Financial Sector Volatility Connectedness and Equity Returns

Demirer, Mert; Gokcen, Umut; Yılmaz, Kamil

doi:10.2139/ssrn.3099738

Cited by 10 publications

(5 citation statements)

References 57 publications

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“…Since the VAR methodology is widely known, we only briefly described the basic terms in the VAR model in order to define the spillover index. Details can be found in Diebold and Yilmaz (2009, 2011, 2012, Koop et al (1996), Pesaran and Shin (1998), Urbina (2013), Lütkepohl (1993Lütkepohl ( , 2006Lütkepohl ( , 2010 and Demirer et al (2018). We observed a stabile VAR(p) model for N variables in a compact form Y t = a + AY t−1 + ε t , where Y t is the vector containing vectors y of variables of the system, a…”

Section: Methodology Descriptionmentioning

confidence: 84%

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Škrinjarić¹

2019

IJFS

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This research observes a time varying relationship between stock returns, volatilities and the online search volume in regard to selected CESEE (Central, Eastern and South-Eastern European) stock markets. The main hypothesis of the research assumes that a feedback relationship exists between stock returns, volatilities and the investor’s attention variable (captured by the online search volume). Moreover, the relationship is assumed to be time varying due to changing market conditions. Previous research does not deal with the time-varying multi-directional relationship. Thus, the contribution to existing research consists of estimating the aforementioned relationship between return, volatility and the search volume series for selected CESEE countries by using a novel approach of spillover indices within the VAR (Vector AutoRegression) model framework. The results indicate that the Google search volume affects the risk series more than the return series on the selected markets.

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Section: Methodology Descriptionmentioning

confidence: 84%

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Škrinjarić¹

2019

IJFS

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“…The description of the methodology is based on Diebold and Yilmaz (2009, 2011), Urbina (2013), Lütkepohl (1993, 2006, 2010), Demirer et al (2019). For the purpose of estimating spillover indices, a stabile VAR model of order p with N variables is estimated, in a compact form: where Y t is a vector of all vectors y of every variable in the system, a = [ v 0 … 0]′, e t =true[boldεt0⋯0true]′ and A = true[bold-italicA1bold-italicA2⋯bold-italicAp−1bold-italicApIN0⋯0 00IN⋮ ⋮⋮00⋱⋯⋮IN⋮...…”

Section: Methodology Descriptionmentioning

confidence: 99%

Empirical analysis of dynamic spillovers between exchange rate return, return volatility and investor sentiment

Škrinjarić

Golubić

Orlović

2020

SEF

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Purpose This paper aims to analyze the effects of investors’ sentiment, return and risk series on one to another of selected exchange rates. The empirical analysis consists of a time-varying inter-dependence between the observed variables, with the focus on spillovers between the variables. Design/methodology/approach Monthly data on the index Sentix, exchange rates EUR–USD, EUR–CHF and EUR–JPY are analyzed from February 2003 to December 2019. The applied methodology consists of vector autoregression models (VAR) with Diebold and Yilmaz (2009, 2011) spillover indices. Findings The results of the empirical research indicate that using static analysis could result in misleading conclusions, with dynamic analysis indicating that the financial of 2007-2008 and specific negative events increase the spillovers of shock between the observed variables for all three exchange rates. The sources of shocks in the model change over time because of variables changing their positions being net emitters and net receivers of shocks. Research limitations/implications The shortfalls of this study include using the monthly data frequency, as this was available for the authors, namely, investors are interested to obtain new information on a weekly and daily basis, not only monthly. However, at the time of writing this research, we could obtain only monthly data. Practical implications As the obtained results are in line with previous literature and were found to be robust, there exists the potential to use such analysis in the future when forecasting risk and return series for portfolio management purposes. Thus, a basic comparison was made regarding the investment strategies, which were based on the results from the estimation. It was shown that using information about shock spillovers could result in strategies that can obtain better portfolio value over time compared to basic benchmark strategies. Originality/value First, this paper allows for the spillovers of shocks in variables within the VAR models in all directions. Second, a dynamic analysis is included in the study. Third, the mentioned spillover indices are included in the study as well.

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“…Since the unfolding of the recent Russian ruble crisis leading to the dampening of global exports, investigations into the dynamic contemporaneous relationships between different markets have flourished (Demirer et al, 2018a;Capponi, 2016;Diebold et al, 2017;Diebold and Yilmaz, 2015;Diebold and Yılmaz, 2014;Yilmaz et al, 2018;Demirer et al, 2018c;Liu et al, 2017;Malik and Xu, 2017;Vergote, 2016;Badshah, 2018;Liow, 2015;Andrada-Félix et al, 2018;Ghulam and Doering, 2017;Chiang et al, 2017;Badshah, 2018). We complement these studies by investigating the dynamics in a multi-cluster representation.…”

Section: Dynamic Analysismentioning

confidence: 96%

Crisis transmission: Visualizing vulnerability

Dungey

Islam

Volkov

2020

Pacific-Basin Finance Journal

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This paper develops a means of visualizing the vulnerability of complex systems of financial interactions around the globe using Neural Network clustering techniques. We show how time-varying spillover indices can be translated into two dimensional crisis maps. The crisis maps have the advantage of showing the changing paths of vulnerability, including the direction and extent of the effect between source and affected markets. Using equity market data for 31 global markets over 1998-2017 we provide these crisis maps. These tools help portfolio managers and policy makers to distinguish which of the available tools for crisis management will be most appropriate for the form of vulnerability in play.

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Financial Sector Volatility Connectedness and Equity Returns

Cited by 10 publications

References 57 publications

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Empirical analysis of dynamic spillovers between exchange rate return, return volatility and investor sentiment

Crisis transmission: Visualizing vulnerability

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