The Misbehavior of Markets: A Fractal View of Risk, Ruin, and Reward
By Benoit B. Mandelbrot, Richard L. Hudson. 2004
523 citations recorded by the source corpus.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/2f82413d3e42b846175e560deabbb454ac015886
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In Economics and Econometrics
- Bin Jiang
- Elise Gourier
- Leandro Nunes de Castro
- Alfred Stein
- Bill McKelvey
- Dan Hendrycks
- Pierpaolo Andriani
- Jacob Steinhardt
Citations in the corpus
- Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, background): “Third, big data due to the size are more likely to capture a true picture of reality, and reality is no doubt fractal (Bak 1996, Mandelbrot & Hudson 2004).”
- Social discount rates for the European Union: an overview (2006, background): “Second is a parallel finance literature, the first readily accessible text being Mandelbrot and Hudson (2004) , that develops statistical models of financial markets based on fractal analysis.”
- Fundamentals of natural computing: an overview (2007, background): “However, fractals have several other approaches and applications, among which the main current trends are the use of fractals as function approximators [19,46,200], their applications in economics and finance [163,184,185,189], several technological applications, such as antenna design, data mining ”
- Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity (2014, background): “Mandelbrot B. B. and Hudson R. L. (2004), The (Mis)Behavior of Markets: A fractal view of risk, ruin and reward, Basic Books: New York.”
- Operational Risk Quantification Using Extreme Value Theory and Copulas: From Theory to Practice (2009, background): “11See Mandelbrot and Hudson (2004), p.232, for a discussion of the consequences of subexponentiality.”
- Why Gaussian statistics are mostly wrong for strategic organization (2005, background): “Needless to say, GARCH also does not accommodate the power law extremes that Mandelbrot has been observing in financial markets over the past 70 years (Mandelbrot and Hudson, 2004).”
- Unsolved Problems in ML Safety (2021, background): “Additionally, benchmarks should focus on “wild” distribution shifts that cause large accuracy drops over “mild” shifts [126].”