Stylized facts on financial time series data are the volatility of returns that follow non-normal conditions such as leverage effects and heavier tails leading returns to have heavier magnitudes of extreme losses. Value-at-risk is a standard method of forecasting possible future losses in...

This paper fits Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) models to the daily closing stock market indices of Australia, China, Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, and Taiwan to compute for time-varying weights associated with the...

The high level of hunger incidence in the country is perhaps one of the most pressing issues that need to be addressed by our policy makers. Official government statistics and data from self-rated hunger surveys show an increasing trend in hunger incidence among Filipino households. Data from...

Financial institutions hold risks in their investments that can potentially affect their ability to serve their clients. For banks to weigh their risks, Value-at-Risk (VaR) methodology is used, which involves studying the distribution of losses and formulating a statistic from this distribution....

The high poverty incidence in the county is a concern that needs to be addressed by our policy makers. Official poverty statistics from the National Statistical Coordination Board (NSCB) shows that the reduction in poverty over the past two decades has been quite dismal from 38% in 1988 to 26%...

The adoption of Basel II standards by the Bangko Sentral ng Pilipinas initiates financial institutions to develop value-at-risk (VaR) models to measure market risk. In this paper, two VaR models are considered using the peaks-over-threshold (POT) approach of the extreme value theory: (1) static...