In this paper, we propose two practicable approaches for consistently modelling the realworld and risk-neutral measures within cross-asset Monte-Carlo frameworks. We go on to explore the necessity of supporting the real-world measure and consider its calibration with the aid of an explicit...

Using daily data, this paper empirically investigates the price discovery and information transmission in China's stock index futures and spot markets based on a VAR-GARCH model with SSAEPD margins. By comparing our model with classic VAR-GARCH model, we discover that our model can better...

By exploiting the flexibility of the Wishart process, we propose an application of this framework to the pricing of Chicago Board Options Exchange (CBOE) volatility index (VIX) options. Our methodology is analytically tractable and yet flexible enough to efficiently price CBOE VIX options. In...

Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or...

In this paper we propose a novel flexible framework based on time changed Lévy process for the joint evolution of stock log-returns and their volatility with the aim of analysing which risk factors and which distribution features provide a robust calibration, repricing and hedging performance....

Empirical studies of structural credit risk models so far are often based on calibration, rolling estimation, or regressions. This paper proposes a GMM-based method that allows us to both consistently estimate the model parameters and test whether all the restrictions of the model are satisfied....

A credit derivative is a path dependent contingent claim on the aggregate loss in a portfolio of credit sensitive securities. We estimate the value of a credit derivative by Monte Carlo simulation of the affine point process that models the loss. We consider two algorithms that exploit the...

I develop variations of three default-risk determinants of structural corporate bond models. The three determinants are the asset return distribution law, recovery rule, and default time. First, I replace the Gaussian distribution of an asset return with three alternative non-Gaussian...

This paper introduces a new semi-parametric methodology for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor...

This paper examines the intertemporal relation between expected return and risk for 30 stocks in the Dow Jones Industrial Average. The mean-reverting dynamic conditional correlation model of Engle (2002) is used to estimate a stock's conditional covariance with the market and test whether the...