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Agnieszka Borowska, Vrije Universitet Amsterdam

Date: February 5

Start time: 10.15

End time: 11.45

Location: Economics conference room 730, SSE, Bertil Ohlins Gata 4, 7th fl.

Job Market Paper: “Bayesian Risk Evaluation for Long Horizons”

Abstract: We present an accurate and efficient method for Bayesian estimation of two financial risk measures, Value at Risk and Expected Shortfall, for a given volatility model. We obtain precise forecasts of the tail of the distribution of returns not only for the 10-days-ahead horizon required by the Basel Committee but even for long horizons, like one-month or one-year ahead. The latter has recently attracted considerable attention due to the different properties of short term risk and long run risk. Precise forecasts of the tail of the distribution can also be useful for option pricing. The key insight behind our proposed importance sampling based approach is the sequential construction of marginal and conditional importance densities for consecutive periods. For robustness, these importance densities are efficiently constructed as mixtures of Student’s t densities. By oversampling the extremely negative scenarios and giving them lower importance weights, we achieve a much higher precision in characterising the properties of the left tail. We report substantial accuracy gains for all the considered horizons in empirical studies on two datasets of daily financial returns, including a highly volatile period of the recent financial crisis. We analyse two workhorse models used by financial practitioners, GARCH(1,1)-t and GAS(1,1)-t. To illustrate the flexibility of the proposed construction method, we present how it can be adjusted to the frequentist case, for which we provide counterparts of both Bayesian applications.

The paper can be downloaded here https://aborowska.github.io/research/A.Borowska%20-%20Bayesian%20Risk%20Evaluation%20for%20Long%20Horizons.pdf

To find out more about Ali Habibnia, visit here website https://aborowska.github.io/

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