Search for dissertations about: "conditional covariance matrix"
Showing result 1 - 5 of 7 swedish dissertations containing the words conditional covariance matrix.
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1. Modeling the covariance matrix of financial asset returns
Abstract : The covariance matrix of asset returns, which describes the fluctuation of asset prices, plays a crucial role in understanding and predicting financial markets and economic systems. In recent years, the concept of realized covariance measures has become a popular way to accurately estimate return covariance matrices using high-frequency data. READ MORE
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2. Essays on Financial Markets
Abstract : This thesis consists of five empirical essays dealing with different issues related to financial markets. Chapter 2 studies a new multivariate technique, Orthogonal GARCH, of forecasting large covariance matrices based on GARCH models. READ MORE
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3. Four Essays on Building Conditional Correlation GARCH Models
Abstract : This thesis consists of four research papers. The main focus is on building the multivariate Conditional Correlation (CC-) GARCH models. In particular, emphasis lies on considering an extension of CC-GARCH models that allow for interactions or causality in conditional variances. READ MORE
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4. Issues of multicollinearity and conditional heteroscedasticy in time series econometrics
Abstract : This doctoral thesis consists of four chapters all related to the field of time series econometrics. The main contribution is firstly the development of robust methods when testing for Granger causality in the presence of generalized autoregressive conditional heteroscedasticity (GARCH) and causality-in-variance (i.e. spillover) effects. READ MORE
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5. Essays on univariate long memory models
Abstract : This thesis consists of five papers dealing with univariate long memory modelsin time series analysis.The first paper examines the performance of information criteria when usedto determine the lag order of a long memory process. The results indicate thatinformation criteria cannot be used successfully for small sample sizes. READ MORE