Out-of-sample pricing performance across different moneyness groups.

Out-of-sample pricing performance across different moneyness groups.</p

The returns and the liquidity levels of FB stock.

This paper investigates the pricing problem of quanto options with market liquidity risk using the Bayesian method. The increasing volatility of global financial markets has made liquidity risk a significant factor that should be taken into consideration while evaluating option prices. To address this issue, we first derive the pricing formula for quanto options with liquidity risk. Next, we construct a likelihood function to conduct posterior inference on model parameters. We then propose a numerical algorithm to conduct statistical inferences on the option prices based on the posterior distribution. This proposed method considers the impact of parameter uncertainty on option prices. Finally, we conduct a comparison between the Bayesian method and traditional estimation methods to examine their validity. Empirical results show that our proposed method is feasible for pricing and predicting quanto options with liquidity risk, particularly for parameter estimations with a small sample size.</div

Quanto option price with liquidity adjustment by Bayesian method and NOP method.

Quanto option price with liquidity adjustment by Bayesian method and NOP method.</p

Posterior histogram and posterior kernel density for parameters under liquidity measure RDV.

Posterior histogram and posterior kernel density for parameters under liquidity measure RDV.</p

S1 Dataset -

This paper investigates the pricing problem of quanto options with market liquidity risk using the Bayesian method. The increasing volatility of global financial markets has made liquidity risk a significant factor that should be taken into consideration while evaluating option prices. To address this issue, we first derive the pricing formula for quanto options with liquidity risk. Next, we construct a likelihood function to conduct posterior inference on model parameters. We then propose a numerical algorithm to conduct statistical inferences on the option prices based on the posterior distribution. This proposed method considers the impact of parameter uncertainty on option prices. Finally, we conduct a comparison between the Bayesian method and traditional estimation methods to examine their validity. Empirical results show that our proposed method is feasible for pricing and predicting quanto options with liquidity risk, particularly for parameter estimations with a small sample size.</div

Posterior results using Bayesian method and NOP estimates for quanto option model.

Posterior results using Bayesian method and NOP estimates for quanto option model.</p

An Ensemble Method with Hybrid Features to Identify Extracellular Matrix Proteins

<div><p>The extracellular matrix (ECM) is a dynamic composite of secreted proteins that play important roles in numerous biological processes such as tissue morphogenesis, differentiation and homeostasis. Furthermore, various diseases are caused by the dysfunction of ECM proteins. Therefore, identifying these important ECM proteins may assist in understanding related biological processes and drug development. In view of the serious imbalance in the training dataset, a Random Forest-based ensemble method with hybrid features is developed in this paper to identify ECM proteins. Hybrid features are employed by incorporating sequence composition, physicochemical properties, evolutionary and structural information. The Information Gain Ratio and Incremental Feature Selection (IGR-IFS) methods are adopted to select the optimal features. Finally, the resulting predictor termed IECMP (Identify ECM Proteins) achieves an balanced accuracy of 86.4% using the 10-fold cross-validation on the training dataset, which is much higher than results obtained by other methods (ECMPRED: 71.0%, ECMPP: 77.8%). Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED. These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples. It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets. For public access, we develop a user-friendly web server for ECM protein identification that is freely accessible at <a href="http://iecmp.weka.cc" target="_blank">http://iecmp.weka.cc</a>.</p></div

The overall work flow of the proposed method IECMP(Identify ECM Proteins).

<p>(i) The training sequences are mapped into feature vectors. (ii) To reduce the complexity and the feature redundancy, the Information Gain Ratio and Incremental Feature Selection (IGR-IFS) methods are employed. (iii) The training set is divided into 11 training subsets through the undersampling approach. (iv) With the optimal features, the 11 training subsets train Random Forest classifiers, respectively. (v) The predicted class labels of the test set are determined by the majority voting method.</p

Prediction results with or without the ensemble method.

<p>Prediction results with or without the ensemble method.</p

The prediction results compared with other methods on the independent testing dataset.

<p>The prediction results compared with other methods on the independent testing dataset.</p