Non-stationarity dynamics of asset comovement

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Campos de Carvalho, Pablo Jose
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In this dissertation, we contribute to the risk and asset pricing literature by investigating causes and effects of non-stationarity of financial time series. We propose a network-based framework to analyze structural and dynamic changes of how asset prices are related. We also propose an asset pricing model to mitigate the effects of correlation changes in portfolio allocation. Since the topic of non-stationarity often comes to surface in light of international financial crisis, we also investigate empirically how financial market integration affects comovement in international equity markets.
Evidence of nonstationarity of comovement in international equity markets has been widely documented in the finance literature. At the same time, an increasingly integrated financial system has raised suspicion that banking crises lead to increased systemic risk. We tie these two ideas and investigates the role of cross-border interbank exposures in explaining comovement between international equity indices. We find that higher financial market integration explains correlation between international equity indices. Likewise, directional cross-border banking exposures increases comovement in international equity market.
Asset prices exhibit periods of normality and others of extreme observations, invalidating the traditional diffusion representation. Additionally, there is some evidence of jumps in one market preceding jumps in other markets. We build on the network-based framework that we developed and propose a multivariate jump diffusion model with Markovian contagion to capture asset price dynamics. We assume that channel of contagion between two financial instruments may take two possible states: connected or disconnected. We use a dynamic condition correlation network approach to determine these states. We apply this model in an international cross asset class portfolio allocation example and assess its implications to the diversification effect.
In many fields, networks have been used to filter information and describe connected systems. We build on the minimum spanning tree (MST) literature for developing a layered MST that uses a multi-factor model to explain the dynamic dependencies among elements using systematic and idiosyncratic components of asset prices. This framework proves to be flexible with changes in the underlying data and the choice of factors for the investigation. We show applications of our framework in different contexts and observe that the methodology is helpful in understanding the change of the interdependencies among entities in a dataset. Using this approach we are able to identify dramatic changes in the topology of asset prices networks.
December 2016
School of Management
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Rensselaer Polytechnic Institute, Troy, NY
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