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Essays in Empirical Asset Pricing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Essays in Empirical Asset Pricing./
Author:	Lanza, Ariel Aldo Giovanni.
Description:	1 online resource (151 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Finance. -
Online resource:	click for full text (PQDT)
ISBN:	9798382762739

Essays in Empirical Asset Pricing.
Lanza, Ariel Aldo Giovanni.

Essays in Empirical Asset Pricing. - 1 online resource (151 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Northwestern University, 2024.

Includes bibliographical references

Accurate forecasting of the implied volatility surface is crucial for various market participants, including asset managers and market makers, as it enables effective risk management and the identification of opportunities. Additionally, improving the estimation of its evolution is essential for distinguishing between external shocks and movements inherent to the underlying dynamics, preventing researchers from misclassifying predictable market fluctuations as anomalies.Traditionally, linear models and affine jump-diffusion processes have dominated the study of the implied volatility surface. However, emerging evidence suggests nonlinear dynamics, challenging conventional approaches. Given its high dimensionality (due to the discretization of moneyness and time to maturity), standard nonparametric techniques like kernel regressors face limitations because of the curse of dimensionality. A growing body of literature points to neural networks as ideal candidates for efficiently capturing the effects of an underlying lower-dimensional structure. This hypothesis aligns with the economic principle of parsimony modeling complex systems with minimal explanatory variables.This work applies recent advancements from the modern literature on Machine Learning to forecast the implied volatility surface. In doing so, it highlights how recent developments can enrich the toolkit of financial researchers by integrating techniques proven successful in other fields. Furthermore, through a review of neural network applications in finance and the recent innovations in AI, this work highlights how the establishment of standardized benchmarks and collaborative tools has been fundamental in the advancement of AI in statistics and suggests that similar initiatives could be beneficial for financial forecasting.The second chapter of this work focuses on vertical options spreads. Despite their apparent simplicity, vertical spreads can provide new insights into the market dynamics and risk premia. By exploring the concept of constructing maximum growth portfolios, the aim is to more accurately assess the inherent "put premium'' in out-of-the-money put options. Furthermore, this work suggests the construction of a liquidity index tailored to options markets. This index could leverage the turnover inherent in options trading and mitigate the difficulties arising from tail risks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382762739Subjects--Topical Terms:

559073
Finance.
Subjects--Index Terms:

Asset pricingIndex Terms--Genre/Form:

554714
Electronic books.

Essays in Empirical Asset Pricing.
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520 $a Accurate forecasting of the implied volatility surface is crucial for various market participants, including asset managers and market makers, as it enables effective risk management and the identification of opportunities. Additionally, improving the estimation of its evolution is essential for distinguishing between external shocks and movements inherent to the underlying dynamics, preventing researchers from misclassifying predictable market fluctuations as anomalies.Traditionally, linear models and affine jump-diffusion processes have dominated the study of the implied volatility surface. However, emerging evidence suggests nonlinear dynamics, challenging conventional approaches. Given its high dimensionality (due to the discretization of moneyness and time to maturity), standard nonparametric techniques like kernel regressors face limitations because of the curse of dimensionality. A growing body of literature points to neural networks as ideal candidates for efficiently capturing the effects of an underlying lower-dimensional structure. This hypothesis aligns with the economic principle of parsimony modeling complex systems with minimal explanatory variables.This work applies recent advancements from the modern literature on Machine Learning to forecast the implied volatility surface. In doing so, it highlights how recent developments can enrich the toolkit of financial researchers by integrating techniques proven successful in other fields. Furthermore, through a review of neural network applications in finance and the recent innovations in AI, this work highlights how the establishment of standardized benchmarks and collaborative tools has been fundamental in the advancement of AI in statistics and suggests that similar initiatives could be beneficial for financial forecasting.The second chapter of this work focuses on vertical options spreads. Despite their apparent simplicity, vertical spreads can provide new insights into the market dynamics and risk premia. By exploring the concept of constructing maximum growth portfolios, the aim is to more accurately assess the inherent "put premium'' in out-of-the-money put options. Furthermore, this work suggests the construction of a liquidity index tailored to options markets. This index could leverage the turnover inherent in options trading and mitigate the difficulties arising from tail risks.
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