Quantitative Risk Management: Volatility Modeling

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Explores risk and return in finance, covering securities' performance, rates of return, variance, volatility, and portfolio diversification.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Explores long memory in time series and ARCH models for financial volatility.

Covers the concept of multiperiod binomial pricing and the Black-Scholes formula.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explores forecasting in time series analysis, long memory processes, and ARCH models for volatility modeling.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Covers statistical tools, risk models, and challenges in quantitative risk management.

Explores the tradeoff between risk and return in portfolios, the benefits of diversification, and the impact of correlation on portfolio risk.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.