Daniel K Dimitrov

I’m a PhD graduate from the University of Amsterdam with research interest in the area of macro-finance, asset pricing, portfolio allocation and risk management. I’m currently on the job market. You can contact me at daniel.k.dimitrov@gmail.com.

My PhD thesis “Three essays on the optimal allocation of risk” discusses topics of illiquidity, intergenerational risk sharing and builds an approach for the measurement of systemic risk for financial institutions. My PhD supervisors were Prof. Roel Beetsma and Prof. Sweder van Wijnbergen.

You can find here my job market paper, entitled “Macroprudential Regulation: A Risk Management Approach”.

Workikg papers

  • Quantifying Systemic Risk in the Presence of Unlisted Banks: Application to the Dutch Financial Sector. link, slides

  • Intergenerational Risk Sharing with Market Liquidity Risk. (joint with Sweder wan Wijnbergen) link, slides

  • Macroprudential Regulation: A Risk Management Approach. (joint with Sweder wan Wijnbergen) link, slides, code

Ongoing projects

  • Strategic Asset Allocation with Private Assets: Untangling Illiquidity. slides

    We use a dynamic portfolio choice model to approach the problem of optimal asset allocation with illiquid assets. Illiquidity comes in the form of uncertain trading potential and the inability to adjust the investor’s allocation to strategic targets. Along with the traditional asset mix of bonds and equity, we also allow investments in private asset classes such as hedge funds, private equity, direct real estate, and infrastructure. Despite the endemic illiquidity of these asset classes, we illustrate quantitatively that they improve significantly long-term investors’ welfare by providing diversification and return potential.

  • Quantifying Systemic Risk in the Presence of Unlisted Banks: Application to the European Banking Sector. (joint with Sweder wan Wijnbergen)

    We propose a credit portfolio approach for evaluating systemic risk and attributing it across institutions. We construct a model that can be estimated from high-frequency CDS data. This captures risks from publicly traded banks, privately held institutions, and coöperative banks, extending approaches that rely on information from the public equity market only. We account for correlated losses between the institutions, overcoming a modeling weakness in earlier studies. We also offer a modeling extension of the standard credit factor model to account for fat tails and skewness of asset returns. We apply the model to a universe of European banks and relate our estimates to the capital buffers set by regulators.