A neural network enhanced volatility component model

Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio

On the calibration of stochastic volatility models: A comparison study

We studied the application of gradient based optimization methods for calibrating stochastic volatility models. In this study, the algorithmic differentiation is proposed as a novel approach for Greeks computation. The “payoff function independent” feature of algorithmic differentiation offers a unique solution cross distinct models. To this end, we derived, analysed and compared Monte Carlo estimators for computing the gradient of a certain payoff function using four different methods: algorithmic differentiation, Pathwise delta, likelihood ratio and finite differencing. We assessed the accuracy and efficiency of the four methods and their impacts into the optimisation algorithm. Numerical results are presented and discussed

Investigation of Boundary Effects on the Natural Cavitating Flow around a 2D Wedge in Shallow Water

When a cavitated body moves in shallow water, both flexible free surface and rigid bottom wall will produce great influence on the cavity pattern and hydrodynamics to change the motion attitude and stability of the body. In this paper, a single-fluid multiphase flow method coupled with a natural cavitation model was employed to study the effects of two kinds of boundaries on the natural cavitating flow around a two-dimensional symmetry wedge in shallow water. Within the range of the cavitation number for computation (0.05 ~ 2.04), the cavity pattern would be divided into three types, namely, stable type, transition type and wake-vortex type. The shape of the free surface is fairly similar to that of the cavity's upper surface with well right-and-left symmetry. However, when the immersion depth and the cavitation number are decreasing, the symmetry of the cavity shape is destroyed due to the influence of bottom wall effects. When the cavitation number is less than about 0.1, with the immersion depth going down, free surface effects exerts a stronger influence on the drag coefficient of this 2D wedge, whereas wall effects bring a stronger influence on the lift coefficient

Option valuation under no-arbitrage constraints with neural networks

In this paper, we start from the no-arbitrage constraints in option pricing and develop a novel hybrid gated neural network (hGNN) based option valuation model. We adopt a multiplicative structure of hidden layers to ensure model differentiability. We also select the slope and weights of input layers to satisfy the no-arbitrage constraints. Meanwhile, a separate neural network is constructed for predicting option-implied volatilities. Using S&P 500 options, our empirical analyses show that the hGNN model substantially outperforms well-established alternative mod els in the out-of-sample forecasting and hedging exercises. The superior prediction performance stems from our model’s ability in describing options on the boundary, and in offering analytical expressions for option Greeks which generate better hedging results