Correlation of financial markets in times of crisis

Sandoval, Leonidas; Franca, Italo De Paula

doi:10.1016/j.physa.2011.07.023

Cited by 219 publications

(123 citation statements)

References 128 publications

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“…Forbes and Rigobon 13 have shown that a high level of dependence is visible between most markets, and that changes in correlation are coupled to volatility changes. A more multidisciplinary analysis of the correlation between the indices of different markets can be found for example in the recent study of Song et al 14 , and of Sandoval et al 15 .…”

Section: Introductionmentioning

confidence: 99%

Correlations and Dependencies in the Global Financial Village

Kenett

Raddant

Zatlavi

et al. 2012

Int. J. Mod. Phys. Conf. Ser.

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The high degree of coupling between global financial markets has made the financial village prone to systemic collapses. Here we present a new methodology to assess and quantify inter-market relations. The approach is based on meta-correlations (correlations between the intra-market correlations), and a Dependency Network analysis approach. We investigated the relations between six important world markets -U.S., U.K., Germany, Japan, China and India from January 2000 until December 2010. Our findings show that while the developed Western markets (U.S., U.K., Germany), are highly correlated, the inter-dependencies between these markets and the Eastern markets (India and China) are very volatile and with noticeable maxima at times of global world events. Finally, using the Dependency network approach, we quantify the flow of information between the different markets, and how markets affect each other. We observe that German and U.K. stocks show a large amount of coupling, while other markets are more segmented. These and additional reported findings illustrate that this methodological framework provides a way to quantify interdependencies in the global market and their

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Section: Introductionmentioning

confidence: 99%

Correlations and Dependencies in the Global Financial Village

Kenett

Raddant

Zatlavi

et al. 2012

Int. J. Mod. Phys. Conf. Ser.

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“…Using stock market data from Coletti and Murgia (2015), which starts in 1973, we can also study the network's topology during past decades' crises like Sandoval and Franca (2012) and Sandoval (2012b) to identify common stock market's patterns across different economic cycles. Moreover, since MST tends to hide some important relations and does not display cliques while the full network is difficult to visualize, we could analyze the network using other alternative methods, such as planar graph PMFG (Coronnello et al, 2005;Tumminello et al, 2005) or MST with cliques (Onnela et al, 2003a).…”

Section: Discussionmentioning

confidence: 99%

The network of the Italian stock market during the 2008–2011 financial crises

Coletti

Murgia

2017

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Abstract.We build the network of the top 190 Italian quoted companies during the two financial crises of 2008-2009 (US credit crisis) and 2010-2011 (European sovereign debt crisis) and compare its structure to the pre-crises years, using both minimum spanning trees and the full network with thresholds. We also analyze the centrality and compactness of industry sectors. We find a general contraction of the network during the crises, both numerically due to stronger correlation as well as topologically, with the appearance of central dominant companies which attract the other ones into a very large cluster, dominated by financial institutions (commercial banks and insurance companies). In particular, we note the role of insurance behemoth Assicurazioni Generali, which rises from a pre-crises subordinate role to become the central company in the minimum spanning tree after the crises period. The few sectors which maintain compactness before and during the crises are utilities, publishing, and construction.

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“…Sharp growth of volatility signals about increase of the panic moods in the stock market and allows to detect crisis phases (Crato & Ray, 2000;Kazemi, 2013). The growth of correlations between various assets in the stock market signals about the emergence of crisis phenomena in financial and economic systems (Sandoval & Franca, 2012), the same is true for the market persistence (Caporale et al, 2016).…”

Section: Introductionmentioning

confidence: 91%

Exploring frequency of price overreactions in the Ukrainian stock market

Plastun

Makarenko

Khomutenko

et al. 2018

Investment Management and Financial Innovations

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AUTHORS Alex AbstractThis paper explores the frequency of price overreactions in the Ukrainian stock market by focusing on the PFTS Index over the period 2006-2017 and UX index over the period 2008-2017, as well as some "blue chips" (BAVL, UNAF, MSICH, CEEN) for the period of 2013-2015. Using static approach to detect overreactions, a number of hypotheses are tested: the frequency of price overreactions is informative about crisis events in the economy (H1), can be used for price prediction purposes (H2), and exhibits seasonality (H3). To do this, various statistical tests (both parametric and non-parametric), including correlation analysis, augmented Dickey-Fuller tests (ADF), Granger causality tests, and regression analysis with dummy variables, are carried out. Hypotheses H1 and H2 are confirmed: frequency of price overreactions can be used as a crisis predictor (a sharp increase in the number of overreactions is associated with a crisis period) and could be used to predict stock returns. No seasonality in the overreactions frequency is found. Implications of this research include crisis prediction and stock market prices forecasting and can be used for designing trading strategies.

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Correlation of financial markets in times of crisis

Cited by 219 publications

References 128 publications

Correlations and Dependencies in the Global Financial Village

Correlations and Dependencies in the Global Financial Village

The network of the Italian stock market during the 2008–2011 financial crises

Exploring frequency of price overreactions in the Ukrainian stock market

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