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Business Analytics Seminars

The seminars are on Fridays at 11am in Room 2090, Abercrombie Building (H70), unless otherwise specified.

The seminar organiser is Professor Richard Gerlach.

Upcoming Seminars


31st Mar 2017 - 11:00 am

Venue: Rm 2090 Abercrombie Bldg H70

Speaker: Dr Simon Kwok, School of Economics; The University of Sydney

Title: A Flexible Generalised Hyberbolic Option Pricing Model and its Special Cases

We apply a generalised hyperbolic (GH) time-changed Lévy process to option pricing and examine six three-parameter special cases: the variance gamma (VG), student t, hyperbolic, normal inverse Gaussian, reciprocal hyperbolic and normal reciprocal inverse Gaussian option pricing models. Using S&P 500 Index options, we compare the GH, VG, t and Black-Scholes models. The GH model offers the best out-of-sample pricing overall, while the t model outperforms the VG model for both in-sample and out-of-sample pricing.

 

Joint work with Claudia Yeap, Simon S. Kwok, and S. T. Boris Choy


7th Apr 2017 - 11:00 am

Venue: Rm 2090 Abercrombie Bldg H70

Speaker: Dr Andrey Vasnev, Discipline of Business Analytics; The University of Sydney

Title: Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts

In applied forecasting, there is a trade-off between in-sample fit and out-of-sample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, optimal combination weights should minimize conditional mean squared error, or a conditional loss function, rather than the unconditional variance as in the commonly used framework of Bates and Granger (1969). We prove that our conditionally optimal weights lead to better forecast performance. The conditionally optimal weights support other forward-looking approaches to combining forecasts, where the forecast weights depend on the expected model performance. We show that forward-looking approaches can robustly outperform the random walk benchmark and many of the commonly used forecast combination strategies, including equal weights, in real-time out-of-sample forecasting exercises of inflation.

 

*joint work with Christopher G. Gibbs (UNSW)