Status : Verified
| Personal Name | Villareal, Laurence M. |
|---|---|
| Resource Title | Estimating Financial System Sentiment Using Hierarchical Dynamic Factor Models |
| Date Issued | June 2026 |
| Abstract | This study constructs a daily Philippine financial system sentiment index using a hierarchical dynamic factor model (HDFM) estimated via the Kalman expectation-maximization algorithm. The index synthesizes 22 indicators spanning the banking, equities, fixed income, and external segments of the Philippine financial system into a composite system-wide sentiment measure and four sectoral sub-indices, covering the period January 2016 to March 2026. The modeling framework accommodates the mixed-frequency and ragged-edge structure of the indicator panel — with observation frequencies ranging from daily to quarterly. The extracted composite index captures the two most significant stress episodes within the sample — the COVID-19 onset in March 2020 and the 2022 global monetary tightening cycle. The sentiment index represents the first application of the Moench–Ng–Potter hierarchical factor framework to an emerging market financial system and constitutes a novel daily-frequency macroprudential surveillance tool for the Bangko Sentral ng Pilipinas. |
| Degree Course | Master of Statistics |
| Language | English |
| Keyword | financial sentiment index, hierarchical dynamic factor model, Kalman filter, mixed-frequency data, macroprudential surveillance, Philippines |
| Material Type | Thesis/Dissertation |
Preliminary Pages
10.87 Mb
Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access