural gas during the period 1994-2014. There is not only a positive relationship between corn and ammonia prices in the short run, but both prices react to deviations from the long-run parity. Furthermore, the lagged conditional volatility of ammonia prices positively affects conditional volatility in the corn market and vice versa. This result is robust to a specification using crude oil price as an alternative to natural gas price to account for the large transportation cost built into ammonia prices. Results for the period of 2006-2014 indicate virtually no linkage between natural gas prices and those of fertilizer and corn during that period, while linkages in price level and volatility between the latter remain strong. Originality/value – This paper is the first in the literature to comprehensively examine the role of fertilizer on corn prices and volatility, and its relation to natural gas prices.
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17

Wang, Kent. "Volatility linkages of the equity, bond and money markets: an implied volatility approach." Accounting & Finance 49, no. 1 (March 2009): 207–19. http://dx.doi.org/10.1111/j.1467-629x.2008.00281.x.

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18

Dutt, Mala, and Sanjay Sehgal. "Domestic and International Information Linkages between Gold Spot and Futures Markets: An Empirical Study for India." Metamorphosis: A Journal of Management Research 17, no. 1 (May 8, 2018): 1–17. http://dx.doi.org/10.1177/0972622518761745.

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This article examines information linkages between gold spot market in India and gold futures at India’s Multi Commodity Exchange (MCX) and five international platforms [i.e., Commodity Exchange (COMEX), Dubai Gold and Commodity Exchange (DGCX), Tokyo Commodity Exchange (TOCOM), Hong Kong Exchange (HKE) and Singapore Mercantile Exchange (SMX)] from August 2008 to March 2015. Cointegration procedure and vector error correction model (VECM), supported by Granger causality, are employed to study price discovery process, and bivariate EGARCH-BEKK model is used to examine volatility spillover process. At domestic level, spot market dominates the futures in information transmission process. Internationally, DGCX leads all other exchanges in price discovery process, while COMEX leads in volatility spillovers. In price discovery, MCX leads only TOCOM till August 2013, while price discovery is absent thereafter. In volatility spillovers, MCX dominates TOCOM and HKE till this period and only HKE afterwards. Thus, information linkages between MCX and international exchanges appear to have been impacted severely since August 2013. The study highlights the need to re-establish price and volatility linkages between Indian and international exchanges, and also provides significant suggestions for policymakers. The study is relevant for investors, researchers and the academia. It contributes to market efficiency and information transmission literature for commodity markets.
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Hung, Ngo Thai. "Return and volatility spillover across equity markets between China and Southeast Asian countries." Journal of Economics, Finance and Administrative Science 24, no. 47 (April 29, 2019): 66–81. http://dx.doi.org/10.1108/jefas-10-2018-0106.

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Purpose This paper aims to study the daily returns and volatility spillover effects in common stock prices between China and four countries in Southeast Asia (Vietnam, Thailand, Singapore and Malaysia). Design/methodology/approach The analysis uses a vector autoregression with a bivariate GARCH-BEKK model to capture return linkage and volatility transmission spanning the period including the pre- and post-2008 Global Financial Crisis. Findings The main empirical result is that the volatility of the Chinese market has had a significant impact on the other markets in the data sample. For the stock return, linkage between China and other markets seems to be remarkable during and after the Global Financial Crisis. Notably, the findings also indicate that the stock markets are more substantially integrated into the crisis. Practical implications The results have considerable implications for portfolio managers and institutional investors in the evaluation of investment and asset allocation decisions. The market participants should pay more attention to assess the worth of across linkages among the markets and their volatility transmissions. Additionally, international portfolio managers and hedgers may be better able to understand how the volatility linkage between stock markets interrelated overtime; this situation might provide them benefit in forecasting the behavior of this market by capturing the other market information. Originality/value This paper would complement the emerging body of existing literature by examining how China stock market impacts on their neighboring countries including Vietnam, Thailand, Singapore and Malaysia. Furthermore, this is the first investigation capturing return linkage and volatility spill over between China market and the four Southeast Asian markets by using bivariate VAR-GARCH-BEKK model. The authors believe that the results of this research’s empirical analysis would amplify the systematic understanding of spillover activities between China stock market and other stock markets.
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Singh, Amanjot, and Parneet Kaur. "Stock Market Linkages: Evidence From The US, China And India During The Subprime Crisis." Timisoara Journal of Economics and Business 8, no. 1 (June 1, 2015): 137–62. http://dx.doi.org/10.1515/tjeb-2015-0012.

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AbstractThe Subprime crisis spillovered the returns and volatility from the US stock market to the other integrated economies. The present study attempts to analyze the stock market linkages between the US, India and China, especially during the US subprime Crisis. The technique of Tri-Variate Vector Autoregression and the Spillover Index has been employed so as to analyze the relations during the time period 2007 to 2009. To estimate the time varying risk parameters, the technique of Threshold Generalized Autoregressive Conditional Heteroskedastic [TGARCH (1,1)] model has been used. A uni-directional causality has been observed from the US market to the Indian and Chinese market, whereas another unidirectional causality has also been spotted running from the Chinese market to the Indian market in the context of stock market returns during the crisis period. A unidirectional volatility spillover from the US to the Indian market and from the Indian to the Chinese market has been found to be significant. As per the volatility Spillover Index, the cross market impact on the volatility reduces over a time period 2007-2009, due to the increased impact of the past volatility and the presence of 'leverage effect'. The falling returns added to the volatility in the respective markets. The efficient tests of causality inspired by Hill (2007) reported an indirect impact of the US market volatility on the Chinese market via Indian. The portfolio managers should discount this information well ahead of time to maintain the portfolio values by taking positions in futures and options market.
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Tsai, Pei-Jung, Peggy E. Swanson, and Salil K. Sarkar. "Mean and volatility linkages for closed-end country funds." Quarterly Review of Economics and Finance 47, no. 4 (September 2007): 550–75. http://dx.doi.org/10.1016/j.qref.2006.12.001.

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22

Pinho, Carlos, and Isabel Maldonado. "Commodity and Equity Markets: Volatility and Return Spillovers." Commodities 1, no. 1 (July 19, 2022): 18–33. http://dx.doi.org/10.3390/commodities1010003.

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The present paper provides an empirical analysis of the relationship between shocks to commodity markets and stock markets. By employing a total volatility connectedness measure, we study the relationship between shocks to oil, gold, copper, and agricultural commodity markets and emerging and developed stock markets. We conduct a connectivity analysis in the time and frequency domain to quantify market linkages using volatility spillovers over the period from 2004 to 2021. In addition, we analyze the spillovers of returns in these markets over the same period. The results suggest that both on volatility and returns spillovers, slightly more than 35% of the total variance of forecast errors is explained by shocks to markets during the period January 2004 to June 2021. We also show that, in terms of both volatility and returns, the contribution of equity market shocks to other markets is substantially more important than that of commodities; however, our analysis reveals that the total link between market returns is larger in the short run than in the long run, while in the case of volatility, the long-run frequencies concentrate the market link. Additionally, we use dynamic analysis to assess both the time evolution of total connectivity and all directional partial connectivity between markets. Our results show that both volatility and return linkages change significantly over time and that a set of events has a significant impact on them.
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23

Perumandla, Swamy, and Padma Kurisetti. "Commodity Transaction Tax (CTT)." International Journal of Asian Business and Information Management 12, no. 2 (April 2021): 16–36. http://dx.doi.org/10.4018/ijabim.20210401.oa2.

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This study aims to examine the time-varying correlations and volatility linkages between commodity and equity markets before and after the implementation of the commodity transaction tax (CTT) in India in 2013. The study utilizes symmetric and asymmetric DCC-EGARCH model to estimate correlation dynamics. Evidence suggests that the volatility and dynamic correlation linkages between commodities and equity markets are significantly affected by the triggering events. The time-varying correlations of Comdex-Nifty 50 show an unintended steep decline in the post-CTT period. It is an indication of a “flight to quality” phenomenon, where investors move capital from non-agricultural commodity futures to other cross markets and international markets. However, DCC of Comdex-Dhaanya pair is highly volatile in the post-CTT period and also noticed an increased correlation and volatility between the Dhaanya-Nifty 50 pair. Moreover, the correlation dynamics reveal a certain degree of interdependence between the cross markets, which are lower especially during the triggering episodes.
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Lee, Hsiu-Chuan, Chih-Hsiang Hsu, and Cheng-Yi Chien. "Spillovers of international interest rate swap markets and stock market volatility." Managerial Finance 42, no. 10 (October 10, 2016): 943–62. http://dx.doi.org/10.1108/mf-08-2015-0221.

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Purpose The purpose of this paper is to investigate volatility spillovers across the interest rate swap markets of the G7 economies, and then the authors investigate whether spillovers of swap markets contain useful information to explain subsequent stock price movements. Design/methodology/approach This study uses the short- and long-term swap spread volatility of the G7 countries to explore the spillover effects of international swap markets, and then investigates the relationship between swap and stock markets. The authors use the generalized VAR approach suggested by Diebold and Yilmaz (2012) to study spillovers of international swap markets. The Granger-causality tests are employed to examine the linkage of interest rate swap and stock markets. Findings This paper shows that a moderate spillover effect exists for the short- and long-term swap markets. Moreover, the results show that the short- and long-term swap markets of France and Germany have a larger impact on other countries’ swap markets than that of other countries’ swap markets on the French and German swap markets. Finally, the results indicate that the total volatility spillovers for the long-term swap markets have a larger influence on the total volatility spillover index of stock markets and the global stock market volatility than that of the short-term swap markets. Originality/value Prior literature has used impulse response and variance decomposition analyses to investigate international swap markets linkages. However, the results depend on the ordering of variables. This study uses the framework of Diebold and Yilmaz (2012) to overcome the ordering issue, and thus the authors can compute directional spillovers. This paper is the first study to explore the linkage of the total volatility spillover of swap markets and the stock markets.
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Hiang Liow, Kim. "The dynamics of return co-movements and volatility spillover effects in Greater China public property markets and international linkages." Journal of Property Investment & Finance 32, no. 6 (August 26, 2014): 610–41. http://dx.doi.org/10.1108/jpif-06-2014-0039.

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Purpose – The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013. Design/methodology/approach – First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles. Findings – Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks. Research limitations/implications – Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets. Originality/value – This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.
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(Pal), Suparna Nandy, and Arup Kr Chattopadhyay. "‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects." Journal of Emerging Market Finance 18, no. 2_suppl (June 21, 2019): S183—S212. http://dx.doi.org/10.1177/0972652719846321.

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The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multi­variate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17
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Lai, Wing-Choong, and Kim-Leng Goh. "Dependence Structure Between Renminbi Movements and Volatility of Foreign Exchange Rate Returns." China Report 57, no. 1 (February 2021): 57–78. http://dx.doi.org/10.1177/0009445520984737.

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This article investigates the linkages of the movements in Renminbi (RMB) to volatility of exchange rate returns of other currencies before and after the yuan devaluation on 11 August 2015. A comparison between the onshore Chinese yuan (CNY) and the offshore Chinese yuan (CNH) is made. Standard regression methods underestimate the tail dependence between yuan and other exchange rate volatility, as financial data are non-normally distributed, especially when extreme event occurs. We apply Gumbel copulas to capture the presence of tail dependence between RMB returns and the volatility of exchange rate returns for 13 selected currencies, and found dependencies not revealed by the standard ARCH models. The tail dependence has increased after the RMB devaluation, suggesting that RMB depreciation is associated with higher downside risks in these currencies. This is most obvious in the currencies of Asian and ASEAN-5 countries that have strong trade and financial linkages with China. The dependence structure has shifted away from the dominance of onshore CNY rates before the devaluation to the growing importance of more volatile offshore CNH rates after the devaluation. Hence, any large depreciation in CNH will lead to a higher volatility in the other exchange rate returns, and the corresponding downside currency risks are higher than those of the CNY.
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Liow, Kim Hiang, and Qing Ye. "Switching volatility and cross-market linkages in public property markets." Journal of Property Research 31, no. 4 (January 6, 2014): 287–314. http://dx.doi.org/10.1080/09599916.2013.870921.

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Nazlioglu, Saban, Rangan Gupta, Alper Gormus, and Ugur Soytas. "Price and volatility linkages between international REITs and oil markets." Energy Economics 88 (May 2020): 104779. http://dx.doi.org/10.1016/j.eneco.2020.104779.

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30

Laopodis, Nikiforos T. "Volatility linkages among interest rates: implications for global monetary policy." International Journal of Finance & Economics 7, no. 3 (2002): 215–33. http://dx.doi.org/10.1002/ijfe.190.

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31

Bouri, Elie, Brian Lucey, and David Roubaud. "The volatility surprise of leading cryptocurrencies: Transitory and permanent linkages." Finance Research Letters 33 (March 2020): 101188. http://dx.doi.org/10.1016/j.frl.2019.05.006.

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32

Panda, Ajaya Kumar, and Swagatika Nanda. "A GARCH Modelling of Volatility and M-GARCH Approach of Stock Market Linkages of North America." Global Business Review 19, no. 6 (September 5, 2018): 1538–53. http://dx.doi.org/10.1177/0972150918793554.

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The present study attempts to capture the return volatility and the extent of dynamic conditional correlation between the stock markets of North America region. The data contain weekly stock market returns spanning from the second week of 1995 to the fourth week of June 2016. Using univariate ARCH and GARCH approaches, the study finds evidence of return volatility and its persistence within the region. Mexican stock market neither reacts intensely to immediate market fluctuations nor the part of the realized past volatility spill over to the current period, whereas the stock markets of Canada and USA experience high persistence of return volatility and Bermuda stock market returns are highly sensitive to the immediate market fluctuations. Using MGARCH-DCC, this article finds that emerging markets are less linked to the developed market in terms of return and that there also exists a weak co-movement between the stock markets. There is no evidence of market integration throughout the sample period. Correlations tend to spread out equally throughout the sample period, but the co-variances were found to be more volatile during 2008–2010. This article reveals that changes in co-movement are not due to a change in the correlations between markets but is simply due to volatility.
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33

Darrat, Ali F., Grant Colthup, Bin Li, and Maosen Zhong. "Market Interdependence In The Pacific Basin Region: Internal Drives And External Influences." Journal of Applied Business Research (JABR) 28, no. 4 (June 27, 2012): 619. http://dx.doi.org/10.19030/jabr.v28i4.7045.

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<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; text-indent: 0in; mso-pagination: none;" class="MsoBodyTextIndent"><span style="font-family: Times New Roman; font-size: x-small;">This paper examines equity market linkages in the Pacific Basin (PB) region and their relations to other mature markets and also assesses the response of these markets to major global events. Results from weekly data for market pairs and for the region as a whole consistently suggest that markets in the PB region are internally interdependent and exhibit significant external relations mainly with the US (rather than Japan). The presence of potent market linkages seems inconsistent with market efficiency provided that implied trading rules yield risk-adjusted excess returns. However, the results further indicate that PB market linkages, both internally within the region and externally with the US, have endured considerable weaknesses particularly since the September 11, 2001 terrorist attack. Such recent weakening of equity market linkages may have strengthened diversification benefits available to US investors from investing in the PB region. We also obtain evidence indicating that three main factors significantly explain the differing degrees of market linkages across countries in the PB region; namely, exchange-rate volatility, equity market volatility and money-market interlink.</span></p><span style="font-family: Times New Roman; font-size: small;"> </span>
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34

Darinda, Dwika, and Fikri C. Permana. "Volatility Spillover Effects In Asean-5 Stock Market: Does The Different Oil Price Era Change The Pattern?" Kajian Ekonomi dan Keuangan 3, no. 2 (August 31, 2019): 116–34. http://dx.doi.org/10.31685/kek.v3i2.484.

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The aim of this study is to identify the pattern of volatility transmission in ASEAN-5 (Indonesia, Malaysia, Thailand, Singapore and the Philippines) stock market by examine Global Macro Shocks (proxyed by Brent oil price); Cross-Market Linkages (proxied by Dow Jones Index); and Economic Fundamental (proxied by exchange rate) as the sources of volatility. This paper utilizing VAR and asymmetric GARCH (1,1)-BEKK model using the daily data between 4 January 2012 and 30 June 2017. The result shows that all independent variables have a significant volatility transmission to every ASEAN-5 stock market. Then in order to capture the different volatility transmission pattern, we divided the data into two periods which are “high-oil price” era and “low-oil price” era. Besides the different rate of volatility, we also find a different pattern of volatility transmission at Malaysia stock market (KLCI); Thailand stock market (SETI); and at Philippines stock market (PSEI) between these two eras.
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35

Kumar, Muneesh, Tarunika Jain Agrawal, and Srishti Sehgal. "Domestic and International Information Linkages for Indian Commodities Market in the Pre- and Post-CTT Periods." Metamorphosis: A Journal of Management Research 16, no. 2 (November 15, 2017): 75–91. http://dx.doi.org/10.1177/0972622517737869.

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This article investigates the impact of commodity transaction tax, in effect from 1 July 2013, on the information linkages for the Indian commodity market. We use daily data on five sample commodities—gold, aluminium, copper, zinc, and crude oil from 1 May 2010 to 31 August 2016. MCX has been used as a reference commodity exchange for India, while we use COMEX and DGCX for gold, LME and SHFE for base metals, and NYMEX and ICE for crude oil for international comparison. Price discovery has been evaluated using static and dynamic cointegration procedures, while volatility spillover has been evaluated based on BEKK-GARCH and Diebold Yilmaz models. We find that CTT imposition has weakened the price discovery and volatility spillover process, thus reducing the price and hedging efficiency of the Indian commodities market. For gold and crude oil, the information linkages have been severely hampered, owing to their international character. For base metals, MCX takes greater time for information transmission. International information linkages seem to have been more adversely impacted, owing to lower cost competitiveness of Indian commodities market. The findings of the study are pertinent for the policymakers, commodity exchanges, and other stakeholders.
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Stolbov, Mikhail. "How are interbank and sovereign debt markets linked? Evidence from 14 OECD countries, the Euro area and Russia." Panoeconomicus 61, no. 3 (2014): 331–48. http://dx.doi.org/10.2298/pan1403331s.

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The paper explores causal linkages between interbank and sovereign bond markets in 14 OECD countries, the Euro area and Russia during the 2008-2009 crisis and post-crisis period. The analysis has been carried out for individual countries and in a multivariate framework. It enables to identify systemically important countries in both markets. The USA, Switzerland, Australia, South Korea and Russia are of particular significance in the interbank lending market. Switzerland, the UK, Poland, Australia and Canada play a pivotal role in the public debt market. The analysis under the multivariate framework reveals substantial heterogeneity in the network structure of both markets. Only 12% of causal relationships coincide, which may fuel financial contagion. Volatility spillovers underlie the causal linkages. They are estimated by means of dynamic volatility indices based on rolling correlation matrices and help identify the transformation of the international banking turmoil into the sovereign debt crisis.
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Zaher, Heba F., and László Buics. "The impact of financial globalisation on stock market volatility in European Union countries." Hungarian Statistical Review 5, no. 1 (2022): 109–22. http://dx.doi.org/10.35618/hsr2022.01.en109.

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This study examines the impact of financial globalisation on stock market volatility in 24 countries, based on yearly observations from 1993 to 2019. Previous research shows that growing global financial linkages are decreasing countries’ stock market volatility. The financial globalisation composite index consists of two indices. The first is the de facto index, which measures the actual activities and flows between a country and other parts of the world, and the second is the de jure index, which expresses the conditions and policies that enable these activities and flows. According to the authors’ results, the de facto index has no significant effect on countries’ stock market volatility, while there is a significant negative relationship between the de jure index and stock market volatility, underlining the importance of policies and conditions conducive to financial globalisation.
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38

Meric, Ilhan, Joe Kim, Lewis Coopersmith, and Gulser Meric. "Co-Movements of Pacific-Basin Stock Markets: Portfolio Diversification Implications." Journal of International Business and Economy 8, no. 2 (December 1, 2007): 11–34. http://dx.doi.org/10.51240/jibe.2007.2.2.

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This paper studies the co-movements of and the linkages between twelve Pacific-Basin stock markets during the June 1995-May 2005 period. We use the principal components analysis (PCA) technique to group the stock markets into statistically significant principal components in terms of the similarities of their index return movements. The rolling correlation analysis results show that correlation between the Pacific-Basin stock markets has considerable time-varying volatility. The Granger causality test results indicate that the weekly index returns of most Pacific-Basin stock markets are weak-form efficient and that most Pacific-Basin stock markets have significant lead/lag linkages. The study investigates the portfolio diversification implications of the linkages between the Pacific-Basin stock markets.
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Madhavan, Vinodh, and Partha Ray. "Price and Volatility Linkages Between Indian Stocks and Their European GDRs." Journal of Emerging Market Finance 18, no. 2_suppl (June 21, 2019): S213—S237. http://dx.doi.org/10.1177/0972652719846353.

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This article tests for price and volatility linkages between Indian global depositary receipts (GDRs) traded in Luxembourg/London and their underlying shares traded in Mumbai. The relationship is studied between the GDR price and the domestic share price along with the appropriate exchange rates, the foreign stock index and the domestic stock index using the vector autoregression (VAR) and dynamic conditional correlation (DCC) specification of multivariate generalised autoregressive conditional heteroscedasticity (GARCH) models. VAR results indicate a similarity between the two prices of scrips: one trading in Mumbai and the other trading in Luxembourg (London). Further, DCC-GARCH model outcomes point to, by and large, a high-dynamic correlation between Indian GDRs traded in Luxembourg/London and their underlying stocks listed in Mumbai. Thus, the price and volatility linkages between the Indian stock and its European counterpart are invariant with respect to the choice of the foreign stock exchange. Such a similarity in findings, notwithstanding the difference in degree of information disclosure as well as listing requirements at London and Luxembourg, is perhaps indicative of the stock-exchange-invariant nature of law of one price. JEL Classification: G15, C22
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40

Sosa, Miriam, and Edgar Ortiz. "International Financial US Linkages: Networks Theory and MS-VAR Analyses." Revista Mexicana de Economía y Finanzas 14, PNEA (August 1, 2019): 459–84. http://dx.doi.org/10.21919/remef.v14i0.418.

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This paper aims to examine the impact of the Global Financial Crisis on portfolio investment flows, as well as on stock market activity. Network Theory is used to analyze structural changes of foreign portfolio investment flows (FPI) to a sample of 13 developed countries and 6 emerging Latin American countries. Additionally, using daily data from 2003 to 2015, the dynamics of returns are analyzed to test whether the US market influenced these markets or vice versa; univariate (MS-AR) and multivariate (MS-VAR) regime-switching models are used. The evidence confirms the presence of two different regimes, low volatility and a high volatility for all markets. Findings suggest strengthening local productive and financial institutions in order to anchor FPI. The MS-(V)AR study is limited to stock markets from the Americas and Europe. Previous literature has not applied the innovative and complementary methodologies employed here to analyze financial crisis impacts on FPI flows. We conclude that US financial markets keep a close financial relationship with the most important European and American countries’ stock markets, both by receiving and delivering FPI, and in addition influencing the behavior of stock indexes.
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41

Dania, Akash, and Sandip Dutta. "Examining the Dynamic Linkages of Performance and Volatility of REIT Returns." Journal of Wealth Management 19, no. 4 (January 31, 2017): 104–14. http://dx.doi.org/10.3905/jwm.2017.19.4.104.

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42

Ho, Kin-Yip, Lin Zheng, and Zhaoyong Zhang. "Volume, volatility and information linkages in the stock and option markets." Review of Financial Economics 21, no. 4 (November 2012): 168–74. http://dx.doi.org/10.1016/j.rfe.2012.06.001.

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43

Cifarelli, Giulio, and Giovanna Paladino. "Volatility linkages across three major equity markets: A financial arbitrage approach." Journal of International Money and Finance 24, no. 3 (April 2005): 413–39. http://dx.doi.org/10.1016/j.jimonfin.2005.01.005.

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44

Laopodis, Nikiforos T. "Monetary policy implications of volatility linkages among long-term interest rates." Journal of Economics and Finance 24, no. 2 (June 2000): 160–77. http://dx.doi.org/10.1007/bf02752710.

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45

Badshah, Ihsan, Stelios Bekiros, Brian M. Lucey, and Gazi Salah Uddin. "Asymmetric linkages among the fear index and emerging market volatility indices." Emerging Markets Review 37 (December 2018): 17–31. http://dx.doi.org/10.1016/j.ememar.2018.03.002.

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46

Cheng, Siwei, Kyriaki Kosidou, Bo Burström, Charlotte Björkenstam, Anne R. Pebley, and Emma Björkenstam. "Precarious Childhoods: Childhood Family Income Volatility and Mental Health in Early Adulthood." Social Forces 99, no. 2 (March 20, 2020): 672–99. http://dx.doi.org/10.1093/sf/soaa020.

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Abstract The rise of income volatility in western countries has been extensively documented in the literature, but empirical research has just started to examine how childhood exposure to family income volatility affects subsequent wellbeing. This study takes advantage of several nation-wide, population registers from Sweden with linkages within and across generations to examine the intergenerational impact of childhood family income volatility on psychiatric disorders in early adulthood. In addition to the population-average effects, we also examine the heterogeneity in the impact of family income volatility for families at the top, bottom, and middle of the family income distribution. Our results suggest that after controlling for a set of family- and child-level characteristics, childhood family income volatility has a negative effect on mental wellbeing, and this finding is consistent across a range of psychiatric outcomes. Furthermore, we show that while children from low-income families exhibit the greatest likelihood of psychiatric disorder, children from families in the middle of the income distribution experience the greatest negative impact of income volatility.
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Bhowmik, Roni, Gouranga Chandra Debnath, Nitai Chandra Debnath, and Shouyang Wang. "Emerging stock market reactions to shocks during various crisis periods." PLOS ONE 17, no. 9 (September 13, 2022): e0272450. http://dx.doi.org/10.1371/journal.pone.0272450.

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This study investigates granger causal linkages among six Asian emerging stock markets and the US market over the period 2002–2020, taking into account several crisis periods. The pairwise Granger causality tests for investigating the short-run causality show significant bi- and uni-directional causal relationships in those markets and evidence that they have become more internationally integrated after every crisis period. An exception is Bangladesh with almost no significant short-term causal linkages with other markets. For understanding, how the financial linkages amplify volatility spillover effects, we apply the GARCH-M model and find that volatility and return spillovers act very inversely over time. However, market interface is weak before the crisis periods and becomes very strong during the financial crisis and US-China economic policy uncertainty periods. The US market plays a dominant role during the financial crisis and COVID-19 periods. Further analysis using the VAR model shows that a large proportion of the forecast variance of the Asian emerging stock markets is affected by the S&P 500 and that market shock starts to rise notably from the 1 to 10 period. The overall findings could provide important policy implications in the six countries under study regarding hedging, trading strategies, and financial market regulation.
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Lahiani, Amine, Duc Khuong Nguyen, and Thierry Vo. "Understanding Return And Volatility Spillovers Among Major Agricultural Commodities." Journal of Applied Business Research (JABR) 29, no. 6 (October 29, 2013): 1781. http://dx.doi.org/10.19030/jabr.v29i6.8214.

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<p>We provide comprehensive evidence of return and volatility spillovers for the four major agricultural commodities including sugar, wheat, corn and cotton over the recent period 2003-2010. Our results from the recent VAR-GARCH model of Ling and McAleer (2003) that allows for simultaneous shock transmissions of conditional volatilities of returns across commodities show the existence of substantial volatility spillover linkages between agricultural commodity returns and volatilities. Our findings are also particularly insightful for optimal portfolio designs and risk management through the computation of optimal weights and hedge ratios.</p>
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Rashid, Abdul, and Fazal Husain. "Capital Inflows, Inflation, and the Exchange Rate Volatility: An Investigation for Linear and Nonlinear Causal Linkages." Pakistan Development Review 52, no. 3 (September 1, 2013): 183–206. http://dx.doi.org/10.30541/v52i3pp.183-206.

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This paper empirically examines the effect of foreign capital inflows on domestic price levels, monetary expansion, and the exchange rate volatility for Pakistan using linear and nonlinear causality tests. The key message emerging from the analysis is that there is a significant inflationary impact of capital inflows, in particular during the period of surges in capital inflows. Specifically, we find evidence of a significant nonlinear Granger causality running from capital inflows to the change in domestic prices. We also show that domestic prices are nonlinearly caused (in Granger sense) by the growth of domestic debt and money supply-to-GDP ratio. Our results, however, suggest that the market interest rate and the nominal exchange rate do not have significant relationships with domestic prices. The findings suggest that there is a need to manage the capital inflows in such a way that they should neither create an inflationary pressure in the economy nor fuel the exchange rate volatility. JEL Classification: C22, C32, F21, F31, F32 Keywords: Capital Inflows, Inflationary Pressures, the Exchange Rate Volatility, Monetary Expansion, Nonlinear Dynamics
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Montasser, Ghassen El, Rangan Gupta, Jooste Charl, and Stephen M. Miller. "The Time-series Linkages between US Fiscal Policy and Asset Prices." Public Finance Review 48, no. 3 (April 9, 2020): 303–39. http://dx.doi.org/10.1177/1091142120916032.

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This article studies the interplay of fiscal policy and asset price returns of the United States in a time-varying parameter vector autoregressive (VAR) model. Using annual data from 1890 to 2013, we study the effects of dynamic shocks to both fiscal policy on asset returns and asset returns on fiscal policy. Distinguishing between low-volatility (bull market) and high-volatility (bear market) regimes together with a time-varying parameter VAR model enables us to isolate the different sizes and signs of responses to shocks during different time periods. The results indicate that increases in the primary surplus-to-gross-domestic-product ratio decrease house returns over the entire sample and at each impulse horizon. Unlike the house return responses, stock returns only decrease in the first year after the fiscal shock but then increase for the following eight years. Furthermore, the findings show that asset return movements affect fiscal policy, whereby fiscal policy responds more to equity returns than to house returns. The response of fiscal policy to asset returns proves relatively stable and constant over time while controlling for various asset return regimes. Asset returns respond uniformly to fiscal policy shocks since the 1900s.
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