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87
We present a network model of the interbank market in which optimizing risk averse banks lend to each other and invest in non-liquid assets. Market clearing takes place through a tâtonnement process which yields the equilibrium price, while traded quantities are determined by means of a matching algorithm. We compare three alternative matching algorithms: maximum entropy, closest matching and random matching. Contagion occurs through liquidity hoarding, interbank interlinkages and fire sale externalities. The resulting network configurations exhibits a core-periphery structure, dis-assortative behavior and low clustering coefficient. We measure systemic importance by means of network centrality and input-output metrics and the contribution of systemic risk by means of Shapley values. Within this framework we analyze the effects of prudential policies on the stability/efficiency trade-off. Liquidity requirements unequivocally decrease systemic risk but at the cost of lower efficiency (measured by aggregate investment in non-liquid assets); equity requirements tend to reduce risk (hence increase stability) without reducing significantly overall investment.
259
We uncover a new channel for spillovers of funding dry-ups. The 2016 US money market fund (MMF) reform exogenously reduced unsecured MMF funding for some banks. We use novel data to trace those banks to a platform for corporate deposit funding. We show that intensified competition for corporate deposits spilled the funding squeeze over to other banks with no MMF exposure. These banks paid more for deposits, and their pool of funding providers deteriorated. Moreover, their lending volumes and margins declined, and their stocks underperformed. Our results suggest that banks' competitiveness in funding markets affect their competitiveness in lending markets.
29
The analyses of intersectoral linkages of Leontief (1941) and Hirschman (1958) provide a natural way to study the transmission of risk among interconnected banks and to measure their systemic importance. In this paper we show how classic input-output analysis can be applied to banking and how to derive six indicators that capture different aspects of systemic importance, using a simple numerical example for illustration. We also discuss the relationship with other approaches, most notably network centrality measures, both formally and by means of a simulated network.
102
Research on interbank networks and systemic importance is starting to recognise that the web of exposures linking banks balance sheets is more complex than the single-layer-of-exposure paradigm. We use data on exposures between large European banks broken down by both maturity and instrument type to characterise the main features of the multiplex structure of the network of large European banks. This multiplex network presents positive correlated multiplexity and a high similarity between layers, stemming both from standard similarity analyses as well as a core-periphery analyses of the different layers. We propose measures of systemic importance that fit the case in which banks are connected through an arbitrary number of layers (be it by instrument, maturity or a combination of both). Such measures allow for a decomposition of the global systemic importance index for any bank into the contributions of each of the sub-networks, providing a useful tool for banking regulators and supervisors. We use the dataset of exposures between large European banks to illustrate the proposed measures.
264
A common prediction of macroeconomic models of credit market frictions is that the tightness of financial constraints is countercyclical. As a result, theory implies a negative collateralizability premium; that is, capital that can be used as collateral to relax financial constraints provides insurance against aggregate shocks and commands a lower risk compensation compared with non-collateralizable assets. We show that a longshort portfolio constructed using a novel measure of asset collateralizability generates an average excess return of around 8% per year. We develop a general equilibrium model with heterogeneous firms and financial constraints to quantitatively account for the collateralizability premium.
20
In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). Within a system of quantile regressions for four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies) we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions. Using daily data, we can trace out the spillover effects over time in a set of impulse response functions and find that they reach their peak after 10 to 15 days.
152
The ECB’s Outright Monetary Transactions (OMT) program, launched in summer 2012, indirectly recapitalized periphery country banks through its positive impact on the value of sovereign bonds. However, the regained stability of the European banking sector has not fully transferred into economic growth. We show that zombie lending behavior of banks that still remained undercapitalized after the OMT announcement is an important reason for this development. As a result, there was no positive impact on real economic activity like employment or investment. Instead, firms mainly used the newly acquired funds to build up cash reserves. Finally, we document that creditworthy firms in industries with a high prevalence of zombie firms suffered significantly from the credit misallocation, which slowed down the economic recovery.
376
We develop a two-sector incomplete markets integrated assessment model to analyze the effectiveness of green quantitative easing (QE) in complementing fiscal policies for climate change mitigation. We model green QE through an outstanding stock of private assets held by a monetary authority and its portfolio allocation between a clean and a dirty sector of production. Green QE leads to a partial crowding out of private capital in the green sector and to a modest reduction of the global temperature by 0.04 degrees of Celsius until 2100. A moderate global carbon tax of 50 USD per tonne of carbon is 4 times more effective.
314
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.