Value at Risk (VaR) is a popular measure for quantifying the market risk that a financial institution faces into a single number. Due to the complexity of financial markets, the risks associated with a portfolio may vary over time. For accurate VaR estimation, it is necessary to have flexible methods that adapt to the underlying data distribution. In this paper, we consider VaR estimation by us...

This paper argues that inferring long-horizon asset-return predictability from the properties of vector autoregressive (VAR) models on relatively short spans of data is potentially unreliable. We illustrate the problems that can arise by re-examining the findings of Bekaert and Hodrick (1992), who detected evidence of in-sample predictability in international equity and foreign exchange markets...

This paper examines the finite sample accuracy of impulse response functions in VAR models when cointegration is present but not imposed in estimation. It finds that in typical applications there can be substantial biases at the short horizon, as well as the long. These results suggest that, in contrast to common practice, before conducting impulse response analysis researchers should analyse t...

This paper characterizes the term structure of risk measures such as Value at Risk (VaR) and expected shortfall under different econometric approaches including multivariate regime switching, GARCH-in-mean models with student-t errors, two-component GARCH models and a non-parametric bootstrap. We show how to derive the risk measures for each of these models and document large variations in term...

This paper compares the performance of three alternative models in forecasting housing supply in the Irish Republic. The results highlight key behavioural issues in the dynamics of housing supply that the OLS and VAR models fail to adequately capture due to the inclusion of fundamental variables in their specification. The behaviour of developers in delaying projects means that housing supply c...

9:55 – 10:40 Rodney Strachan (University of Queensland) Reducing Dimensions in Large Time-varying Parameter VAR Models This paper proposes a new approach to estimating high dimensional time varying parameter vector autoregressive models (TVP-VARs). Such models are rarely used with more than 4-5 variables. However recent work has shown the advantages of modelling VARs with large numbers of varia...

2D Free-surface hydraulic modeling tools are commonly used to assess flood hazard for production of maximal water depth (hmax) maps, as support for flood risk assessment. High Resolution (HR) topographic data are big data getting commonly available and used by hydraulic modeling community. Topographical information and its strategy of inclusion in models, are inputs of great importance for over...

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on or with methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for ...