Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption

Aβ, tau and P-tau have been widely accepted as reliable markers for Alzheimer’s disease (AD). The crosstalk between these markers forms a complex network. AD may induce the integral variation and disruption of the network. The aim of this study was to develop a novel mathematic model based on a simplified crosstalk network to evaluate the disease progression of AD. The integral variation of the network is measured by three integral disruption parameters. The robustness of network is evaluated by network disruption probability. Presented results show that network disruption probability has a good linear relationship with Mini Mental State Examination (MMSE). The proposed model combined with Support vector machine (SVM) achieves a relative high 10-fold cross-validated performance in classification of AD vs normal and mild cognitive impairment (MCI) vs normal (95% accuracy, 95% sensitivity, 95% specificity for AD vs normal; 90% accuracy, 94% sensitivity, 83% specificity for MCI vs normal). This research evaluates the progression of AD and facilitates AD early diagnosis

Risk-Neutral and Physical Jumps in Option Pricing

When jumps are present in the price dynamics of the underlying asset, the market is no longer complete, and a more general pricing framework than the risk-neutral valuation is needed. Using Monte Carlo simulation, we investigate the important difference between risk-neutral and physical jumps in option pricing, especially for medium-and long-term options. 1

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

Night trading and market quality: Evidence from Chinese and U.S. precious metal futures markets

Given a dominant exchange, how should other exchanges set their trading hours? We examine the introduction of a night session by the Shanghai Futures Exchange, allowing trading concurrently with daytime trading at the Commodity Exchange in the U.S. After developing hypotheses, results for gold and silver show: trading activity has increased; liquidity in Shanghai has risen and prices are less volatile at market opening; the price discovery share of Chinese gold futures has fallen but this is not a sign of weakening market quality; and volatility spillovers increase bi-directionally. Longer trading hours have decreased market segmentation and increased information flow

Wavelet-based option pricing: An empirical study

In this paper, we adopt a wavelet-based option valuation model and empirically compare the pricing and forecasting performance of this model with that of the stochastic volatility model with jumps and the spline method. Both the in-sample valuation and out-of-sample forecasting accuracy are examined using daily index options in the UK, Germany, and Hong Kong from January 2009 to December 2012. Our results show that the wavelet-based model compares favorably with the other two models and that it provides an excellent alternative for valuing option prices. Its superior performance comes from the powerful ability of the wavelet method in approximating the risk-neutral moment-generating functions

Option-Implied Volatilities and Stock Returns: <i>Evidence from Industry-Neutral Portfolios</i>

Recent studies demonstrate the profitability of stock portfolios constructed according to implied volatility measures inferred from option prices. This article examines industry effects on such portfolios’ performance. Results show that quintile portfolios constructed using volatility skew and volatility spread are subject to substantial industry effects, which are particularly strong during market turbulence. The authors form industry-neutral portfolios and compare their performances to those of full-universe portfolios that do not consider industry exposure. Results show significant improvement when portfolio strategies are implemented in an industry-neutral manner, based on either volatility skew or volatility spread

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

Risk-neutral and Physical Jumps in Option Pricing

When jumps are present in the price dynamics of the underlying asset, the market is no longer complete, and a more general pricing framework than the risk-neutral valuation is needed. Using Monte Carlo simulation, we investigate the important diffrence between risk- neutral and physical jumps in option pricing, especially for medium- and long-term options.

Revealing the Implied Risk-neutral MGF with the Wavelet Method

Options are believed to contain unique information about the risk- neutral moment generating function (MGF hereafter) or the risk-neutral probability density function (PDF hereafter). This paper applies the wavelet method to approximate the risk-neutral MGF of the under- lying asset from option prices. Monte Carlo simulation experiments are performed to elaborate how the risk-neutral MGF can be obtained using the wavelet method. The Black-Scholes model is chosen as the benchmark model. We offer a novel method for obtaining the implied risk-neutral MGF for pricing out-of-sample options and other complex or illiquid derivative claims on the underlying asset using information obtained from simulated data