To be well received in a financial econometrics journal, you want test-based approaches. Depending on your question it is common to see a linear regression (least squares) where the parameter suspected of breaking is interacted with an indicator function $I(E)$ where $E$ is the event in question; this function assumes a unit value when $E$ occurs. This is almost always specified exogenously; see e.g. the contagion literature.
This is less common but in some applications a VAR or VECM is what you want. You will want to review Joyeux (2007) for various test based breaking models within a VECM framework. Note that data generating process of your model is going to be restricted simply because the asymptotics haven't been worked out for many alternatives - this logically follows from how recent Johansen's framework is as well as the complexity of VARs/VECMs versus completely linear single equation models. It may be interesting to look at the impulse response functions of the models in different regimes.
Endogenous approaches also fall under test based approaches. Here it is typical to analyse the residuals of an unbroken model and to determine the time location where the residuals is statistically 'large'.
Another typical approach is to fit a market model around the event and do a test on the cumulative abnormal returns. There is a dauntingly deep literature on how to get an unbiased test statistic in this instance. You may want to look into the 1980s paper by Sefcik and Thompson and GLS/WLS derivatives of this paper. They provide a dummy breakpoint framework which is statistically equivalent to the CAR approaches, and in my opinion more interpretable.
- Contagion literature (truly gigantic).
- Financial integration literature (stock and bond markets).
Non test based
These are harder to publish: event studies basically need a test. But to get a truly thorough idea of what's happening - much better than a test in my opinion in all real world applications - to all your time series around the event, look at a power spectrum of your time series. This is done with the click of a button in the
biwavelet package in
R (there are around 10 others).
This is for qualitative understanding - no official test comes from this. It is therefore not likely to publish (unless you're going for econophysics journals such as Physica A where you might not get an economist reviewer).