This dissertation provides the developments and extensions of methodologies in time series econo-metrics and their financial applications. Chapter 1 of the disseration introduces and summarizes the following chapters. Chapter 2 develops an estimating equation approach to construct confidence intervals for autocorrelation functions for time series with general stationary serial correlation structures. Its empirical application using S&P 500 index returns shows that conclusions about market efficiency and volatility clustering during pre and post-Covid periods using the estimating equation approach contrast with conclusions using traditional (and often incorrectly used) methods. Chapter 3 develops fixed-? asymptotics results for heteroskedasticity autocorrelation robust (HAR) Wald tests for regressions for high frequency data using an existing continuous time framework. Its empirical application suggests that the validity of the uncovered interest parity hypothesis depends on whether normal or fixed-b critical values are used. Chapter 4 investigates the distribution of realized US corporate bond return volatility using a compound poisson process setting and realized volatility.
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