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We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks.
Derivatives usage in risk management by U.S. and German non-financial firms : a comparative survey
(1998)
This paper is a comparative study of the responses to the 1995 Wharton School survey of derivative usage among US non-financial firms and a 1997 companion survey on German non-financial firms. It is not a mere comparison of the results of both studies but a comparative study, drawing a comparable subsample of firms from the US study to match the sample of German firms on both size and industry composition. We find that German firms are more likely to use derivatives than US firms, with 78% of German firms using derivatives compared to 57% of US firms. Aside from this higher overall usage, the general pattern of usage across industry and size groupings is comparable across the two countries. In both countries, foreign currency derivative usage is most common, followed closely by interest rate derivatives, with commodity derivatives a distant third. Usage rates across all three classes of derivatives are higher for German firms than US firms. In contrast to the similarities, firms in the two countries differ notably on issues such as the primary goal of hedging, their choice of instruments, and the influence of their market view when taking derivative positions. These differences appear to be driven by the greater importance of financial accounting statements in Germany than the US and stricter German corporate policies of control over derivative activities within the firm. German firms also indicate significantly less concern about derivative related issues than US firms, which appears to arise from a more basic and simple strategy for using derivatives. Finally, among the derivative non-users, German firms tend to cite reasons suggesting derivatives were not needed whereas US firms tend to cite reasons suggesting a possible role for derivatives, but a hesitation to use them for some reason.
This paper analyzes the emergence of systemic risk in a network model of interconnected bank balance sheets. Given a shock to asset values of one or several banks, systemic risk in the form of multiple bank defaults depends on the strength of balance sheets and asset market liquidity. The price of bank assets on the secondary market is endogenous in the model, thereby relating funding liquidity to expected solvency - an important stylized fact of banking crises. Based on the concept of a system value at risk, Shapley values are used to define the systemic risk charge levied upon individual banks. Using a parallelized simulated annealing algorithm the properties of an optimal charge are derived. Among other things we find that there is not necessarily a correspondence between a bank's contribution to systemic risk - which determines its risk charge - and the capital that is optimally injected into it to make the financial system more resilient to systemic risk. The analysis has policy implications for the design of optimal bank levies. JEL Classification: G01, G18, G33 Keywords: Systemic Risk, Systemic Risk Charge, Systemic Risk Fund, Macroprudential Supervision, Shapley Value, Financial Network
This paper makes a conceptual contribution to the effect of monetary policy on financial stability. We develop a microfounded network model with endogenous network formation to analyze the impact of central banks' monetary policy interventions on systemic risk. Banks choose their portfolio, including their borrowing and lending decisions on the interbank market, to maximize profit subject to regulatory constraints in an asset-liability framework. Systemic risk arises in the form of multiple bank defaults driven by common shock exposure on asset markets, direct contagion via the interbank market, and firesale spirals. The central bank injects or withdraws liquidity on the interbank markets to achieve its desired interest rate target. A tension arises between the beneficial effects of stabilized interest rates and increased loan volume and the detrimental effects of higher risk taking incentives. We find that central bank supply of liquidity quite generally increases systemic risk.
We develop a dynamic network model whose links are governed by banks' optmizing decisions and by an endogenous tâtonnement market adjustment. Banks in our model can default and engage in firesales: risk is transmitted through direct and cascading counterparty defaults as well as through indirect pecuniary externalities triggered by firesales. We use the model to assess the evolution of the network configuration under various prudential policy regimes, to measure banks' contribution to systemic risk (through Shapley values) in response to shocks and to analyze the effects of systemic risk charges. We complement the analysis by introducing the possibility of central bank liquidity provision.
This paper makes a conceptual contribution to the effect of monetary policy on financial stability. We develop a microfounded network model with endogenous network formation to analyze the impact of central banks' monetary policy interventions on systemic risk. Banks choose their portfolio, including their borrowing and lending decisions on the interbank market, to maximize profit subject to regulatory constraints in an asset-liability framework. Systemic risk arises in the form of multiple bank defaults driven by common shock exposure on asset markets, direct contagion via the interbank market, and firesale spirals. The central bank injects or withdraws liquidity on the interbank markets to achieve its desired interest rate target. A tension arises between the beneficial effects of stabilized interest rates and increased loan volume and the detrimental effects of higher risk taking incentives. We find that central bank supply of liquidity quite generally increases systemic risk.
This paper outlines a new method for using qualitative information to analyze the monetary policy strategy of central banks. Quantitative assessment indicators that are extracted from a central bank's public statements via the balance statistic approach are employed to estimate a Taylor-type rule. This procedure allows to directly capture a policymaker's assessments of macroeconomic variables that are relevant for its decision making process. As an application of the proposed method the monetary policy of the Bundesbank is re-investigated with a new dataset. One distinctive feature of the Bundesbank's strategy consisted of targeting growth in monetary aggregates. The analysis using the proposed method provides evidence that the Bundesbank indeed took into consideration monetary aggregates but also real economic activity and inflation developments in its monetary policy strategy since 1975. JEL Classification: E52, E58, N14 Keywords: Monetary Policy Rule, Statement Indicators, Bundesbank, Monetary Targeting
The interbank market is important for the efficient functioning of the financial system, transmission of monetary policy and therefore ultimately the real economy. In particular, it facilitates banks' liquidity management. This paper aims at extending the literature which views interbank markets as mutual liquidity insurance mechanism by taking into account persistence of liquidity shocks. Following a theory of long-term interbank funding a financial system which is modeled as a micro-founded agent based complex network interacting with a real economic sector is developed. The model features interbank funding as an over-the-counter phenomenon and realistically replicates financial system phenomena of network formation, monetary policy transmission and endogenous money creation. The framework is used to carry out an optimal policy analysis in which the policymaker maximizes real activity via choosing the optimal interest rate in a trade-off between loan supply and financial fragility. It is shown that the interbank market renders the financial system more efficient relative to a setting without mutual insurance against persistent liquidity shocks and therefore plays a crucial role for welfare.
This paper provides an overview of how to use "big data" for economic research. We investigate the performance and ease of use of different Spark applications running on a distributed file system to enable the handling and analysis of data sets which were previously not usable due to their size. More specifically, we explain how to use Spark to (i) explore big data sets which exceed retail grade computers memory size and (ii) run typical econometric tasks including microeconometric, panel data and time series regression models which are prohibitively expensive to evaluate on stand-alone machines. By bridging the gap between the abstract concept of Spark and ready-to-use examples which can easily be altered to suite the researchers need, we provide economists and social scientists more generally with the theory and practice to handle the ever growing datasets available. The ease of reproducing the examples in this paper makes this guide a useful reference for researchers with a limited background in data handling and distributed computing.
In this paper, we investigate how bank mergers affect bank revenues and present empirical evidence that mergers among banks have a substantial and persistent negative impact on merging banks’ revenues. We refer to merger related negative effects on banks’ revenues as dissynergies and suggest that they are a result of organizational diseconomies, the loss of customers and the temporary distraction of management from day-to-day operations by effecting the merger. For our analyses we draw on a proprietary data set with detailed financials of all 457 regional savings banks in Germany, which have been involved in 212 mergers between 1994 and 2006. We find that the negative impact of a merger on net operating revenues amounts to 3% of pro-forma consolidated banks’ operating profits and persists not only for the year of the merger but for up to four years post-merger. Only thereafter mergers exhibit a significantly superior performance compared to their respective pre-merger performance or the performance of their non-merging peers. The magnitude and persistence of merger related revenue dissynergies highlight their economic relevance. Previous research on post-merger performance mainly focuses on the effects from mergers on banks’ (cost) efficiency and profitability but fails to provide clear and consistent results. We are the first, to our knowledge, to examine the post-merger performance of banks’ net operating revenues and to empirically verify significant negative implications of mergers for banks’ net operating revenues. We propose that our finding of negative merger related effects on banks’ operating revenues is the reason why previous research fails to show merger related gains.