What is the Minimal Systemic Risk in Financial Exposure Networks?

Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased. It has been shown that systemic risk is to a large extent related to the underlying network topology of financial exposures. A natural question arising is how much systemic risk can be eliminated by optimally rearranging these networks and without increasing capital requirements. Overlapping portfolios with minimized systemic risk which provide the same market functionality as empirical ones have been studied by [pichler2018]. Here we propose a similar method for direct exposure networks, and apply it to cross-sectional interbank loan networks, consisting of 10 quarterly observations of the Austrian interbank market. We show that the suggested framework rearranges the network topology, such that systemic risk is reduced by a factor of approximately 3.5, and leaves the relevant economic features of the optimized network and its agents unchanged. The presented optimization procedure is not intended to actually re-configure interbank markets, but to demonstrate the huge potential for systemic risk management through rearranging exposure networks, in contrast to increasing capital requirements that were shown to have only marginal effects on systemic risk [poledna2017]. Ways to actually incentivize a self-organized formation toward optimal network configurations were introduced in [thurner2013] and [poledna2016]. For regulatory policies concerning financial market stability the knowledge of minimal systemic risk for a given economic environment can serve as a benchmark for monitoring actual systemic risk in markets.Comment: 25 page

Laboratory evolution of a fungal heme-thiolate enzyme promoting peroxidase or peroxygenase activity

Please click Additional Files below to see the full abstrac

Tuning microbial hosts for membrane protein production

The last four years have brought exciting progress in membrane protein research. Finally those many efforts that have been put into expression of eukaryotic membrane proteins are coming to fruition and enable to solve an ever-growing number of high resolution structures. In the past, many skilful optimization steps were required to achieve sufficient expression of functional membrane proteins. Optimization was performed individually for every membrane protein, but provided insight about commonly encountered bottlenecks and, more importantly, general guidelines how to alleviate cellular limitations during microbial membrane protein expression. Lately, system-wide analyses are emerging as powerful means to decipher cellular bottlenecks during heterologous protein production and their use in microbial membrane protein expression has grown in popularity during the past months

Constrained Global Optimization by Smoothing

This paper proposes a novel technique called "successive stochastic smoothing" that optimizes nonsmooth and discontinuous functions while considering various constraints. Our methodology enables local and global optimization, making it a powerful tool for many applications. First, a constrained problem is reduced to an unconstrained one by the exact nonsmooth penalty function method, which does not assume the existence of the objective function outside the feasible area and does not require the selection of the penalty coefficient. This reduction is exact in the case of minimization of a lower semicontinuous function under convex constraints. Then the resulting objective function is sequentially smoothed by the kernel method starting from relatively strong smoothing and with a gradually vanishing degree of smoothing. The finite difference stochastic gradient descent with trajectory averaging minimizes each smoothed function locally. Finite differences over stochastic directions sampled from the kernel estimate the stochastic gradients of the smoothed functions. We investigate the convergence rate of such stochastic finite-difference method on convex optimization problems. The "successive smoothing" algorithm uses the results of previous optimization runs to select the starting point for optimizing a consecutive, less smoothed function. Smoothing provides the "successive smoothing" method with some global properties. We illustrate the performance of the "successive stochastic smoothing" method on test-constrained optimization problems from the literature.Comment: 17 pages, 1 tabl

What is the Minimal Systemic Risk in Financial Exposure Networks? INET Oxford Working Paper, 2019-03

Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased (see ECB data 1 ; SRISK time series 2 ). It has been shown that systemic risk is to a large extent related to the underlying network topology of financial exposures. A natural question arising is how much systemic risk can be eliminated by optimally rearranging these networks and without increasing capital requirements. Overlapping portfolios with minimized systemic risk which provide the same market functionality as empir- ical ones have been studied by Pichler et al. (2018). Here we propose a similar method for direct exposure networks, and apply it to cross-sectional interbank loan networks, consisting of 10 quarterly observations of the Austrian interbank market. We show that the suggested framework rearranges the network topol- ogy, such that systemic risk is reduced by a factor of approximately 3.5, and leaves the relevant economic features of the optimized network and its agents unchanged. The presented optimization procedure is not intended to actually re-configure interbank markets, but to demonstrate the huge potential for systemic risk management through rearranging exposure networks, in contrast to increasing capital requirements that were shown to have only marginal effects on systemic risk (Poledna et al., 2017). Ways to actually incentivize a self-organized formation toward optimal network configurations were introduced in Thurner and Poledna (2013) and Poledna and Thurner (2016). For regulatory policies concerning financial market stability the knowledge of minimal systemic risk for a given economic environment can serve as a benchmark for monitoring actual systemic risk in markets

Forecasting the propagation of pandemic shocks with a dynamic input-output model

We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool

Building an alliance to map global supply networks

The global economy consists of more than 300 million firms, connected through an estimated 13 billion supply links [see supplementary materials (SM)], that produce most goods and services. It has long been unthinkable to analyze the world economy at the firm level, even less so its intricate network of supply chain linkages. This blind spot has left us ill-prepared to make fast and well-informed decisions, begetting, for example, prolonged shortages in raw materials and critical medical supplies during the COVID-19 pandemic. Now, the availability of new data and recent methodological advances allow us to reconstruct a large share of the global firm-level supply network. Because mapping this network is likely to continue to improve, it is essential to initiate a discussion about responsible management and effective use of these data for the global public good. This requires new collaborative efforts between nations, their public institutions, international organizations, the private sector, and scientists

Chronik des Großherzoglichen Hof- und Nationaltheaters in Mannheim : zur Feier seines hundertjährigen Bestehens am 7. October 1879; nebst einer Abbildung des Theaters vor seinem Umbau

von Anton Pichler, Großh. Hoftheater-RegisseurIn Fraktu