This thesis consists of two independent essays on financial econometrics. The first study introduces a new family of portmanteau tests for serial correlation. Using the wavelet transform, we decompose the variance of the underlying process into the variance of its low frequency and of its high frequency components and we design a variance ratio test of no serial correlation in the presence of dependence. Such decomposition can be carried out iteratively, each wavelet filter leading to a rich family of tests whose joint limiting null distribution is a multivariate normal. We illustrate the size and power properties of the proposed tests through Monte Carlo simulations. The second study focuses on counterparty risk and its role as a determinant of corporate credit spreads. However, there are only a few techniques available to isolate it from other factors. In this paper we describe a model of financial networks that is suitable for the construction of proxies for counterparty risk. Using data on the U.S. supplier-customer network of public companies, we find that, for each supplier, counterparties' leverage and jump risk are significant determinants of corporate credit spreads. Our findings are robust after controlling for several idiosyncratic, industry, and market factors.
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Thesis advisor: Gencay, Ramazan
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