Showing 1 - 10 of 147
The quantification of risk and dependence are major components of financial risk modelling. Financial risk modelling frequenty uses the assumption of a normal distribution when considereing the return series which makes modelling easy but is inefficient if the data is not normally distributed or...
Persistent link: https://www.econbiz.de/10013090357
The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken...
Persistent link: https://www.econbiz.de/10013200547
The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and STOXX50 indices, sampled at 5-minute intervals, taken...
Persistent link: https://www.econbiz.de/10012611433
We consider a new class of time series models (introduced by Engle and Russell (1998)) used in statistical applications in finance. These models treat the time between events (durations) as a stochastic process and the corresponding durations are modelled using a theory similar to that of...
Persistent link: https://www.econbiz.de/10012735631
We consider a new class of time series models (introduced by Engle and Russell (1998)) used in statistical applications in finance. These models treat the time between events (durations) as a stochastic process and the corresponding durations are modelled using a theory similar to that of...
Persistent link: https://www.econbiz.de/10012738811
The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries....
Persistent link: https://www.econbiz.de/10012888683
The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we use the realized volatility (RV) of FTSE sampled at 5-minute intervals, taken from the Oxford Man Realised Library. Both models...
Persistent link: https://www.econbiz.de/10012859426
The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and STOXX50 indices, sampled at 5-minute intervals, taken...
Persistent link: https://www.econbiz.de/10012384599
The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken...
Persistent link: https://www.econbiz.de/10012203997
This paper features an analysis of volatility spillover effects from Australia's major trading partners, namely, China …
Persistent link: https://www.econbiz.de/10010391535