Suchergebnisse

Systemic risk is a multi-layer network phenomenon. Layers represent various types of direct financial exposure of various types, including interbank liabilities and derivative- or foreign exchange exposures. An important layer of systemic risk emerges through common asset holdings of financial institutions. Strongly overlapping portfolios lead to similar exposures that are caused by price movements of the underlying financial assets. Based on the knowledge of individual portfolio holdings of financial agents, we quantify the systemic risk of overlapping portfolios. We then present an optimization procedure whereby we minimize the systemic risk in a given financial market by optimally rearranging overlapping portfolio networks. The optimization is performed under the constraints that the expected returns and risk of the individual portfolios are unchanged. We explicitly demonstrate the power of the method on the overlapping portfolio network of sovereign exposure between major European banks, using data from the European Banking Authority stress test of 2016. Systemic risk can be reduced by more than a factor of two, without any detrimental effects for the individual banks. These results are confirmed by a simple simulation of fire sales in the government bond market. In particular, we show that the contagion probability is dramatically reduced in the optimized network. We comment on the efficiency of the network optimization approach in comparison to equity-injection-based ways to reduce systemic risk. To obtain the same risk levels that are obtained in the network optimization, it would be necessary to increase the actual available capital by two thirds. This shows the immense potential of network-based systemic risk management.

Systemic risk is a multi-layer network phenomenon. Layers represent various types of direct financial exposure of various types, including interbank liabilities and derivative- or foreign exchange exposures. An important layer of systemic risk emerges through common asset holdings of financial institutions. Strongly overlapping portfolios lead to similar exposures that are caused by price movements of the underlying financial assets. Based on the knowledge of individual portfolio holdings of financial agents, we quantify the systemic risk of overlapping portfolios. We then present an optimization procedure whereby we minimize the systemic risk in a given financial market by optimally rearranging overlapping portfolio networks. The optimization is performed under the constraints that the expected returns and risk of the individual portfolios are unchanged. We explicitly demonstrate the power of the method on the overlapping portfolio network of sovereign exposure between major European banks, using data from the European Banking Authority stress test of 2016. Systemic risk can be reduced by more than a factor of two, without any detrimental effects for the individual banks. These results are confirmed by a simple simulation of fire sales in the government bond market. In particular, we show that the contagion probability is dramatically reduced in the optimized network. We comment on the efficiency of the network optimization approach in comparison to equity-injection-based ways to reduce systemic risk. To obtain the same risk levels that are obtained in the network optimization, it would be necessary to increase the actual available capital by two thirds. This shows the immense potential of network-based systemic risk management.

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national and sector accounts, input-output tables, government statistics, census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts as heterogeneous agents. The detailed structure of the ABM allows for a breakdown into sector level forecasts. Potential applications of the model include stress-testing and predicting the effects of changes in monetary, fiscal, or other macroeconomic policies.

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national and sector accounts, input-output tables, government statistics, census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts as heterogeneous agents. The detailed structure of the ABM allows for a breakdown into sector level forecasts. Potential applications of the model include stress-testing and predicting the effects of changes in monetary, fiscal, or other macroeconomic policies.

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national and sector accounts, input-output tables, government statistics, census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts as heterogeneous agents. The detailed structure of the ABM allows for a breakdown into sector level forecasts. Potential applications of the model include stress-testing and predicting the effects of changes in monetary, fiscal, or other macroeconomic policies.

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national and sector accounts, input-output tables, government statistics, census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts as heterogeneous agents. The detailed structure of the ABM allows for a breakdown into sector level forecasts. Potential applications of the model include stress-testing and predicting the effects of changes in monetary, fiscal, or other macroeconomic policies.

Since the beginning of the 2008 financial crisis almost half a trillion euros have been spent to financially assist EU member states in taxpayer-funded bail-outs. These crisis resolutions are often accompanied by austerity programs causing political and social friction on both domestic and international levels. The question of how to resolve failing financial institutions, and how this depends on economic preconditions, is therefore a pressing and controversial issue of vast political importance. In this work we employ an agent-based model to study the economic and financial ramifications of three highly relevant crisis resolution mechanisms. To establish the validity of the model we show that it reproduces a series of key stylized facts of the financial and real economy. The distressed institution can either be closed via a purchase & assumption transaction, it can be bailed-out using taxpayer money, or it may be bailed-in in a debt-to-equity conversion. We find that for an economy characterized by low unemployment and high productivity the optimal crisis resolution with respect to financial stability and economic productivity is to close the distressed institution. For economies in recession with high unemployment the bail-in tool provides the most efficient crisis resolution mechanism. Under no circumstances do taxpayer-funded bail-out schemes outperform bail-ins with private sector involvement.

Since the beginning of the 2008 financial crisis almost half a trillion euros have been spent to financially assist EU member states in taxpayer-funded bail-outs. These crisis resolutions are often accompanied by austerity programs causing political and social friction on both domestic and international levels. The question of how to resolve failing financial institutions, and how this depends on economic preconditions, is therefore a pressing and controversial issue of vast political importance. In this work we employ an agent-based model to study the economic and financial ramifications of three highly relevant crisis resolution mechanisms. To establish the validity of the model we show that it reproduces a series of key stylized facts of the financial and real economy. The distressed institution can either be closed via a purchase & assumption transaction, it can be bailed-out using taxpayer money, or it may be bailed-in in a debt-to-equity conversion. We find that for an economy characterized by low unemployment and high productivity the optimal crisis resolution with respect to financial stability and economic productivity is to close the distressed institution. For economies in recession with high unemployment the bail-in tool provides the most efficient crisis resolution mechanism. Under no circumstances do taxpayer-funded bail-out schemes outperform bail-ins with private sector involvement.

Reliable estimates of indirect economic losses arising from natural disasters are currently out of scientific reach. To address this problem, we propose a novel approach that combines a probabilistic physical damage catastrophe model with a new generation of macroeconomic agent-based models (ABMs). The ABM moves beyond the state of the art by exploiting large data sets from detailed national accounts, census data, and business information, etc., to simulate interactions of millions of agents representing \emph{each} natural person or legal entity in a national economy. The catastrophe model introduces a copula approach to assess flood losses, considering spatial dependencies of the flood hazard. These loss estimates are used in a damage scenario generator that provides input for the ABM, which then estimates indirect economic losses due to the event. For the first time, we are able to link environmental and economic processes in a computer simulation at this level of detail. We show that moderate disasters induce comparably small but positive short- to medium-term, and negative long-term economic impacts. Large-scale events, however, trigger a pronounced negative economic response immediately after the event and in the long term, while exhibiting a temporary short- to medium-term economic boost. We identify winners and losers in different economic sectors, including the fiscal consequences for the government. We quantify the critical disaster size beyond which the resilience of an economy to rebuild reaches its limits. Our results might be relevant for the management of the consequences of systemic events due to climate change and other disasters.

Reliable estimates of indirect economic losses arising from natural disasters are currently out of scientific reach. To address this problem, we propose a novel approach that combines a probabilistic physical damage catastrophe model with a new generation of macroeconomic agent-based models (ABMs). The ABM moves beyond the state of the art by exploiting large data sets from detailed national accounts, census data, and business information, etc., to simulate interactions of millions of agents representing \emph{each} natural person or legal entity in a national economy. The catastrophe model introduces a copula approach to assess flood losses, considering spatial dependencies of the flood hazard. These loss estimates are used in a damage scenario generator that provides input for the ABM, which then estimates indirect economic losses due to the event. For the first time, we are able to link environmental and economic processes in a computer simulation at this level of detail. We show that moderate disasters induce comparably small but positive short- to medium-term, and negative long-term economic impacts. Large-scale events, however, trigger a pronounced negative economic response immediately after the event and in the long term, while exhibiting a temporary short- to medium-term economic boost. We identify winners and losers in different economic sectors, including the fiscal consequences for the government. We quantify the critical disaster size beyond which the resilience of an economy to rebuild reaches its limits. Our results might be relevant for the management of the consequences of systemic events due to climate change and other disasters.