IDEAS home Printed from https://ideas.repec.org/a/wsi/ijfexx/v02y2015i03ns2424786315500310.html
   My bibliography  Save this article

Dynamic asset allocation for a bank under CRRA and HARA framework

Author

Listed:
  • Ryle S. Perera

    (Department of Applied Finance and Actuarial Studies, Faculty of Business and Economics, Macquarie University, Sydney, NSW 2109, Australia;
    Department of Applied Mathematics and Statistics, Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA)

Abstract

This paper analyzes an optimal investment and management strategy for a bank under constant relative risk aversion (CRRA) and hyperbolic absolute risk aversion (HARA) utility functions. We assume that the bank can invest in treasuries, stock index fund and loans, in an environment subject to stochastic interest rate and inflation uncertainty. The interest rate and the expected rate of inflation follow a correlated Ornstein–Uhlenbeck processes and the risk premia are constants. Then we consider the portfolio choice under a power utility that the bank's shareholders can maximize expected utility of wealth at a given investment horizon. Closed form solutions are obtained in a dynamic portfolio optimization model. The results indicate that the optimal proportion invested in treasuries increases while the optimal proportion invested in the loans progressively decreases with respect to time.

Suggested Citation

  • Ryle S. Perera, 2015. "Dynamic asset allocation for a bank under CRRA and HARA framework," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-19.
    • Handle: RePEc:wsi:ijfexx:v:02:y:2015:i:03:n:s2424786315500310
    DOI: 10.1142/S2424786315500310
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S2424786315500310
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S2424786315500310?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Claudio Borio & Craig Furfine & Philip Lowe, 2001. "Procyclicality of the financial system and financial stability: issues and policy options," BIS Papers chapters, in: Bank for International Settlements (ed.), Marrying the macro- and micro-prudential dimensions of financial stability, volume 1, pages 1-57, Bank for International Settlements.
    2. Philip Lowe, 2002. "Credit risk measurement and procyclicality," BIS Working Papers 116, Bank for International Settlements.
    3. Bank for International Settlements, 2001. "Marrying the macro- and micro-prudential dimensions of financial stability," BIS Papers, Bank for International Settlements, number 01.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ryle S. Perera & Kimitoshi Sato, 2018. "Optimal asset allocation for a bank under risk control," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 1-27, September.
    2. Ryle S. Perera, 2020. "Provisions for bank deposit withdrawals and portfolio selection," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 1-32, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Markus Behn & Rainer Haselmann & Paul Wachtel, 2016. "Procyclical Capital Regulation and Lending," Journal of Finance, American Finance Association, vol. 71(2), pages 919-956, April.
    2. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
    3. Lennart Freitag, 2015. "Procyclicality and Path Dependence of Sovereign Credit Ratings: The Example of Europe," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 44(2), pages 309-332, July.
    4. Claudio Borio & Mathias Drehmann, 2011. "Toward an Operational Framework for Financial Stability: “Fuzzy” Measurement and Its Consequences," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 4, pages 063-123, Central Bank of Chile.
    5. Claudio Borio, 2007. "Change and Constancy in the Financial System: Implications for Financial Distress and Policy," RBA Annual Conference Volume (Discontinued), in: Christopher Kent & Jeremy Lawson (ed.),The Structure and Resilience of the Financial System, Reserve Bank of Australia.
    6. David Martinez-Miera & Rafael Repullo, 2019. "Monetary Policy, Macroprudential Policy, and Financial Stability," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 809-832, August.
    7. Pamphile MEZUI-MBENG, 2012. "Cycle Du Credit Et Cycle Des Affaires Dans Les Pays De La Cemac," Cahiers du CEREFIGE 1202, CEREFIGE (Centre Europeen de Recherche en Economie Financiere et Gestion des Entreprises), Universite de Lorraine, revised 2012.
    8. Ozili, Peterson K, 2017. "Bank Loan Loss Provisions, Investor Protection and the Macroeconomy," MPRA Paper 80147, University Library of Munich, Germany.
    9. Peura, Samu & Jokivuolle, Esa, 2004. "Simulation based stress tests of banks' regulatory capital adequacy," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1801-1824, August.
    10. Abdelaziz Rouabah, 2006. "La sensibilité de l'activité bancaire aux chocs macroéconomiques : une analyse en panel sur des données de banques luxembourgeoises," BCL working papers 21, Central Bank of Luxembourg.
    11. Simona Malovana, 2018. "The Pro-Cyclicality of Risk Weights for Credit Exposures in the Czech Republic," Working Papers 2018/12, Czech National Bank.
    12. Galati, Gabriele & Hindrayanto, Irma & Koopman, Siem Jan & Vlekke, Marente, 2016. "Measuring financial cycles in a model-based analysis: Empirical evidence for the United States and the euro area," Economics Letters, Elsevier, vol. 145(C), pages 83-87.
    13. Fabrizio Fabi & Sebastiano Laviola & Paolo Marullo Reedtz, 2004. "The treatment of SMEs loans in the New Basel Capital Accord: some evaluations," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 57(228), pages 29-70.
    14. Michael Böheim, 2011. "Competition policy: ten lessons learnt from the financial crisis," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 38(3), pages 315-330, July.
    15. Kevin T. Jacques, 2008. "Capital shocks, bank asset allocation, and the revised Basel Accord," Review of Financial Economics, John Wiley & Sons, vol. 17(2), pages 79-91.
    16. Gabriele Galati & Irma Hindrayanto & Siem Jan Koopman & Marente Vlekke, 2016. "Measuring financial cycles with a model-based filter: Empirical evidence for the United States and the euro area," DNB Working Papers 495, Netherlands Central Bank, Research Department.
    17. Peura, Samu & Jokivuolle, Esa, 2003. "Simulation-based stress testing of banks' regulatory capital adequacy," Research Discussion Papers 4/2003, Bank of Finland.
    18. Peura, Samu & Jokivuolle, Esa, 2003. "Simulation-based stress testing of banks' regulatory capital adequacy," Bank of Finland Research Discussion Papers 4/2003, Bank of Finland.
    19. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    20. Michiel Bijlsma & Wim Suyker, 2008. "The credit crisis and the Dutch economy... in eight frequently asked questions," CPB Memorandum 210.rdf, CPB Netherlands Bureau for Economic Policy Analysis.

    More about this item

    Keywords

    Bank asset allocation; Basel II CAR; CRRA utility; HARA utility; stochastic interest rate; C02; C61; GE50; G23;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijfexx:v:02:y:2015:i:03:n:s2424786315500310. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscientific.com/worldscinet/ijfe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.