Modelling Stock Market Crashes: The Case of Bucharest Stock Exchange

Pele, Daniel Traian; Mazurencu-Marinescu, Miruna

doi:10.1016/j.sbspro.2012.09.1030

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…For centuries, the world had witnessed numerous endogenous crashes, some more devastating than others. Among the most severe and well-documented crashes include the seventeenth century "Tulip Mania," the eighteenth century "South Sea Bubble," the "Great Depression" in the 1930s, the "Black Monday" in 1987, the "Dot Com Bubble" in the late 1990s and the "Subprime Financial Crisis" in the end of the 2000s (Malkiel, 2007;Pele & Mazurencu-Marinescu, 2012;Vines, 2009). Throughout the course of history, random events created shocks to the market, such as the 9/11 tragedy and the breakout of war (i.e., World War I).…”

Section: Complexity Of Financial Market and Stock Market Crashmentioning

confidence: 99%

The Evolution of Complex Systems Theory and the Advancement of Econophysics Methods in the Study of Stock Market Crashes

Joe¹,

Estrada²,

Fei³

2017

LBIBF

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This paper traces the origin and development of the complex systems theory over the course of history, up to its latest advancement in the study of stock market crashes. The trail of the theoryâ€™s fuzzy evolution is expansive that covers the ground of the complexity epistemology, natural science and computer science. A meticulous review is undertaken to distinguish the complex systems theory fromanother seemingly overlapping theory of the chaos systems. The paper recounts how researchers from cross-disciplines, particularly from the econophysics have banded together to consolidate and diffuse the application of the complex systems theory in the economics and further discusses the methodological contribution of the econophysics in the area of stock market. To date, the complex systems theory and the methodologies from the econophysics are wellestablished as the frontier for studies in stock market bubbles and crashes.

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Section: Complexity Of Financial Market and Stock Market Crashmentioning

confidence: 99%

The Evolution of Complex Systems Theory and the Advancement of Econophysics Methods in the Study of Stock Market Crashes

Joe¹,

Estrada²,

Fei³

2017

LBIBF

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“…Moreover, EMH is also hard to explain complex capital market instability such as leverage effect and herd effect [13], financial crises [14], market panics [15], and stock market crashes [16][17][18][19][20], etc. These examples illustrate the imperfections and inefficiencies of the capital market, highlighting its tendency towards imbalances and instability, particularly evident during stock market crashes when liquidity becomes scarce, as exemplified by the 1987 stock market crash [21] and the 2008 financial crisis [22].…”

Section: Introductionmentioning

confidence: 99%

The roles of liquidity and delay in financial markets based on an optimal forecasting model

Yang,

Ma,

Bian

et al. 2023

PLoS ONE

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We investigate the roles of liquidity and delay in financial markets through our proposed optimal forecasting model. The efficiency and liquidity of the financial market are examined using stochastic models that incorporate information delay. Based on machine learning, we estimate the in-sample and out-of-sample forecasting price performances of the six proposed methods using the likelihood function and Bayesian methods, and the out-of-sample prediction performance is compared with the benchmark model ARIMA-GARCH. We discover that the forecasting price performance of the proposed simplified delay stochastic model is superior to that of the benchmark methods by the test methods of a variety of loss function, superior predictive ability test (SPA), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Using data from the Chinese stock market, the best forecasting model assesses the efficiency and liquidity of the financial market while accounting for information delay and trade probability. The rise in trade probability and delay time affects the stability of the return distribution and raises the risk, according to stochastic simulation. The empirical findings show that empirical and best forecasting approaches are compatible, that company size and liquidity (delay time) have an inverse relationship, and that delay time and liquidity have a nonlinear relationship. The most efficient have optimal liquidity.

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“…Academic researchers have extensively discussed stock market crashes to identify a general framework to explain the bubble behavior of a stock price, to estimate the probable crash time, and to detect speculative stock market bubbles (Sornette, 2003;Johansen et al 2000;Vosvrda et al 2009;Pelea and Marinescu, 2012;and Friedman and Abraham, 2007). Why it is useful to identify, model, and analyze the stock market crashes are crucial questions for stock and risk managers.…”

Section: Introductionmentioning

confidence: 99%

Stock Returns and Roughness Extreme Variations: A New Model for Monitoring 2008 Market Crash and 2015 Flash Crash

Shirvani¹

2020

AEF

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We use Student’s t-copula to study the extreme variations in the bivariate kinematic time series of log–return and log–roughness of the S&P 500 index during two market crashes, the financial crisis in 2008 and the flash crash on Monday August 24, 2015. The stable and small values of the tail dependence index observed for some months preceding the market crash of 2008 indicate that the joint distribution of daily return and roughness was close to a normal one. The volatility of the tail and degree of freedom indices as determined by Student’s t-copula falls down substantially after the stock market crash of 2008. The number of degrees of freedom in the empirically observed distributions falls while the tail coefficient of the copula increases, indicating the long memory effect of the market crash of 2008. A significant change in the tail and degree of freedom indices associated with the intraday price of S&P 500 index is observed before, during, and after the flash crash on August 24, 2015. The long memory effect of the stock market flash crash of August 2015 is indicated by the number of degrees of freedom in the empirically observed distributions fall while the tail coefficient of the joint distribution increases after the flash crash. The small and stable value of degrees of freedom preceding the flash crash provides evidence that the joint distribution for intraday data of return and roughness is heavy-tailed. Time-varying long-range dependence in mean and volatility as well as the Chow and Bai-Perron tests indicate non-stability of the stock market in this period.

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Modelling Stock Market Crashes: The Case of Bucharest Stock Exchange

Cited by 7 publications

References 11 publications

The Evolution of Complex Systems Theory and the Advancement of Econophysics Methods in the Study of Stock Market Crashes

The Evolution of Complex Systems Theory and the Advancement of Econophysics Methods in the Study of Stock Market Crashes

The roles of liquidity and delay in financial markets based on an optimal forecasting model

Stock Returns and Roughness Extreme Variations: A New Model for Monitoring 2008 Market Crash and 2015 Flash Crash

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