Date of Degree
Econometrics | Finance
Econometrics, Machine Learning, Option pricing, Forecasting, Counterfactuals
This dissertation consists of two chapters.
Chapter 1: Volatility estimates under the risk neutral density have become a much revisited topic of interest in recent years. The density proves itself a powerful tool for sentiment analysis, since its moments provide insights about expectations in price trends. A standard procedure for its extraction utilizes artificial volatility predictions to form a dense enough grid for approximating a complete probability distribution. This paper proposes two common machine learning technique variations to produce implied volatility predictions when data is very scarce. First, a model using regularization through a variation of a generalized LASSO path combined with signal processing called ‘1 trend filtering. Second, a model averaging strategy by creating an ensemble model from weak predictors from past literature via random forests. These models suggest good interpolating capabilites under stringent conditions, hence serving as a good complement to other methodologies preferred for more abundant data sets.
Chapter 2: Synthetic control is an important tool in the set of methodologies for estimating treatment effects. It is, however, dependent on the assumption of trend stationarity. In order to relax the assumption, this paper proposes an alternative approach based on modern v techniques for automatic forecasting which learn the trend and seasonality components from the treated unit and correct them using candidate controls in the same spirit as synthetic control when candidate controls are cointegrated. Monte Carlo simulations show that this method is more robust than synthetic control in the presence of non-stationary cointegrated series, and able to identify treatment effects in a variety of forms. An empirical application reexamines the work of Abadie and Gardeazabal (2003) demonstrating the method’s ability to replicate their results.
Crespo, Pablo A., "Essays on Applied Machine Learning for Implied Volatility Interpolation and Artificial Counterfactuals" (2019). CUNY Academic Works.