Yazar "Hormann, Wolfgang" seçeneğine göre listele

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    Öğe
    Efficient simulations for a bernoulli mixture model of portfolio credit risk
    (Springer, 2018) Başoğlu, İsmail; Hormann, Wolfgang; Sak, Halis
    We consider the problem of calculating tail loss probability and conditional excess for the Bernoulli mixture model of credit risk. This is an important problem as all credit risk models proposed in literature can be represented as Bernoulli mixture models. Thus, we deviate from the efficient simulation of credit risk literature in that we propose an efficient simulation algorithm for this general Bernoulli mixture model in contrast to previous works that focus on specific credit risk models like CreditRisk or Credit Metrics. The algorithm we propose is a combination of stratification, importance sampling based on cross-entropy, and inner replications using the geometric shortcut method. We evaluate the efficiency of our general method considering three different examples: CreditRisk and two of the latent variable models, the Gaussian and the t-copula model. Numerical results suggest that the proposed general algorithm is more efficient than the benchmark methods for these specific models.
  • [ X ]
    Öğe
    Efficient stratified sampling implementations in multiresponse simulation
    (Ieee, 2014) Başoğlu, İsmail; Hormann, Wolfgang
    Often the accurate estimation of multiple values from a single simulation is of practical importance. Among the many variance reduction methods known in the literature, stratified sampling is especially useful for such a task as the allocation fractions can be used as decision variables to minimize the overall error of all estimates. Two different classes of overall error functions are proposed. The first, including the mean squared absolute and the mean squared relative error, allows for a simple closed-form solution. For the second class of error functions, including the maximal absolute and the maximal relative error, a simple and fast heuristic is proposed. The application of the new method, called "multiresponse stratified sampling", and its performance are demonstrated with numerical examples.