A5021016988- Financial Risk and Volatility Modeling
- Market Dynamics and Volatility
- Bayesian Methods and Mixture Models
- Statistical Methods and Inference
- Insurance, Mortality, Demography, Risk Management
- Advanced Statistical Modeling Techniques
- Bayesian Modeling and Causal Inference
- Probabilistic and Robust Engineering Design
- Stochastic processes and financial applications
- Model Reduction and Neural Networks
- Complex Systems and Time Series Analysis
- Terrorism, Counterterrorism, and Political Violence
- Monetary Policy and Economic Impact
Capital University of Economics and Business
2023-2025
We apply flexible multivariate dynamic models to capture the dependence structure of various US commodity futures across different sectors between 2004 and 2022; particular attention is paid 2008 financial crisis COVID-19 pandemic. Our copula-based allow for time-varying nonlinear asymmetric by integrating elliptical skewed copulas with conditional correlation (DCC) block equicorrelation (Block DECO). Flexible copula that asymmetry tail are found provide best performance in characterizing...
We propose a novel bootstrap procedure for conducting inference factor model-based average treatment effects estimators. Our method overcomes bias inherent to existing procedures and substantially improves upon large sample normal theory in small settings. The finite improvements arising from the use of our proposed are illustrated via set Monte Carlo simulations, formal justification is outlined.
AbstractWe consider nonparametric kernel estimation of density functions in the bounded-support setting having known support [a,b] using a boundary-adaptive function and data-driven bandwidth selection, where b are finite prior to estimation. We observe, theoretically sample settings, that when bounds priori this approach is capable outperforming even correctly specified parametric models, case uniform distribution. demonstrate result has implications for modelling range densities other than...