This dissertation explores the application of quantitative modeling to analyze the features of different financial assets. This study investigates the dynamics of financial assets using advanced mathematical, statistical approaches, and Machine Learning method. We aimIn the first chapter, we provide empirical analysis on Fixed Index Annuity (FIA) and Fixed Index Linked Annuity (FILA) with insights on utility gains of different types of investors. As one of the most recent financial product in the market, we find that this financial asset provides higher and secured returns for investors and could be an alternative investment especially in the era of low yield market. We also construct a multi-period utility framework to model the utility preferences which provide many intuitive findings in the insurance industry. We investigate the conditional betas in the U.S. stock market based on individual stocks. In this study, we not only use econometric modeling but also Machine Learning approach to capture the conditional betas in stock market. Different from previous literature, we include a comprehensive list of variables to model time-varying betas and examine the asset pricing models such as Capital Asset Pricing Model (CAPM), Fama French models, and Q5 models from the asset pricing model tests perspective. As one of the most important investment in the market, Real Estate Investment Trusts (REITs) has been constantly regarded as a diversification investment for many fund managers. Especially in the 20008 financial crisis and 2020 Covid pandemic, REITs has played an important role and achieved defensive role in the portfolio optimization. The findings of this dissertation contribute to the understanding of quantitative modeling in finance and offer practical implications for asset pricing, risk management and investment. In summary, these essays provide intuitive, economically insightful and interesting findings on financial modeling for wide applications. at contributing to the field of finance in empirical asset pricing, risk management, and investment.