Chicago Mercantile Exchange Inc.

Nothing Technology Limited

Shuo Liu

Jennifer Yingying Weng

Guo Chen

Hang Du

Siwen Yang

Nikhil Joshi

Zijiang Yang

Xiaowen Xu

Sebastiano Rossi

Bin Huang

Chongyang Cui

Ruoxue Yang

Xianqing Zou

Ziyi Wang

Yingwen Liu

Chenda Huang

Panagiotis Xythalis

Shengli Hu

Abhinaw Prakash

Nataliya Frost

Lingrui Xiang

Hariharan Kesavarao

Jie Zhu

Charging case for wireless earphones

A method and system are disclosed for more efficiently constructing a volatility surface. The methodology results in measurable reduction in the quantity of storage memory space needed on a computer executing the novel methodology, reduces the measurable, on-demand computational load on the processor(s) of a computer executing the novel methodology, and permits as-of-day calculations of the volatility surface that were previously impossible to obtain in near real-time.

Computer methodology providing as-of-day volatility surface construction for pricing

A physical container (e.g., a battery) may be filled up (charged) or emptied (discharged) with energy commensurate with requirements to post a particular amount of collateral. The disclosure provides computing systems and methods for processing data using a novel combination of wavelet techniques and rolling techniques to more efficiently detect seasonality in particular products (e.g., energy products) to more accurately model and determine collateral/margin requirements. A clearinghouse computing device may be configured to generate a margin requirement for a portfolio of products and may include a processor to process instructions that cause the clearinghouse computing device to perform wavelet decomposition and rolling methods on a historical database of records.

Futures margin modeling system having seasonality detection

Wireless earbud

Earbud case

Electric battery recharging unit controlled by a seasonality detection unit of a futures margin modeling system

A method and system are disclosed for more efficiently constructing a volatility surface. The methodology results in measurable reduction in the quantity of storage memory space needed on a computer executing the novel methodology, reduces the measurable, on-demand computational load on the processor(s) of a computer executing the novel methodology, and also permits as-of-day calculations of the volatility surface that were previously impossible to obtain in near real-time.

Datasets may be characterized by patterns. The patterns may be caused or otherwise influenced by external factors, such as temporal, meteorological, and/or system factors. The external factors, as well as the patterns which result in the data values of the dataset because of the external factors, may provide for techniques used to account for missing data elements, outlier data elements and/or otherwise cleanse the dataset. New elements may be generated to provide for the missing data elements, and derivative datasets may be generated based on one or more cleansed datasets.

Dataset cleansing

Growing community of inventors

London, United Kingdom

Shuo Liu