# 2 UBC Statistics Students' MSc Presentations

Tuesday, April 4, 2017 - 11:00 to 12:00
Yijun Xie and Jonathan Agyeman, UBC Statistics MSc Students
Statistics Seminar
Room 4192, Earth Sciences Building (2207 Main Mall)

11:00am - 11:30am:  Yijun Xie

Title:  A Flexible Inference Method for an Autoregressive Stochastic Volatility Model with an Application to Risk Management

Abstract:  Autoregressive Stochastic Volatility (ARSV) model is a discrete-time process that can model financial returns. Existing inference methods can only be applied to the classic ARSV model. In this talk, I present the work from my master's thesis, which discusses a new inference method that allows flexible model assumptions for the ARSV model. I also present an application to risk management and compare the ARSV model with another commonly used model for financial time series, namely the GARCH model. My talk will cover the following: (1) motivation for an extension of the classic ARSV model, (2) details about the new inference method, and (3) examples of using ARSV model in risk management.

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11:30am - 12:00pm:  Jonathan Agyeman

Title:  On the choice of scoring functions for forecast comparisons

Abstract:  Forecasting of risk measures is an important part of risk management for financial institutions.  Value-at-Risk and Expected Shortfall are two commonly used risk measures and accurately predicting these risk measures enables financial institutions to plan adequately for possible losses. Point forecasts from different methods can be compared using consistent scoring functions, provided the underlying functional to be forecasted is elicitable. It has been shown that the choice of a scoring function from the family of consistent scoring functions does not influence the ranking of forecasting methods as long as the underlying model is correctly specified and nested information sets are used. However, in practice, these conditions do not hold, which may lead to discrepancies in the ranking of methods under different scoring functions.

We investigate the choice of scoring functions in the face of model misspecification, parameter estimation error and nonnested information sets. We concentrate on the family of homogeneous consistent scoring functions for Value-at-Risk and the pair of Value-at-Risk and Expected Shortfall and identify conditions required for existence of the expectation of these scoring functions. We also assess the finite-sample properties of the Diebold-Mario Test, as well as examine how these scoring functions penalize for over-prediction and under-prediction with the aid of simulation studies.