We study neural networks as nonparametric estimation tools for the hedging of options. To
this end, we design a network, named HedgeNet, that directly outputs a hedging strategy …

This study reexamines whether option price monotonicity properties hold in a liquid market
with little market friction and considers the validity of the monotonicity properties in light of …

We study how the equilibrium risk sharing of agents with heterogeneous perceptions of
aggregate consumption growth affects bond and stock returns. Although credit spreads and …

This study demonstrates that the basic properties predicted by one‐dimensional diffusion
option pricing models are often violated, even in a highly liquid and leading options market …

This study empirically tests whether price violations, as defined by Bakshi, Cao, and Chen
(2000), show different patterns in response to market shocks. Specifically, we analyze the …

We re-examine the monotonicity violations of option price dynamics considering the roles of
market depth and domestic investors. Violations caused by option price movements in …

The model offers a simple structural explanation for the positive empirical link between the
volatility of stock returns, the implied volatility of individual stock options, and corporate credit …

Many option pricing models are based on the assumption that the underlying asset price
follows one-dimensional diffusion process. An alternative approach is to test the properties …

In the literature, microstructure effects have been documented as determinants of the
violations of the monotonicity property. In this article, we argue that in an order-driven marker …

This thesis investigates the problem of statistical hedging with artificial neural networks
(ANNs). The statistical hedging is a data-driven approach that derives hedging strategy from …