Topics in financial econometrics

In the first chapter, we consider the joint estimation of objective and risk-neutral parameters for SV option pricing models. We propose a strategy which exploits the information contained in large heterogeneous panels of options, and we apply it to S&P 500 index and index call options data. Our approach breaks the stochastic singularity between contemporaneous option prices by assuming that every observation is affected by measurement error. We evaluate the likelihood function by using a MC-IS strategy combined with a Particle Filter algorithm. The second chapter examines the impact of different categories of traders on market transactions. We estimate a model which takes into account traders’ identities at the transaction level, and we find that the stock prices follow the direction of institutional trading. These results are carried out with data from an anonymous market. To explain our estimates, we examine the informativeness of a wide set of market variables and we find that most of them are unambiguously significant to infer the identity of traders. The third chapter investigates the relationship between the categories of market traders and three definitions of financial durations. We consider trade, price and volume durations, and we adopt a Log-ACD model where we include information on traders at the transaction level. As to trade durations, we observe an increase of the trading frequency when informed traders and the liquidity provider intensify their presence in the market. For price and volume durations, we find the same effect to depend on the state of the market activity. The fourth chapter proposes a strategy to express order aggressiveness in quantitative terms. We consider a simultaneous equation model to examine price and volume aggressiveness at Euronext Paris, and we analyse the impact of a wide set of order book variables on the price-quantity decision.

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.