Forecasting gains by using extreme value theory with realised GARCH filter

Forecasting Value-at-risk by Using Garch Models

Extreme Value GARCH modelling with Bayesian Inference

Extreme value theory is widely used financial applications such as risk analysis, forecasting and pricing models. One of the major difficulties in the applications to finance and economics is that the assumption of independence of time series observations is generally not satisfied, so that the dependent extremes may not necessarily be in the domain of attraction of the classical generalised ex...

Bayesian Tail Risk Forecasting using Realised Volatility DCC-Copula-GARCH Models

A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio Value at Risk and Conditional Value at Risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using tradi...

Demand-Side Management Program Planning Using Stochastic Load Forecasting with Extreme Value Theory

Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describe...

Auditing Journal Entries Using Extreme Value Theory

While a wealth of statutory and auditing pronouncements attest to the importance of the auditing of journal entries for preventing and detecting material misstatements to financial statements, existing literature has so far paid inadequate attention to this line of research. To explore this line of research further, this paper proposes a bipartite model that is based on extreme value theory and...