Essays in financial econometrics


This dissertation consists of three chapters discussing issues in the field of financial econometrics. All three chapters are largely empirical, with some theoretical developments in second moments modelling in the second chapter.
The first chapter of this thesis analyses the market neutrality of Pairs Trading, a statistical arbitrage trading technique, from a second moments perspective. In this study, I analyse how market and idiosyncratic news affect the profitability of this trading strategy. I propose a conditional covariance framework based on Kroner and Ng (1998) extension of the BEKK Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to analyse the dependence of second moments between different portfolios of pairs and the returns of the market. In contradiction to what is generally assumed about the market neutrality of this strategy, the results indicate the existence of significant spillovers from market news to different portfolios of pairs. In a second step of the study, I analyse the contribution of both idiosyncratic and market components to pairs volatility over time in an asynchronous panel of pairs. This analysis shows that the volatility of the pairs strategy has become more dependent on idiosyncratic rather than market shocks. In this sense, although Pairs Trading cannot be said to be market neutral from a second moments perspective if we look at the full sample from 1962 to 2018, the strategy has certainly become more market neutral as markets have evolved over the last two decades.
In the second chapter of this thesis, Susana Campos-Martins (Oxford) and I propose an econometric framework that explains the Purchasing Power Parity (PPP) Puzzle as common volatility shocks. Most of the discussion about the PPP Puzzle of Rogoff (1996) has pertained to the reversion speed of deviations from PPP. Much less attention, however, has been given to the other component of the puzzle: the high volatilities of real exchange rates. In this paper, we provide a framework that is capable of explaining the econometric sources of these volatilities. First, we study the drivers of real exchange rate volatilities using a Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) panel framework and the conditional covariance matrices of the system with nominal exchange rates and price differentials. This analysis indicates that, for both emerging and developed markets, common factors are the main drivers of volatility. With this result in hand, we propose a novel econometric framework that explains the sources of these volatilities as common second moment shocks. This model allows us to gives structure to the origins of these high volatilities and propose an extension to study their macrofinancial drivers.
The third and final chapter of this thesis is an adapted version of a current IMF working paper which introduces the IMF Soft Power Index. In this chapter, Serhan Cevik (IMF) and I introduce a new composite Global Soft Power Index (GSPI) composed of six dimensions for a broad sample of 72 countries across the world over the period 2007-2019. The proposed framework allows for comparisons not only at the “headline” level of the GSPI, but also at the level of the sub-indices, which allows us to identify and study how countries differ at a granular level of soft power. In a final step of the analysis, we present a possible macro-financial application to analyse the relationship between soft power and the volatility of Real Effective Exchange Rates (REER) across countries and over time. The results indicate that some dimensions of the GSPI play an important role in explaining real exchange rate volatility at almost all significance levels. Overall, our framework presents a systematic approach to measure soft power and its dimensions. By capturing the matrix of soft power characteristics, the GSPI offers significant advantages in comparative analysis of soft power across countries and over time.

Advisor: Sheppard, K