Entropy Measures for Assessing Volatile Markets

Sheraz, Muhammad; Dedu, Silvia; Preda, Vasile

doi:10.1016/s2212-5671(15)00279-8

Cited by 32 publications

(23 citation statements)

References 14 publications

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“…An extension of the Minimum spanning tree (MST) method for the correlation network was later on presented by Tumminello et al [ 12 ], known as planar maximally filtered graph (PMFG), and Boginski et al [ 13 ] formed a correlation threshold (CT) network. The uncertainty of the stock market and the volatility in stock market returns can be measured with entropy-based approaches, as suggested by previous studies [ 14 , 15 , 16 , 17 , 18 , 19 ]. Most importantly, a complex system such as the stock market presents its structure better when it is under stress.…”

Section: Introductionmentioning

confidence: 99%

Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective

Memon

Yao

2019

Entropy

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We studied the cross-correlations in the daily closing prices of 181 stocks listed on the Pakistan stock exchange (PSX) covering a time period of 2007–2017 to compute the threshold networks and minimum spanning trees. In addition to the full sample analysis, our study uses three subsamples to examine the structural change and topological evolution before, during, and after the global financial crisis of 2008. We also apply Shannon entropy on the overall sample to measure the volatility of individual stocks. Our results find substantial clustering and a crisis-like less stable overall market structure, given the external and internal events of terrorism, political, financial, and economic crisis for Pakistan. The subsample results further reveal hierarchal scale-free structures and a reconfigured metastable market structure during a postcrisis period. In addition, time varying topological measures confirm the evidence of the presence of several star-like structures, the shrinkage of tree length due to crisis-related shocks, and an expansion in the recovery phase. Finally, changes of the central node of minimum spanning trees (MSTs), the volatile stock recognition using Shannon entropy, and the topology of threshold networks will help local and international investors of Pakistan Stock Exchange limited (PSX) to manage their portfolios or regulators to monitor the important nodes to achieve stability and to predict an upcoming crisis.

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Section: Introductionmentioning

confidence: 99%

Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective

Memon

Yao

2019

Entropy

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“…In 2015, Sati and Gupta [7] defined a generalized cumulative remaining entropy on the basis of the nonadditive Tsallis entropy. In the same year, Sheraz et al [8] employed an entropy for volatility markets.…”

Section: Introductionmentioning

confidence: 99%

The Impact of the SARS-CoV-2 Epidemic on World Indices: The Entropy Approach

Karakaş

Doğan

Çalik

2021

Mathematical Problems in Engineering

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The coronavirus disease (COVID-19) outbreak started in December 2019 in Wuhan. The virus has spread around the whole world, and it has caused a strong and serious pandemic. Symptoms such as cough, respiratory distress, diarrhea, and fatigue associated with COVID-19 are typical clinical findings. Coronavirus infection has become an important public health concern because of its increasing prevalence, serious complications, and mortality. In light of this information, we examine different entropy methods for world indices (ISE 30, FTSE 100, NIKKEI 225, SP 500, and DAX 30) in the pre-COVID-19 period (02.01.2019–17.11.2019) and the post-COVID-19 period (18.11.2019–23.11.2020) in this article. Besides, we discuss the performances of entropies such as Shannon, Renyi, Tsallis, and approximate entropy (ApEn) in detail and perform the notion of entropy for volatility measure. As a result, we present the numerical results for the data set.

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“…Sati and Gupta [20] described a generalized cumulative remaining entropy based on the non-additive Tsallis entropy. Sheraz [21] used entropy approach for volatility markets. Stosic et al [22] considered the effects of financial attacks on alien exchange (FX) markets, where entropy evolution was measured for varied exchange rates, using the time dependent complex entropy method.…”

Section: Introductionmentioning

confidence: 99%

Entropy approach for volatility of wind energy

Çalik

Metin

2019

THERM SCI

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Original scientific paper https://doi.org/10.2298/TSCI190101346CIn this study, we give the practice of entropy in wind energy. Firstly, we fit marginal distributions to each of the variables and later demonstrate the notion of entropy to perform a comparison the wind energy data of the stations (Bursa, Elazig, Istanbul, Mugla, Rize, Tokat, Van, and Zonguldak) that have been examined in a period 2015-2018. The results of probability distribution fitting to these wind energy variables show that the wind energy time series of Bursa, Elazıg, Istanbul, Mugla, Rize, Tokat, Van, and Zonguldak are best resubmitted by Gamma Burr and Lognormal distributions. Later, we calculate Shannon entropy for several various values, Tsallis entropy, Renyi entropy, and the approximate entropy. We form calculation outcomes with these entropies for daily data.

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Entropy Measures for Assessing Volatile Markets

Cited by 32 publications

References 14 publications

Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective

Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective

The Impact of the SARS-CoV-2 Epidemic on World Indices: The Entropy Approach

Entropy approach for volatility of wind energy

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