Essays in Financial Econometrics

The first paper sheds light on the informational content of high frequency data and daily data. I assess the economic value of the two family models comparing their performance in forecasting asset volatility through the Value at Risk metric. In running the comparison this paper introduces two key assumptions: jumps in prices and leverage effect in volatility dynamics. Findings suggest that high frequency data models do not exhibit a superior performance over daily data models. In the second paper, building on Majewski et al. (2015), I propose an affine-discrete time model, labeled VARG-J, which is characterized by a multifactor volatility specification. In the VARG-J model volatility experiences periods of extreme movements through a jump factor modeled as an Autoregressive Gamma Zero process. The estimation under historical measure is done by quasi-maximum likelihood and the Extended Kalman Filter. This strategy allows to filter out both volatility factors introducing a measurement equation that relates the Realized Volatility to latent volatility. The risk premia parameters are calibrated using call options written on S&P500 Index. The results clearly illustrate the important contribution of the jump factor in the pricing performance of options and the economic significance of the volatility jump risk premia. In the third paper, I analyze whether there is empirical evidence of contagion at the bank level, measuring the direction and the size of contagion transmission between European markets. In order to understand and quantify the contagion transmission on banking market, I estimate the econometric model by Aït-Sahalia et al. (2015) in which contagion is defined as the within and between countries transmission of shocks and asset returns are directly modeled as a Hawkes jump diffusion process. The empirical analysis indicates that there is a clear evidence of contagion from Greece to European countries as well as self-contagion in all countries.

Essays on Atypicality: A narrative perspective to illuminate how atypical actors can counter the disciplining effect of categories

Using Big Data and Natural Language Processing (NLP) tools, this dissertation investigates the narrative strategies that atypical actors can leverage to deal with the adverse reactions they often elicit. Extensive research shows that atypical actors, those who fail to abide by established contextual standards and norms, are subject to skepticism and face a higher risk of rejection. Indeed, atypical actors combine features and behaviors in unconventional ways, thereby generating confusion in the audience and instilling doubts about their propositions' legitimacy. However, the same atypicality is often cited as the precursor to socio-cultural innovation and a strategic act to expand the capacity for delivering valued goods and services. Contextualizing the conditions under which atypicality is celebrated or punished has been a significant theoretical challenge for scholars interested in reconciling this tension. Nevertheless, prior work has focused on audience side factors or on actor-side characteristics that are only scantily under an actor's control (e.g., status and reputation). This dissertation demonstrates that atypical actors can use strategically crafted narratives to mitigate against the audience’s negative response. In particular, when atypical actors evoke conventional features in their story, they are more likely to overcome the illegitimacy discount usually applied to them. Moreover, narratives become successful navigational devices for atypicality when atypical actors use a more abstract language. This simplifies classification and provides the audience with more flexibility to interpret and understand them.