Probability weighting functions obtained from Hong Kong index option market

In this paper we estimate the pricing kernel from the Hong Kong index option market and obtain the empirical probability weighting functions based on the rank-dependent expected utility. The empirical pricing kernel is estimated semi-parametrically as the ratio of the risk-neutral and objective densities. We employ a two-step estimation procedure to estimate the objective and risk-neutral densities under a consistent parametric framework of the non-affine generalised autoregressive conditional heteroskedasticity (G.A.R.C.H.) diffusion model. In the first step, we develop a continuous particle filters-based maximum likelihood estimation method to estimate the objective parameters of the G.A.R.C.H. diffusion model using the Hang Seng Index (H.S.I.) returns. In the second step of our estimation, we depart from the usual pure calibration approach and use the H.S.I. option prices to estimate the risk-neutral parameters of the G.A.R.C.H. diffusion model by constraining certain parameters to be consistent with the time-series behaviour of H.S.I. returns. Based on the estimated objective and risk-neutral parameters, the objective and risk-neutral densities are obtained by inverting the corresponding characteristic functions. Empirical results indicate that the empirical pricing kernel estimated from the Hong Kong index option market is non-monotonic and the estimated probability weighting functions are S-shaped, which implies that investors underweight small probability events and overweight large one

The Bias of Growth Opportunity

The bias of growth opportunity (BGO), measured as the difference between market and fundamental values of a firm's growth opportunity, has an ability to predict future stock returns. In the portfolio sort, downward-biased BGO firms earn higher returns than upward-biased ones, which is unexplained by the common asset pricing models. Cross-sectional regression results also confirm BGO's power in predicting stock returns. To explain the anomaly, we show that the BGO premium is more pronounced when investor sentiment is high or when limits-to-arbitrage is severe, which suggests that the urn:x-wiley:13547798:media:eufm12323:eufm12323-math-0001 is more likely to capture behavioural biases than systematic risk

The Bias of Growth Opportunity

The bias of growth opportunity (BGO), measured as the difference between market and fundamental values of a firm's growth opportunity, has an ability to predict future stock returns. In the portfolio sort, downward-biased BGO firms earn higher returns than upward-biased ones, which is unexplained by the common asset pricing models. Cross-sectional regression results also confirm BGO's power in predicting stock returns. To explain the anomaly, we show that the BGO premium is more pronounced when investor sentiment is high or when limits-to-arbitrage is severe, which suggests that the urn:x-wiley:13547798:media:eufm12323:eufm12323-math-0001 is more likely to capture behavioural biases than systematic risk

An Integrated Strategy for a Production Planning and Warehouse Layout Problem: Modeling and Solution Approaches

We study a real-world production warehousing case, where the company always faces the challenge to find available space for their products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and-fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported

An evolving network model of credit risk contagion in the financial market

This paper introduces an evolving network model of credit risk contagion containing the average fitness of credit risk contagion, the risk aversion sentiments, and the ability of resist risk of credit risk holders. We discuss the effects of the aforementioned factors on credit risk contagion in the financial market through a series of theoretical analysis and numerical simulations. We find that, on one hand, the infected path distribution of the network gradually increases with the increase in the average fitness of credit risk contagion and the risk aversion sentiments of nodes, but gradually decreases with the increase in the ability to resist risk of nodes. On the other hand, the average fitness of credit risk contagion and the risk aversion sentiments of nodes increase the average clustering coefficient of nodes, whereas the ability to resist risk of nodes decreases this coefficient. Moreover, network size also decreases the average clustering coefficient. First published online: 29 Feb 201

Time-frequency analysis framework for understanding non-stationary and multi-scale characteristics of sea-level dynamics

Rising sea level caused by global climate change may increase extreme sea level events, flood low-lying coastal areas, change the ecological and hydrological environment of coastal areas, and bring severe challenges to the survival and development of coastal cities. Hong Kong is a typical economically and socially developed coastal area. However, in such an important coastal city, the mechanisms of local sea-level dynamics and their relationship with climate teleconnections are not well explained. In this paper, Hong Kong tide gauge data spanning 68 years was documented to study the historical sea-level dynamics. Through the analysis framework based on Wavelet Transform and Hilbert Huang Transform, non-stationary and multi-scale features in sea-level dynamics in Hong Kong are revealed. The results show that the relative sea level (RSL) in Hong Kong has experienced roughly 2.5 cycles of high-to-low sea-level transition in the past half-century. The periodic amplitude variation of tides is related to Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO). RSL rise and fall in eastern Hong Kong often occur in La Niña and El Niño years, respectively. The response of RSL to the PDO and ENSO displays a time lag and spatial heterogeneity in Hong Kong. Hong Kong's eastern coastal waters are more strongly affected by the Pacific climate and current systems than the west. This study dissects the non-stationary and multi-scale characteristics of relative sea-level change and helps to better understand the response of RSL to the global climate system

Quantification of hypsarrhythmia in infantile spasmatic EEG:a large cohort study

Infantile spasms (IS) is a neurological disorder causing mental and/or developmental retardation in many infants. Hypsarrhythmia is a typical symptom in the electroencephalography (EEG) signals with IS. Long-Term EEG/video monitoring is most frequently employed in clinical practice for IS diagnosis, from which manual screening of hypsarrhythmia is time consuming and lack of sufficient reliability. This study aims to identify potential biomarkers for automatic IS diagnosis by quantitative analysis of the EEG signals. A large cohort of 101 IS patients and 155 healthy controls (HC) were involved. Typical hypsarrhythmia and non-hypsarrhythmia EEG signals were annotated, and normal EEG were randomly picked from the HC. Root mean square (RMS), teager energy (TE), mean frequency, sample entropy (SamEn), multi-channel SamEn, multi-scale SamEn, and nonlinear correlation coefficient were computed in each sub-band of the three EEG signals, and then compared using either a one-way ANOVA or a Kruskal-Wallis test (based on their distribution) and the receiver operating characteristic (ROC) curves. The effects of infant age on these features were also investigated. For most of the employed features, significant ({p} &lt; {0}.{05} ) differences were observed between hypsarrhythmia EEG and non-hypsarrhythmia EEG or HC, which seem to increase with increased infant age. RMS and TE produce the best classification in the delta and theta bands, while entropy features yields the best performance in the gamma band. Our study suggests RMS and TE (delta and theta bands) and entropy features (gamma band) to be promising biomarkers for automatic detection of hypsarrhythmia in long-Term EEG monitoring. The findings of our study indicate the feasibility of automated IS diagnosis using artificial intelligence.</p

An Overview of Plant Phenolic Compounds and Their Importance in Human Nutrition and Management of Type 2 Diabetes

In this paper, the biosynthesis process of phenolic compounds in plants is summarized, which includes the shikimate, pentose phosphate and phenylpropanoid pathways. Plant phenolic compounds can act as antioxidants, structural polymers (lignin), attractants (flavonoids and carotenoids), UV screens (flavonoids), signal compounds (salicylic acid and flavonoids) and defense response chemicals (tannins and phytoalexins). From a human physiological standpoint, phenolic compounds are vital in defense responses, such as anti-aging, anti-inflammatory, antioxidant and anti-proliferative activities. Therefore, it is beneficial to eat such plant foods that have a high antioxidant compound content, which will cut down the incidence of certain chronic diseases, for instance diabetes, cancers and cardiovascular diseases, through the management of oxidative stress. Furthermore, berries and other fruits with low-amylase and high-glucosidase inhibitory activities could be regarded as candidate food items in the control of the early stages of hyperglycemia associated with type 2 diabetes