The Chebyshev method for the implied volatility
In this paper, the authors propose a bivariate interpolation of the implied volatility surface based on Chebyshev polynomials. This yields a closed-form approximation of the implied volatility, which...
View ArticleCurrency risk in foreign currency accounts for small and medium-sized businesses
This paper estimates the currency exposure before and after the hedging of active foreign currency (FC) accounts, using stochastic models for spot exchange rates and cashflow movements.
View ArticleThe impact of the cross-shareholding network on extreme price movements:...
By using information about the ownership structure of listed companies from 2004 to 2016, the authors construct the cross-shareholding network for each year and examine the effects of the network...
View ArticleA simulation-based model for optimal demand response load shifting: a case...
This paper describes a case study of analyzing DR load-shifting strategies for a retail electric provider for the Texas (ERCOT) market using a Monte Carlo simulation with stochastic loads and...
View ArticleValidation of index and benchmark assignment: adequacy of capturing tail risk
This paper provides practical recommendations for the validation of risk models under the Targeted Review of Internal Models (TRIM).
View ArticleValue-at-risk in the European energy market: a comparison of parametric,...
This paper examines a set of value-at-risk (VaR) models and their ability to appropriately describe and capture price-change risk in the European energy market.
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US nonfarm employment prediction using RIWI Corp. alternative data
IntroductionThe monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors...
View ArticleStatement in Connection with the 2019 AICPA Conference on Current SEC and...
Statement in Connection with the 2019 AICPA Conference on Current SEC and PCAOB Developments Sagar Teotia, Chief Accountant Washington D.C. Dec. 9, 2019
View ArticleRemarks before the 2019 AICPA Conference on Current SEC and PCAOB Developments
Remarks before the 2019 AICPA Conference on Current SEC and PCAOB Developments Aaron Shaw, Professional Accounting Fellow, Office of the Chief Accountant Washington D.C. Dec. 9, 2019
View ArticleSEC Obtains Touting and Fraud Judgment Against Colorado Cannabis Stock Promoter
A Colorado stock promoter and two of his companies agreed to pay $4.2 million to settle the U.S. Securities and Exchange Commission's charges for fraudulently promoting and trading a cannabis stock.Â...
View ArticleJefferies to Pay Nearly $4 Million for Improper Handling of ADRs
The Securities and Exchange Commission today announced that broker-dealer Jefferies LLC will pay nearly $4 million to settle charges of improper handling of âpre-releasedâ American Depositary...
View ArticleMoneyScience: Online Training - IBM - What is Data Science?
Resource: Online Training - IBM - What is Data Science? https://t.co/hulj5snQm8 â moneyscience (@moneyscience) December 9, 2019
View ArticleMoneyScience: Online Training - IBM - Open Source tools for Data Science
Resource: Online Training - IBM - Open Source tools for Data Science https://t.co/5LAp46S1L4 â moneyscience (@moneyscience) December 9, 2019
View ArticleMoneyScience: Online Training - IBM - Data Science Methodology
Resource: Online Training - IBM - Data Science Methodology https://t.co/mSOCm2KEu5 â moneyscience (@moneyscience) December 9, 2019
View ArticleMoneyScience: Online Training - IBM - Python for Data Science and AI
Resource: Online Training - IBM - Python for Data Science and AI https://t.co/I1D1aTbscF â moneyscience (@moneyscience) December 9, 2019
View ArticleOn Utility Maximisation Under Model Uncertainty in Discrete-Time Markets....
We study the problem of maximising terminal utility for an agent facing model uncertainty, in a frictionless discrete-time market with one safe asset and finitely many risky assets. We show that an...
View ArticleAdversarial recovery of agent rewards from latent spaces of the limit order...
Inverse reinforcement learning has proved its ability to explain state-action trajectories of expert agents by recovering their underlying reward functions in increasingly challenging environments....
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