Evaluation of Volatility Forecasting Models in Vietnam Stock Markets

This study aims to find the most appropriate model(s) to estimate and forecast volatility in Vietnam stock markets. Considered volatility models in this study include RiskMetrics, GARCH, EGARCH, IGARCH, FIGARCH and APARCH. The forecast performance evaluations are conducted with two Vietnam stock indices – VNI-index and HNX-index. Selected data periods is from 01 March 2002 to 30 June 2011 for VNI-index and the period for HNX-index spans from 01 June 2006 through 30 June 2011. Symmetric loss functions and asymmetric loss functions are used as basic analysis criteria. Robust conclusions are achieved with the superior predictive ability (SPA) test, the model confidence set (MCS) procedure and Value-at-Risk (VaR) forecast evaluation. The general empirical results generated from symmetric loss functions, the SPA test and the MCS procedure demonstrate that for VNI-index, RiskMetrics and EGARCH have equally best forecast performance while for HNX-index, only EGARCH has the best. However, there are contrast findings resulted from different assessment criteria specifically with asymmetric loss functions and VaR forecast. Actually, the ranking of models is sensitive to the selected criterion. Therefore, selecting reasonable evaluation criteria is very critical and it must be established on the ultimate aims of the forecasting procedure.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening