Enjoy Oligarchy

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“Step one, help yourself to tasty assets from the public trough. Step two, figure out how to keep them.

“The best investment in Ukrainian history may be about to become even better—Burisma’s recruiting of Hunter Biden to its board. After a government minister allegedly awards himself lucrative gas rights, the Ukrainian people overthrow a regime famous for its corruption. In the normal course of events, a successor regime would seek to establish its bona fides by clawing back the disputed gas rights, except for one thing: The new government, under military threat from Russia, is desperately dependent on a U.S. administration whose vice president was Joe Biden.

“It was unnecessary for Mr. Biden to do anything. The new regime was checkmated from the start in its desire to relieve Burisma of its questionably obtained assets. Now U.S. reporters frightened to be seen playing it straight in the middle of an election insist there’s nothing to see here except the sad misjudgment of Mr. Biden’s dissolute son. A normal person, though, can’t help regarding Burisma as the culminating chapter in a Hunter Biden career in which, from day one, every job and opportunity was handed to him by someone seeking influence with his father.”

https://www.wsj.com/articles/a-laptop-window-on-the-oligarchy-11603235685?mod=opinion_featst_pos2

Loss Loss Reserves – and CECL Relief

With Senator Chuck Schumer covering their backs under CARES Act stimulus, mega banks don’t need to think much about reserving for losses on financial instruments under Current Expected Credit Losses or CECL (pronounced Cecil).

So, what kind of reserves are indicated? A lot less than the Crash.

Loan Loss Reserves at Mega Banks Are Far from Where They Need to Be

Dynamical Landscape and Multistability of the Earth’s Climate

Georgios Margazoglou, Tobias Grafke, Alessandro Laio, Valerio Lucarini

Abstract: We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize the data landscape of the simulation data to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth’s climate.

https://arxiv.org/abs/2010.10374