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 (1) introduces a modified-Machlup (mM) assessment, as well as DQA and DQE concerns and a DQXY Thesis that tie the mM assessment to the Duhem-Quine (DQ) Thesis and theories of model validation (TMV), and then (2) applies the mM and DQA/DQE concepts to three archetypal models (i.e.,...

We use a 3-factor Regime Switching Threshold model to study common factors in the excess returns of 18 European corporate bond indices during 2000-2014. Our results document significant time variation of the common factors across bond indices for different maturities, ratings and industries. The...

We propose a simple non-equilibrium model of a financial market as an open system with a possible exchange of money with an outside world and market frictions (trade impacts) incorporated into asset price dynamics via a feedback mechanism. Using a linear market impact model, this produces a...

Market liquidity is a latent and dynamic variable. Building on Cont et al. (2014), we propose a dynamical price impact model at high-frequency, in which price impact is a product of daily, diurnal, and autoregressive stochastic intraday com- ponents. The model is estimated using a Kalman filter...

Factor attribution based on linear regression often fails to satisfactorily explain the performance of systematic investment strategies. A volatile or persistent residual suggests nonlinear interactions between factor returns and portfolio construction. We propose a nonparametric adjustment to...

This presentation reconsiders Knight's Risk, Uncertainty, and Profit of 1921 in light of the emergence of the World Wide Web in early-1990s, Emanuel Derman's pioneering work in Model Risk Management at Goldman Sachs in mid-1990s, backlash against quantitative models in aftermath of the Global...

We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time-varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and...

This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte...