Name: Finance with Monte Carlo
ISBN: 9781461485100

Book information

Authors: Ronald W. Shonkwiler verified identity
First published: 2013
Editions known: 4
Publishers: Springer
Languages: eng
ISBN-13: 9781461485100, 9781461485117, 9781461485124, 9781493943340
Main topics: Numerical Methods, Portfolio Optimization
Also covers: Derivatives Pricing, Quantitative Finance, Stochastic Calculus
Related site topics: Options & Derivatives Pricing, Portfolio Optimization, Stochastic Control & Optimal Stopping

Overview

Finance with Monte Carlo is a book by Ronald W. Shonkwiler, first published in 2013. In this catalogue it is classified mainly under Numerical Methods and Portfolio Optimization, with secondary coverage of Derivatives Pricing, Quantitative Finance and Stochastic Calculus.

Audience and depth. The rule-based model places its mathematical difficulty at advanced level (8/10); readers are typically expected to know calculus, linear algebra, measure theory, numerical analysis, optimization, probability theory, statistics and stochastic processes.

Orientation. The metadata points to an implementation-oriented, applied treatment. 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 99% and difficulty confidence is 99%; 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	77/100 confidence 99% Score 77.3/100. Strongest: quant_relevance 1.00, technical_depth 0.82; weakest: practical_applicability 0.51. Evidence: subjects:'quantitative finance', openalex_topics:'portfolio optimization', ol_description:'black-scholes'.
Mathematical difficulty	Advanced (8/10) confidence 99%
Prerequisites	calculus, linear algebra, measure theory, numerical analysis, optimization, probability theory, statistics, stochastic processes
Practical orientation	Applied
Metadata quality	100%

Difficulty evidence (8 signals)

brownian_martingale matched in ol_description: “brownian motion”
calculus matched in ol_description: “calculus”
levy_jumps matched in ol_description: “jump diffusion”
linear_algebra matched in ol_description: “matrix algebra”
monte_carlo matched in ol_description: “monte carlo”
monte_carlo matched in subjects: “monte carlo”
monte_carlo matched in title: “monte carlo”
numerical_methods matched in subjects: “numerical analysis”

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.

From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance Topic similarity inferred similarity 0.51
Stochastic Calculus for Option Pricing with Convex Duality, Logistic Model, and Numerical Examination Topic similarity inferred similarity 0.45
Optimizing Investment Strategies with Lazy Factor and Probability Weighting: A Price Portfolio Forecasting and Mean-Variance Model with Transaction Costs Approach Topic similarity inferred similarity 0.45
A weighted multilevel Monte Carlo method Topic similarity inferred similarity 0.45
A monotone numerical integration method for mean-variance portfolio optimization under jump-diffusion models Topic similarity inferred similarity 0.41
Random processes for long-term market simulations Topic similarity inferred similarity 0.39

External resources

Open Library
DOI
Search on Amazon (ISBN-13 9781493943340) (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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