scholarly journals Deep Learning-Based Method for Computing Initial Margin

2021 ◽  
Vol 7 (1) ◽  
pp. 41
Author(s):  
Joel Pérez Villarino ◽  
Álvaro Leitao Rodríguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Interest Rate ◽  
Financial Institutions ◽  
Optimal Configuration ◽  
Model Parameters ◽  
Basel Iii ◽  
Interest Rate Swap ◽  
Rate Swap

Following the guidelines of the Basel III agreement (2013), large financial institutions are forced to incorporate additional collateral, known as Initial Margin, in their transactions in OTC markets. Currently, the computation of such collateral is performed following the Standard Initial Margin Model (SIMM) methodology. Focusing on a portfolio consisting of an interest rate swap, we propose the use of Artificial Neural Networks (ANN) to approximate the Initial Margin value of the portfolio over its lifetime. The goal is to find an optimal configuration of structural hyperparameters, as well as to analyze the robustness of the network to variations in the model parameters and swap features.

A note on CVA and wrong way risk

2016 ◽  
Vol 03 (02) ◽  
pp. 1650012
Author(s):  
Roberto Baviera ◽  
Gaetano La Bua ◽  
Paolo Pellicioli
Keyword(s):  
Interest Rate ◽  
Recursion Formula ◽  
Slight Modification ◽  
Path Dependency ◽  
Model Parameters ◽  
Market Data ◽  
Interest Rate Swap ◽  
Fast Approach ◽  
Straightforward Generalization ◽  
Rate Swap

Hull and White approach to Wrong Way Risk in the computation of Credit Value Adjustment (CVA) is considered the most straightforward generalization of the standard Basel approach. The model is financially intuitive and it can be implemented by a slight modification of existing algorithms for CVA calculation. However, path dependency in the key quantities has non-elementary consequences in the calibration of model parameters. We propose a simple and fast approach for computing these quantities via a recursion formula. We show in detail the calibration methodology on market data and CVA computations in two relevant cases: a FX forward and an interest rate swap.

Savings and Loan Capital and the Use of Interest Rate Swaps

1992 ◽  
Vol 36 (2) ◽  
pp. 50-57
Author(s):  
David Vang
Keyword(s):  
Interest Rate ◽  
Financial Institutions ◽  
Interest Rate Risk ◽  
Interest Rate Swap ◽  
Savings And Loan ◽  
Interest Rate Swaps ◽  
Flexible Rate ◽  
The Interest Rate ◽  
The Relationship ◽  
Rate Swap

This paper models the relationship between interest rate swaps and capital in savings and loan associations. The interest rate swap is a way in which financial institutions exchange the flexible rate on their liabilities with a fixed interest rate to hedge themselves from interest rate risk, and therefore reduce the need for a capital cushion. The empirical evidence, however, shows that a small capital cushion reduces the firm's possibility of using interest rate swaps because no partner is willing to engage in a rate swapping contract with a firm that does not have adequate capital and soundness.

scholarly journals Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

2021 ◽  
Vol 13 (8) ◽  
pp. 4572
Author(s):  
Jiří David ◽  
Pavel Brom ◽  
František Starý ◽  
Josef Bradáč ◽  
Vojtěch Dynybyl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Cruise Control ◽  
Control Unit ◽  
Driving Safety ◽  
Model Parameters ◽  
Cruise Control ◽  
Artificial Neural ◽  
Dynamic Obstacle ◽  
Braking Process

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

SỬ DỤNG DEEP NEURAL NETWORKS PHÁT HIỆN GAI ĐỘNG KINH TRONG BẢN GHI EEG

2020 ◽  
pp. 77-84
Author(s):  
Xuyến
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Artificial Neural

Deep Neural Networks là một thuật toán dạy cho máy học, là phương pháp nâng cao của mạng nơ-ron nhân tạo (Artificial Neural Networks) nhiều tầng để học biểu diễn mô hình đối tượng. Bài báo trình bày phương pháp để phát hiện spike tự động, giải quyết bài toán cho các bác sỹ khi phân tích dữ liệu khổng lồ được thu thập từ bản ghi điện não để xác định một khu vực của não gây ra chứng động kinh. Hàng triệu mẫu được phân tích thủ công đã được đào tạo lại để tìm các gai liêp tiếp phát ra từ vùng não bị ảnh hưởng. Để đánh giá phương pháp đề xuất, tác giả đã xây dựng hệ thống trong đó sử dụng một số mô hình deep learning đưa vào thử nghiệm hỗ trợ các bác sỹ khám phát hiện và chẩn đoán sớm bệnh.

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