The Devil's Derivatives: The Untold Story of the Slick by Nicholas Dunbar

By Nicholas Dunbar

A compelling narrative on what went incorrect with our monetary system—and who’s to blame.

From an award-winning journalist who has been overlaying the for greater than a decade, The Devil’s Derivatives charts the untold tale of contemporary monetary innovation—how funding banks invented new monetary items, how traders the world over have been wooed into deciding to buy them, how regulators have been seduced by way of the political rewards of simple credits, and the way speculators made a killing from the near-meltdown of the monetary system.

Author Nicholas Dunbar demystifies the revolution that in brief gave finance an analogous highbrow respectability as theoretical physics. He explains how bankers around the globe created a mystery trillion-dollar laptop that introduced reasonable mortgages to the hundreds and riches past desires to the monetary innovators.

Fundamental to this saga is how “the those that hated to lose” have been persuaded to simply accept chance by way of “the those that enjoyed to win.” Why did humans come to belief and recognize arcane monetary instruments? Who have been the bankers competing to gather the fundamental parts into more and more complicated machines? How did this approach in attaining its personal unstoppable momentum—ending in cave in, bailouts, and a public outcry opposed to the giants of finance?

Provocative and interesting, The Devil’s Derivatives sheds much-needed mild at the forces that fueled the main brutal fiscal downturn because the nice melancholy.

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Extra info for The Devil's Derivatives: The Untold Story of the Slick Traders and Hapless Regulators Who Almost Blew Up Wall Street . . . and Are Ready to Do It Again

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Introduce exogenous market impact which affect different stocks similarly, thereby introducing positive correlation and thus large eigenvalues. This is clear from the general formulation of (linear) factor models such as the CAPM, APT, and Fama–French approaches in which the returns of all stocks are regressed against the same set of factors. Actually, we propose that the two chains of cause and result may be intrinsically coupled: the correlation structure between stocks is a stable attractor of a self-organized dynamics with positive and negative feedbacks in which factors exist because correlations exist, and correlations exist because factors exist.

1 Motivations As discussed in Chap. 1, the risks of a portfolio of N assets are fully characterized by the (possibly time-dependent) multivariate distribution of returns, which is the joint probability of any given realization of the N asset returns. For Gaussian models, this requires only the estimation of the average returns and of their covariance matrix. However, there is no doubt anymore that the Gaussian model is an inadequate description of real financial data (see for instance Fig. 1): the tails of the distributions are much fatter than Gaussian and the dependence between assets is not fully captured by the sole covariance matrix.

21)) for which the multifractal spectrum f (α) defined in Sect. 2 is negative. 19) of f (α), only positive f (α)’s correspond to genuine fractal dimensions and are thus observable: this is because they correspond to more than a few points of observations in the limit ∆t T . The key remark of Muzy et al. [365] is therefore that the observable exponent bobs for an infinite time series will be the largest positive q such that f (α) ≥ 0: bobs = sup{q, q > 1, f (α) > 0} . 3) From the correlation function of the log-volatility, from the scaling approach using the multifractal spectrum or from the generalized method of moment [321].

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