Estimates and Forecasts of GARCH Model under Misspecified Probability Distributions: A Monte Carlo Simulation Approach

The effect of misspecification of correct sampling probability distribution of Generalized Autoregressive Conditionally Heteroscedastic (GARCH) processes is considered. The three assumed distributions are the normal, Student t, and generalized error distributions. The GARCH process is sampled using one of the distributions and the model is estimated based on the three distributions in each sample. Parameter estimates and forecast performance are used to judge the estimated model for performance. The AR-GARCH-GED performed better on the three assumed distributions; even, when Student t distribution is assumed, AR-GARCH-Student t does not perform as the best model

Misspecification of Variants of Autoregressive GARCH Models and Effect on In-Sample Forecasting

Generally, in empirical financial studies, the determination of the true conditional variance in GARCH modelling is largely subjective. In this paper, we investigate the consequences of choosing a wrong conditional variance specification. The methodology involves specifying a true conditional variance and then simulating data to conform to the true specification. The estimation is then carried out using the true specification and other plausible specification that are appealing to the researcher, using model and forecast evaluation criteria for assessing performance. The results show that GARCH model could serve as better alternative to other asymmetric volatility models

Access to Justice software development, Participatory Action Research Methods and Researching the Lived Experiences of British Military Veterans

Participatory action research (PAR) methods aim to position the people who are most affected by the issue being studied as equal partners in the research process through a cyclical process of data gathering, data analysis, planning and implementing action and evaluation and reflection. In doing so, it ensures that the research better reflects participants’ ideas, priorities, and needs, thereby enhancing its validity and relevance and the support for the findings and proposed changes. Furthermore, it generates immediately applicable results. In this paper, we reflect on our experiences of developing the UK’s first access to justice platform for veterans and their families through an ongoing PAR project that brought together armed forces veterans, representatives from veterans' service providers, and the Veterans Legal Link team members comprising of legal academics, lawyers, sociologists, computer software designers and graphic designers to collect, interpret, and apply community information to address issues related to the delivery of access to justice. We present findings from Stages 1 and 2 of our three-stage iterative research process which includes the following steps: Understanding and cross-checking the lived experience of the veteran community (Stage 1), developing and testing a prototype of the access to justice platform (Stage 2) and creating the final product and giving real users an opportunity to use the platform (Stage 3). Data collection and analysis from Stage 1 of the study informed the themes that underpinned Stage 2. Specifically, data was collected through the following methods: co-facilitated focus group discussions, a web survey that was codesigned with veteran community stakeholders and remote and digitally enabled ethnographic research methods. We include several reflections that may help legal practitioners and researchers interested in applying PAR within the area of access to justice and the field of legal research