Quantifying price fluctuations in the Brazilian stock market

a b s t r a c tWe show that the current economic crisis has led the market to exhibit a non-critical behavior. We do so by analyzing the quantitative parameters of time series from the main assets of the Brazilian Stock Market BOVESPA. By monitoring global persistence we show a deviation of power law behavior during the crisis in a strong analogy with spin systems (from where this concept was originally conceived). Such behavior is corroborated by an emergent heavy tail of absolute return distribution and also by the magnitude autocorrelation exponent. Comparisons with universal exponents obtained in the international stock markets are also performed. This suggests how a thorough analysis of suitable exponents can bring a possible way of forecasting market crises characterized by non-criticality.

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“…For BOVESPA, in Ref. [12] is found α = 3.72 for t = 5 min and α = 3.51 for t = 15 min, that corroborate our results.…”

Section: Universal Stylized Facts In Brazilian Stock Markets: Aggregasupporting

confidence: 90%

“…[12][13][14]), the estimation and computation of the exponents generated by supposed universal laws for these stocks have not been investigated extensively, when compared to the large number of existing results for the Nasdaq and SP&500. This suggests that further analyses are needed.…”

Section: Introductionmentioning

confidence: 99%

Stock markets and criticality in the current economic crisis

Silva¹,

Zembrzuski²,

Correa³

et al. 2010

Physica A: Statistical Mechanics and its Applications

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“…Further research showed that the returns have power-law tails with the exponent close to three, known as the inverse cubic law [12][13][14][15]. A number of empirical investigations have been conducted on financial returns at different time scales in different stock markets over different time periods, and the distributions are found to have power-law tails with different tail exponents at short time intervals [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], which can be explained by the variational theory for turbulent signals [32,33]. In addition, it has been shown that the exponent values are universal for three mature markets that do not display variations with respect to market capitalization or industrial sector [34,35], which is however not the case for emerging markets [36].…”

Section: Introductionmentioning

confidence: 99%

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Zhou¹

2012

New J. Phys.

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Common wisdom argues that, in general, large trades cause large price changes, whereas small trades cause small price changes. However, for extremely large price changes, the trade size and news play a minor role, while liquidity (especially price gaps on the limit order book) is a more influential factor. Hence, there might be other factors influencing the immediate price impacts of trades. In this paper, through mechanical analysis of price variations before and after a trade of arbitrary size, we identify that the trade size, the bid-ask spread, the price gaps and the outstanding volumes at the bid and ask sides of the limit order book have an impact on the changes in prices. We propose two regression models to investigate the influence of these microscopic factors on the price impact of buyer-initiated partially filled trades, seller-initiated partially filled trades, buyer-initiated filled trades and seller-initiated filled trades. We find that they have quantitatively similar explanatory powers and these factors can account for up to 44% of the price impacts. Large trade sizes, wide bid-ask spreads, high liquidity at the same side and low liquidity at the opposite side will cause a large price impact. We also find that the liquidity at the opposite side has a more influential impact than the liquidity at the same side. Our results shed new light on the determinants of immediate price impacts.

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“…The authors of the reference [23] present an introductory course to econophysics. Finally, among other works, reference [24] presents a quantification of fluctuations in the Brazilian stock market.Here we do not treat with stochastic processes and Poincaré returns, and we do not deal with the chance of people gain or lose money under certain a probability. Instead, we are interested in understanding the mechanisms that govern the stock market index returns from a deterministic point of view, using the return map of the stock returns.…”

Section: Introductionmentioning

confidence: 99%