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Academic literature on the topic 'Flashcrash'
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Journal articles on the topic "Flashcrash"
Bethel, E. Wes, David Leinweber, Oliver Rübel, and Kesheng Wu. "Federal Market Information Technology in the Post–FlashCrash Era:Roles for Supercomputing." Journal of Trading 7, no. 2 (March 31, 2012): 9–25. http://dx.doi.org/10.3905/jot.2012.7.2.009.
Full textMarbun, Sariana, and Siti Nurhayatun. "Penggunaan Media Flashcard Sebagai Upaya Mengembangkan Kemampuan Bahasa Anak Usia 5-6 Tahun." Paedagogi: Jurnal Kajian Ilmu Pendidikan (e-journal) 9, no. 1 (June 2, 2023): 54. http://dx.doi.org/10.24114/paedagogi.v9i1.45327.
Full textAini, Rofiqotul, and Mutia Rahmi Maulina. "Pendampingan Pembelajaran Huruf Hijaiyah Menggunakan Media FlashCrad di TPQ Aisyiyah Kauman Wiradesa Pekalongan." Mujtama Jurnal Pengabdian Masyarakat 3, no. 2 (November 13, 2023): 94–100. http://dx.doi.org/10.32528/mujtama.v3i2.18441.
Full textHabibi, Nugroho. "The Use of Flashcards in Improving Vocabulary Mastery of Students with Disability." INKLUSI 4, no. 2 (December 3, 2017): 197. http://dx.doi.org/10.14421/ijds.040203.
Full textColbran, Stephen, Wayne Jones, and John Milburn. "Comparing spaced repetition algorithms for legal digital flashcards." ASCILITE Publications, November 20, 2018, 92–102. https://doi.org/10.14742/apubs.2018.1923.
Full textDissertations / Theses on the topic "Flashcrash"
Aubrun, Cécilia. "Unraveling Financial Market Quakes : Exploring Endogenous Volatility Dynamics in Interconnected Markets." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX066.
Full textPast research has highlighted that feedback mechanisms underlie many financial markets instabilities. Endogenous dynamics of markets volatility and activity have indeed led to various notable crashes. Case in point: the events of May 6th, 2010, commonly referred to as the 2010 flash crash, exemplify how market instabilities stem from intrinsic features of financial markets. As evidence, an excessively rapid execution of sell orders triggered the rapid decline and subsequent recovery of the S&Pmini within the span of an hour. Moreover, market instabilities are compounded by their multidimensional nature and interconnectedness, as demonstrated by the propagation of volatility across diverse financial assets during events like the aforementioned flashcrash. Indeed, on May 6th, 2010, the S&Pmini flash crash affected 300 other assets alongside the S&Pmini.This thesis presents both a data-driven approach and a theoretical approach to investigate the endogenous nature of price movements within a multivariate framework.Our data-driven approach aims to characterize empirical price jumps. Leveraging interdisciplinary research suggesting that the time-asymmetry of activity can be used to classify bursts of activity as exogenous or endogenous, we develop a new unsupervised method based on wavelet coefficients (particularly suitable to reflect time asymmetry) to measure reflexivity of univariate price jumps. On top of that, our wavelet-based representation revealed that mean-reversion and trend are two additional key features, permitting identification of new classes of jumps. Furthermore, this representation allows to investigate the reflexive properties of co-jumps, defined by multiple stocks experiencing price jumps within the same minute. We argue that a significant fraction of co-jumps results from an endogenous contagion mechanism. Thus, May 6th event was not an isolated incident, and the interplay of endogenous dynamics alongside high levels of interconnectedness contributes to the instabilities observed within markets.Concomitantly, our theoretical inquiry focuses on the quadratic Hawkes (QHawkes) framework, originally introduced to describe volatility dynamics at tick-by-tick level. QHawkes processes are Poisson processes, which, through the expression of their intensity, depict the influence of the past on the probability of future activity. Previous work has proved that the univariate QHawkes model replicates several empirical features of financial time series, including fat tails of the returns’ distribution, volatility clustering and the time asymmetry effects (leverage and Zumbach effects). Indeed, the supplementary quadratic and leverage feedback allow to overcome the limitations of the original (linear) Hawkes framework. Besides, additional results on the stability of QHawkes processes are discussed, showing that the quadratic feedback can induce extreme events while staying stable by balancing inhibitory and excitatory realizations. To explore market interconnectedness, we extend QHawkes processes into multidimensional settings, encompassing several assets and their cross-interactions. A multi-assets framework necessitates consideration of additional stylized facts, such as the prevalence of co-jumps and cross time asymmetry effects. Indeed, this work sheds light on the cross leverage and cross Zumbach effects. Developing two frameworks, we show that the multivariate QHawkes (MQHawkes) can reproduce the empirical facts observed in financial markets. Calibrating the model on asset pairs further confirms that markets operate on the brink of instability.To be thorough, another multivariate, path-dependent volatility model is studied: the nested factor model with log-SfBM processes as volatilities. Our findings suggest that this framework reconciles differences in roughness between indices and stocks, offering further insights into the dynamics of multivariate volatility