We propose a new method for pricing options based on GARCH models with filtered historical innovations. In an incomplete market framework we allow for different distributions of the historical and the pricing return dynamics enhancing the model flexibility to fit market option prices. An...

By examining the gold leasing market and employing data on the gold forward offered rate (GOFO) and derived lease rates, we propose that rather than using the interest-adjusted basis as a proxy for the convenience yield of gold, the convenience yield is better approximated by the derived gold...

The dynamics of the physical probability of firms that undertake a stock swap merger is developed through a simple model. Using a sample of 1090 deals from 1992 to 2008, we show how movements in target stock prices are informative of the success or failure of a stock swap merger and how...

We propose a model able to estimate the risk of assets in balance from aggregate data by introducing a prudential measure called Filtered Historical Spectral Asset Measure (FH - SAM). Our measure combines a model based method to simulate the evolution of volatility with model free method of...

In the existing literature on barrier options, much effort has been exerted to ensure convergence through placing the barrier in close proximity to, or directly onto, the nodes of the tree lattice. In this paper we show that this may not be necessary to achieve accurate option price...

We provide a new method to derive the state price density per unit probability based on option prices and GARCH model. We derive the risk neutral distribution using the result in Breeden and Litzenberger (1978) and the historical density adapting the GARCH model of Barone-Adesi, Engle, and...

Regulators charged with monitoring systemic risk need to focus on sentiment as well as narrowly defined measures of systemic risk. This chapter describes techniques for jointly monitoring the co-evolution of sentiment and systemic risk. To measure systemic risk, we use Marginal Expected...