A relief supplies purchasing model based on a put option contract

2019 ◽  
Vol 127 ◽  
pp. 253-262 ◽  
Author(s):  
Zhongquan Hu ◽  
Jun Tian ◽  
Gengzhong Feng
Keyword(s):  
Model Based ◽  
Put Option ◽  
Option Contract ◽  
Purchasing Model

scholarly journals TESTING OF THE BLACK SCHOLES AND GARCH MODELS IN LQ45 USING LONG STRADDLE STRATEGY IN 2009-2018

2021 ◽  
Vol 22 (1) ◽  
pp. 30-39
Author(s):  
Riko Hendrawan ◽  
Anggadi Sasmito
Keyword(s):  
Time Series Data ◽  
Mean Squared Error ◽  
Series Data ◽  
Call Option ◽  
Due Date ◽  
Average Percentage ◽  
Put Option ◽  
Option Contract ◽  
Black Scholes ◽  
Better Than

The purpose of this study is to examine the implementation of option contracts using Black Scholes and GARCH on the LQ45 index using the long straddle strategy. This study uses time-series data as a time frame for conducting research, using a sample of closing price data for the LQ 45 daily index for 2009-2018. For the test the model, we used the secondary data of the closing stock price index from February 28, 2009 to March 31, 2019The results of this study are seen by comparing the average percentage value of Average Mean Squared Error (AMSE) of Black Scholes and GARCH with the application of a long straddle strategy, where the smaller the percentage value, the better the model will be. Within one month of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 2.77% and the put option of 1.56%. Within two months of option contract due date, GARCH is better than Black Scholes, with an error value on the call option of 8.12% and the put option of 4.00%. Within three months of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 12.38% and on the put option of 5.50%. The long straddle strategy in the LQ45 index only reached a maximum of 60% of possible profits, with an average of around 30% possible profits.

scholarly journals Impacts of put option contract and supply chain structure in a multi-period supply chain with uncertain demand

2020 ◽  
Author(s):  
Nana Wan ◽  
Jianchang Fan
Keyword(s):  
Supply Chain ◽  
Option Price ◽  
Service Level ◽  
Chain Structure ◽  
Approximate Algorithm ◽  
Put Option ◽  
Option Contract ◽  
Exercise Price ◽  
Total Profit ◽  
Supply Chain Structure

This paper builds the multi-period optimization models that incorporate put option contract and two supply chain structures to determine the production decision for a supplier and the ordering decision for a manufacturer in a two-stage supply chain. This paper applies the method of dynamic programming to derive the structures of optimal policies and provides an approximate algorithm to evaluate the myopic policies. This paper also conducts numerical examples to illustrate the impacts of put option contract, supply chain structure and demand risk on the members’ decisions and total profits as well as the channel’s total profit. The results indicate that put option contract can motivate to increase the channel’s service level and reduce the manufacturer’s inventory risk under two supply chain structures, when compared to the case without put option contract. In the manufacturer-led structure, the channel always benefits from put option contract, the supplier benefits from put option contract with a high option price and a low exercise price, while the manufacturer benefits from put option contract with a low option price and a high exercise price. In the supplier-led structure, the channel and the manufacturer always benefit from put option contract, while the supplier benefits from put option contract with a high option price and a low exercise price. With put option contract, the supplier is more profitable in the manufacturer-led structure than in the supplier-led structure, while the manufacturer and the channel are more profitable in the supplier-led structure than in the manufacturer-led structure. Without and with put option contract, the optimal total profits of two members and the channel will first decrease and then increase in the demand risk. Finally, this paper identifies the explicit conditions under which the multi-period supply chain can be coordinated via put option contract under two supply chain structures. With a coordinating contract, the supplier and the manufacturer are better off compared to the case without put option contract.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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