Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management

By Jean-Philippe Bouchaud, Marc Potters. 2011

703 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a970d706c7c43f4330f334117041cb316000e87d

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Citations in the corpus

Statistical physics of inference: thresholds and algorithms (2015, background): “The same strategy of computing leading singular (or eigen-) vector of some matrix related to the data is applied in many settings including the planted partitioning problem closely related to the planted spin glass model [52, 53], or in understanding financial data [29, 147].”
Negative bubbles and shocks in cryptocurrency markets (2016, unknown): “Similarly, from Assumption 2 equations (4), (6) and (9) give σ(2)(t) + v(2)h(t) = σ(2); σ(2)(t) = σ(2) − v(2)h(t).”
Recommender Systems (2012, background): “Physicists, with their long experience with data-driven research, have joined this trend and contributed to many fields such as finance [3, 4], network theory [5, 6, 7, 8, 9] and social dynamics [10] which are outside their traditional realm.”
Volatility is rough (2014, unknown): “This fractal-type behavior of the volatility has been investigated both empirically and theoretically in, for example, [5, 9, 37].”
Limit order books (2010, background): “Over the same date range, German interest rates futures contracts showed similar behaviour, whereas the Pounds Sterling/US dollar currency pair exhibited negative autocorrelation up to timescales of 20 minutes, but not 25 minutes (Bouchaud and Potters, 2003).”
Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series (2011, background): “Values of the scaling exponents α between 2 and 4 are commonly observed in these systems [26, 27].”
Cleaning large correlation matrices: tools from random matrix theory (2016, background): “It iswell-known for instance that financial returns are strongly non-Gaussian, with power-law tails [25], and hence, the condition of a sufficient number of bounded moments can be seen as restrictive.”
Portfolio diversification and value at risk under thick-tailedness (2009, unknown): “2002 , Bouchaud and Potters 2004 , the papers in Szego 2004 and Fabozzi et al.”
The characteristic function of rough Heston models (2016, background): “The popularity of this model is probably due to three main reasons: • It reproduces well several important stylized facts of low frequency price data, namely leverage effect, time-varying volatility and fat tails, see [7, 9, 13, 35].”
The Joint Distribution of Stock Returns is Not Elliptical (2010, unknown): “There are however many situations of practical interest where the (linear) correlation matrix is inadequate or insufficient [1,30,33].”
Protein sectors: evolutionary units of three-dimensional structure (2009, methodology): “This mode is irrelevant for identifying the nonglobal, heterogeneous correlations between stocks that define the different business sectors and is therefore removed (Bouchaud and Potters, 2004; Plerou et al., 2002).”
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks (2019, background): “In Statistical Physics, Universality of PL exponents is very non-trivial, and it suggests the presence of a deeper, underlying, Universal mechanism driving the system dynamics [21, 22].”