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Essays on Credit Markets and Banking


  • Holmberg, Ulf

    () (Department of Economics, Umeå University)


This thesis consists of four self-contained papers related to banking, credit markets and financial stability. Paper [I] presents a credit market model and finds, using an agent based modeling approach, that credit crunches have a tendency to occur; even when credit markets are almost entirely transparent in the absence of external shocks. We find evidence supporting the asset deterioration hypothesis and results that emphasize the importance of accurate firm quality estimates. In addition, we find that an increase in the debt’s time to maturity, homogenous expected default rates and a conservative lending approach, reduces the probability of a credit crunch. Thus, our results suggest some up till now partially overlooked components contributing to the financial stability of an economy. Paper [II] derives an econometric disequilibrium model for time series data. This is done by error correcting the supply of some good. The model separates between a continuously clearing market and a clearing market in the long-run such that we are able to obtain a novel test of clearing markets. We apply the model to the Swedish market for short-term business loans, and find that this market is characterized by a long-run nonmarket clearing equilibrium. Paper [III] studies the risk-return profile of centralized and decentralized banks. We address the conditions that favor a particular lending regime while acknowledging the effects on lending and returns caused by the course of the business cycle. To analyze these issues, we develop a model which incorporates two stylized facts; (i) banks in which lending decisions are decentralized tend to have a lower cost associated with screening potential borrowers and (ii) decentralized decision-making may generate inefficient outcomes because of lack of coordination. Simulations are used to compare the two banking regimes. Among the results, it is found that even though a bank group where decisions are decentralized may end up with a portfolio of loans which is (relatively) poorly diversified between regions, the ability to effectively screen potential borrowers may nevertheless give a decentralized bank a lower overall risk in the lending portfolio than when decisions are centralized. In Paper [IV], we argue that the practice used in the valuation of a portfolio of assets is important for the calculation of the Value at Risk. In particular, a seller seeking to liquidate a large portfolio may not face horizontal demand curves. We propose a partially new approach for incorporating this fact in the Value at Risk and Expected Shortfall measures and in an empirical illustration, we compare it to a competing approach. We find substantial differences.

Suggested Citation

  • Holmberg, Ulf, 2012. "Essays on Credit Markets and Banking," Umeå Economic Studies 840, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0840

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    References listed on IDEAS

    1. Dionne, Georges & Duchesne, Pierre & Pacurar, Maria, 2009. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 777-792, December.
    2. Turan G. Bali & Panayiotis Theodossiou, 2008. "Risk Measurement Performance of Alternative Distribution Functions," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(2), pages 411-437.
    3. Ernst, Cornelia & Stange, Sebastian & Kaserer, Christoph, 2012. "Measuring market liquidity risk - which model works best?," Journal of Financial Transformation, Capco Institute, vol. 35, pages 133-146.
    4. Quoreshi, Shahiduzzaman, 2005. "Modelling High Frequency Financial Count Data," Umeå Economic Studies 656, Umeå University, Department of Economics.
    5. Clive G. Bowsher, 2004. "Modelling the Dynamics of Cross-Sectional Price Functions: an Econometric Analysis of the Bid and Ask Curves of an Automated Exchange," OFRC Working Papers Series 2004fe19, Oxford Financial Research Centre.
    6. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    7. Pierre Giot & Joachim Grammig, 2006. "How large is liquidity risk in an automated auction market?," Empirical Economics, Springer, vol. 30(4), pages 867-887, January.
    8. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
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    More about this item


    financial stability; credit market; banking; agent based model; simulations; disequilibrium; clearing market; business cycle; risk; organization;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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