Name: Risk measurement, econometrics, and neural networks
ISBN: 9783790811520

Book information

Authors: Karlsruher Ökonometrie-Workshop (6th 1997) verified identity
First published: 1998
Editions known: 2
Publishers: Physica-Verlag
Languages: eng
ISBN-13: 9783790811520
Main topics: Deep Learning for Finance, Financial Econometrics
Also covers: Risk Management
Related site topics: Machine Learning for Market Forecasting, NLP & LLMs in Finance, Risk Management & Tail Risk, Volatility Modeling & Forecasting

Overview

Risk measurement, econometrics, and neural networks is a book by Karlsruher Ökonometrie-Workshop (6th 1997), first published in 1998. In this catalogue it is classified mainly under Deep Learning for Finance and Financial Econometrics, with secondary coverage of Risk Management.

Audience and depth. The rule-based model places its mathematical difficulty at intermediate level (6/10); readers are typically expected to know calculus, linear algebra, machine learning, probability theory and statistics.

Orientation. The theory-versus-practice balance could not be determined from metadata. The archive holds 6 papers whose topics and vocabulary overlap with this book; those links are inferred (see the research connections below).

How to read this page. Topics, difficulty, prerequisites and orientation are model-derived (rule-based, rule_based_v1) from bibliographic metadata such as title, subject headings and edition data. They were not produced by reading the book and are not a review. Relevance confidence is 67% and difficulty confidence is 66%; the catalogue lists no ratings or awards.

Research assessment

Rule-based and model-derived from metadata (title, subject headings, edition data). These are not reviews or ratings; see the evidence below.

Quant-finance relevance	38/100 confidence 67% Score 38.2/100. Strongest: quant_relevance 0.40, technical_depth 0.51; weakest: practical_applicability 0.00. Evidence: subjects:'finance'.
Mathematical difficulty	Intermediate (6/10) confidence 66%
Prerequisites	calculus, linear algebra, machine learning, probability theory, statistics
Metadata quality	85%

Difficulty evidence (3 signals)

econometrics matched in subjects: “econometric”
econometrics matched in title: “econometric”
machine_learning matched in title: “neural networks”

Research connections

Related papers on this site

Similarity of research area (shared vocabulary, topics and methods). Not citations: the paper does not necessarily cite this book.

Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems Topic similarity inferred similarity 0.55
Forecasting Probability Distributions of Financial Returns with Deep Neural Networks Topic similarity inferred similarity 0.53
Dynamic graph neural networks for enhanced volatility prediction in financial markets Topic similarity inferred similarity 0.52
From GARCH to Neural Network for Volatility Forecast Topic similarity inferred similarity 0.52
Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting Topic similarity inferred similarity 0.52
Stochastic Volatility Model with Sticky Drawdown and Drawup Processes: A Deep Learning Approach Topic similarity inferred similarity 0.50

External resources

Open Library
Search on Amazon (ISBN-13 9783790811520) (search link; may be an affiliate link)

Relationship graph

Solid lines are verified facts (authorship from persistent identifiers, editions); dashed lines are inferred by rules or similarity. Open in the full explorer.

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