Statistics Across Campuses

  Superhedging of options in a non-linear incomplete financial market model Date: 30 September 2021, Thursday Time: 7pm AEST Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au Speaker: Dr. Miryana Grigorova ( University of Leeds ) Abstract: We will study the superhedging price (and superhedging strategies) of European and American options in a non-linear incomplete market model with default, with a particular focus on the American options case which is more involved.  We will provide a dual representation of the seller’s (superhedging) price for the American option in terms of a mixed stochastic control/stopping problem with non-linear expectations/ evaluations, and in terms of non-linear Reflected BSDEs with constraints. If time permits, we will also present a duality result for the buyer’s price in terms of a stochastic game of control and stopping with non-linear expectations/ evaluations. Zoom meeting link: https://unimelb.zoom.us/j/86951431269?pwd=S1FPSFBHLzd

      Sampling for border biosecurity inspection Date :  Thursday, 16 September 2021 Time:   10 am  - 11 am Speaker: Dr Raphael Trouvé Abstract: The invasion of alien pests and diseases is one of the most important socio-ecological challenges worldwide. One option available to the regulator to monitor and reduce biosecurity risk associated with international trade is border inspection, that is, intercepting and stopping pests at point of entry. In this talk, we will present the classical acceptance sampling methods that underpin the design of border biosecurity inspection regimes. We will also present several challenges encountered by biosecurity analysts in practice: biosecurity data is often overdispersed, sometimes right-censored, and occasionally, the variables of interest are even unobserved. In this talk, we will present novel methods to deal with these situations and explore how, for better or worse, the different aspects of the data interact with each other.  Zoom Link:  Please

     Autoregressive Networks Date :  Thursday, 9 September 2021 Time:   10 am  - 11 am Speaker:  Associate Professor Binyan Jiang Abstract: We propose a first-order autoregressive model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as the maximum likelihood estimators which are proved to be (uniformly) consistent and asymptotically normal. The model diagnostic checking can be carried out easily using a permutation test. The proposed model can apply to any network processes with various underlying structures but with independent edges. As an illustration, an autoregressive stochastic block model has been investigated in depth, which characterizes the latent communities by the transition probabilities over time. This leads to a more effective spectral clustering algorithm for identifying the latent communities. Inference for a