This study uses a comprehensive data set of VIX and CDS markets to propose pairs trading strategies that represent the dynamic relation between market risk and credit risk in an equilibrium framework with a common non stationary factor. This involves the analysis of price discovery between VIX...

We argue that Islamic principles, in particular the avoidance of ribā and gharar should be applied with respect to real economic value rather than to monetary value in terms of conventional currency. In order to reconcile monetary value with economic value, we propose a reference currency...

This paper uses an exclusive proprietary data set of European Credit Derivatives and VIX markets, covering a sample of 5 to 7 years, to study the nature of the link between credit risk and market risk, widely acknowledged in the academic literature. This allows us to establish cointegration in...

Artificial Neural Networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between...

We test a theory that provides a simple and robust linkage between the market prices of credit default swaps (CDS) and far out-of-the-money equity American put options on the same reference company. The linkage is established under a general class of stock price dynamics. We assume that the...

With a portfolio of options on Samp;P 500 index, the Chicago Board of Options Exchange constructs a volatility index named VIX that approximates the 30-day return variance swap rate on the index. Using high-frequency return data, researchers have proposed various return quadratic variation...

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We...

This paper develops a new top-down valuation framework that links the pricing of an option investment to its daily profit and loss attribution. The framework uses the Black-Merton-Scholes option pricing formula to attribute the short-term option investment risk to variations in the underlying...

We develop a simple robust link between deep out-of-the-money American put options on a company's stock and a credit insurance contract on the company's bond. We assume that the stock price stays above a barrier B before default but drops below a lower barrier $A$ after default, thus generating...