Abstract
This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.
Highlights
There is recently considerable evidence indicating the existence of discrete-time and persistent shifts in the conditional variance process volatility of asset returns
By allowing for di¤erent values of rL and rR, the SVEB model can capture asymmetries of stock market news on volatility function beyond those implied by leverage e¤ects of stochastic volatility models. This will result in producing patterns of stock market news impact functions (NIFs) or impulse response functions (IRFs) with higher degree of asymmetries between negative and positive values of large stock return innovations, compared to those implied by the SV model, which does not allow for breaks
This paper suggests a new stochastic volatility model which extents the standard stochastic volatility model to allow for persistent level shifts in volatility, referred to as breaks in the empirical ...nance literature
Summary
There is recently considerable evidence indicating the existence of discrete-time and persistent shifts in the conditional variance process volatility of asset (stock) returns. To overcome the above pitfalls of the intervention analysis, in this paper we suggest a parametric model of breaks in volatility function which are driven by large in magnitude stock return shocks Depending on their sign, these shocks are identi...ed by being larger (or smaller) than a positive (or negative) value of threshold parameter which can be estimated from the sample based on a search procedure. This will result in producing patterns of stock market news impact functions (NIFs) or impulse response functions (IRFs) with higher degree of asymmetries between negative and positive values of large stock return innovations, compared to those implied by the SV model, which does not allow for breaks This may explain evidence in the literature, based on measures of realized or implied volatility, indicating that volatility responses more.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
