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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Article
Coleman Fung Risk Management Research Center Working Papers 2006-2013 (2013)
    Cover page: Review of "Counterparty Credit Risk by Jon Gregory"
    • Article
    Coleman Fung Risk Management Research Center Working Papers 2006-2013 (2013)

    Counterparty credit risk (CCR), a key driver of the 2007-08 credit crisis, has become one of the main focuses of the major global and U.S. regulatory standards. Financial institutions invest large amounts of resources employing Monte Carlo simulation to measure and price

    their counterparty credit risk. We develop efficient Monte Carlo CCR frameworks by focusing on the most widely used and regulatory-driven CCR measures: expected positive exposure (EPE), credit value adjustment (CVA), and effective expected positive exposure (eEPE). Our numerical examples illustrate that our proposed efficient Monte Carlo estimators outperform the existing crude estimators of these CCR measures substantially in terms of mean square error (MSE). We also demonstrate that the two widely used sampling methods, the so-called Path Dependent Simulation (PDS) and Direct Jump to Simulation date (DJS), are not equivalent in that they lead to Monte Carlo CCR estimators which are drastically different in terms of their MSE.

      Cover page: Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement
      • Article
      Coleman Fung Risk Management Research Center Working Papers 2006-2013 (2012)
        Cover page: Stochastic Intensity Models of Wrong Way Risk: Wrong Way CVA Need Not Exceed Independent CVA
        • Article
        Coleman Fung Risk Management Research Center Working Papers 2006-2013 (2012)
        Asmussen-Kroese [1] Monte Carlo estimators of P(Sn > u) and P(SN > u) are known to work well in rare event settings when Sn is the sum of n i.i.d. heavy-tailed random variables, and N is a non-negative integer-valued random variable independent of the Xi. In this paper we show how to improve the Asmussen-Kroese estimators of both probabilities when the Xi are non-negative. We also apply our ideas to estimate the quantity E[(SN 􀀀 u)+].
          Cover page: Improving the Asmussen-Kroese Type Simulation Estimators
          • Article
          Coleman Fung Risk Management Research Center Working Papers 2006-2013 (2012)
          The normalized importance sampling estimator allows the target density f to be known only up to a multiplicative constant. We indicate how it can be derived by a delta method based approximation of a Rao-Blackwellized acceptance rejection estimator. Using additional terms in the delta method then results on a new estimator that also only requires f to be known only up to a multiplicative constant. Numerical examples indicate that the new estimator usually outperforms the normalized importance sampling estimator in terms of mean square error.
            Cover page: Improving the Normalized Importance Sampling Estimator
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