**Financial modeling** is the task of building an abstract representation (a model) of a real world financial situation.^{ [1] } This is a mathematical model designed to represent (a simplified version of) the performance of a financial asset or portfolio of a business, project, or any other investment.

Typically, then, financial modeling is understood to mean an exercise in either asset pricing or corporate finance, of a quantitative nature. It is about translating a set of hypotheses about the behavior of markets or agents into numerical predictions.^{ [2] } At the same time, "financial modeling" is a general term that means different things to different users; the reference usually relates either to accounting and corporate finance applications, or to quantitative finance applications.

While there has been some debate in the industry as to the nature of financial modeling—whether it is a tradecraft, such as welding, or a science—the task of financial modeling has been gaining acceptance and rigor over the years.^{ [3] }

In corporate finance and the accounting profession, *financial modeling* typically entails financial statement forecasting; usually the preparation of detailed company-specific models used for decision making purposes^{ [1] } and financial analysis.

Applications include:

- Business valuation, especially discounted cash flow, but including other valuation approaches
- Scenario planning and management decision making ("what is"; "what if"; "what has to be done"
^{ [4] }) - Capital budgeting
- Cost of capital (i.e. WACC) calculations
- Financial statement analysis (including of operating- and finance leases, and R&D)
- Project finance modeling
- Cash flow forecasting and asset and liability management related

To generalize ^{[ citation needed ]} as to the nature of these models: firstly, as they are built around financial statements, calculations and outputs are monthly, quarterly or annual; secondly, the inputs take the form of "assumptions", where the analyst *specifies* the values that will apply in each period for external / global variables (exchange rates, tax percentage, etc....; may be thought of as the model * parameters *), and for internal / company specific *variables* (wages, unit costs, etc....). Correspondingly, both characteristics are reflected (at least implicitly) in the mathematical form of these models: firstly, the models are in discrete time; secondly, they are deterministic. For discussion of the issues that may arise, see below; for discussion as to more sophisticated approaches sometimes employed, see Corporate finance#Quantifying uncertainty, and Financial economics#Corporate finance theory.

Modelers are often designated "financial analyst" (and are sometimes referred to (tongue in cheek) as "number crunchers"). Typically, the modeler will have completed an MBA or MSF with (optional) coursework in "financial modeling". Accounting qualifications and finance certifications such as the CIIA and CFA generally do not provide direct or explicit training in modeling.^{[ citation needed ]} At the same time, numerous commercial training courses are offered, both through universities and privately. For the components / steps of business modeling here, see the list for "Equity valuation" under Outline of finance#Discounted cash flow valuation; see also Valuation using discounted cash flows#Determine cash flow for each forecast period for further discussion and considerations.

Although purpose built business software does exist (see also Fundamental Analysis Software), the vast proportion of the market is spreadsheet-based; this is largely since the models are almost always company specific. Also, analysts will each have their own criteria and methods for financial modeling.^{ [5] } Microsoft Excel now has by far the dominant position, having overtaken Lotus 1-2-3 in the 1990s. Spreadsheet-based modelling can have its own problems,^{ [6] } and several standardizations and "best practices" have been proposed.^{ [7] } "Spreadsheet risk" is increasingly studied and managed;^{ [7] } see model audit.

One critique here, is that model *outputs*, i.e. line items, often inhere "unrealistic implicit assumptions" and "internal inconsistencies".^{ [8] } (For example, a forecast for growth in revenue but without corresponding increases in working capital, fixed assets and the associated financing, may imbed unrealistic assumptions about asset turnover, leverage and / or equity financing. See Sustainable growth rate#From a financial perspective.) What is required, but often lacking, is that all key elements are explicitly and consistently forecasted. Related to this, is that modellers often additionally "fail to identify crucial assumptions" relating to *inputs*, "and to explore what can go wrong".^{ [9] } Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures"^{ [10] } — i.e., as mentioned, the problems are treated as deterministic in nature — and thus calculate a single value for the asset or project, but without providing information on the range, variance and sensitivity of outcomes.^{ [11] } Other critiques discuss the lack of basic computer programming concepts.^{ [12] } More serious criticism, in fact, relates to the nature of budgeting itself, and its impact on the organization ^{ [13] }^{ [14] } (see Conditional budgeting#Criticism of budgeting).

In quantitative finance, *financial modeling* entails the development of a sophisticated mathematical model.^{[ citation needed ]} Models here deal with asset prices, market movements, portfolio returns and the like. A general distinction^{[ citation needed ]} is between: "quantitative financial management", models of the financial situation of a large, complex firm; "quantitative asset pricing", models of the returns of different stocks; "financial engineering", models of the price or returns of derivative securities; "quantitative corporate finance", models of the firm's financial decisions.

Relatedly, applications include:

- Option pricing and calculation of their "Greeks"
- Other derivatives, especially interest rate derivatives, credit derivatives and exotic derivatives
- Modeling the term structure of interest rates (Bootstrapping, short rate modelling, building "curve sets") and credit spreads
- Credit scoring and provisioning
- Corporate financing activity prediction problems
- Portfolio optimization
^{ [15] } - Real options
- Risk modeling (Financial risk modeling) and value at risk
^{ [16] } - Dynamic financial analysis (DFA)
- Credit valuation adjustment, CVA, as well as the various XVA
- Statistical arbitrage, convergence trading and pairs trading

These problems are generally stochastic and continuous in nature, and models here thus require complex algorithms, entailing computer simulation, advanced numerical methods (such as numerical differential equations, numerical linear algebra, dynamic programming) and/or the development of optimization models. The general nature of these problems is discussed under Mathematical finance, while specific techniques are listed under Outline of finance#Mathematical tools. For further discussion here see also: Financial models with long-tailed distributions and volatility clustering; Brownian model of financial markets; Martingale pricing; Extreme value theory; Historical simulation (finance).

Modellers are generally referred to as "quants" (quantitative analysts), and typically have advanced (Ph.D. level) backgrounds in quantitative disciplines such as statistics, physics, engineering, computer science, mathematics or operations research. Alternatively, or in addition to their quantitative background, they complete a finance masters with a quantitative orientation,^{ [17] } such as the Master of Quantitative Finance, or the more specialized Master of Computational Finance or Master of Financial Engineering; the CQF is increasingly common.

Although spreadsheets are widely used here also (almost always requiring extensive VBA); custom C++, Fortran or Python, or numerical analysis software such as MATLAB, are often preferred,^{ [17] } particularly where stability or speed is a concern. MATLAB is often used at the research or prototyping stage ^{[ citation needed ]} because of its intuitive programming, graphical and debugging tools, but C++/Fortran are preferred for conceptually simple but high computational-cost applications where MATLAB is too slow; Python is increasingly used due to its simplicity and large standard library. Additionally, for many (of the standard) derivative and portfolio applications, commercial software is available, and the choice as to whether the model is to be developed in-house, or whether existing products are to be deployed, will depend on the problem in question.^{ [17] }

The complexity of these models may result in incorrect pricing or hedging or both. This * Model risk * is the subject of ongoing research by finance academics, and is a topic of great, and growing, interest in the risk management arena.^{ [18] }

Criticism of the discipline (often preceding the financial crisis of 2007–08 by several years) emphasizes the differences between the mathematical and physical sciences, and finance, and the resultant caution to be applied by modelers, and by traders and risk managers using their models. Notable here are Emanuel Derman and Paul Wilmott, authors of the * Financial Modelers' Manifesto *. Some go further and question whether mathematical- and statistical modeling may be applied to finance at all, at least with the assumptions usually made (for options; for portfolios). In fact, these may go so far as to question the "empirical and scientific validity... of modern financial theory".^{ [19] } Notable here are Nassim Taleb and Benoit Mandelbrot.^{ [20] } See also Mathematical finance #Criticism and Financial economics #Challenges and criticism.

- Asset pricing model
- Economic model
- Financial engineering
- Financial forecast
- Financial Modelers' Manifesto
- Financial models with long-tailed distributions and volatility clustering
- Financial planning
- Integrated business planning
- Model audit
- Modeling and analysis of financial markets
- Pro forma#Financial statements
- Profit model
- Real options valuation

**Fundamental analysis**, in accounting and finance, is the analysis of a business's financial statements ; health; and competitors and markets. It also considers the overall state of the economy and factors including interest rates, production, earnings, employment, GDP, housing, manufacturing and management. There are two basic approaches that can be used: bottom up analysis and top down analysis. These terms are used to distinguish such analysis from other types of investment analysis, such as quantitative and technical.

**Financial economics** is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on *both sides* of a trade". Its concern is thus the interrelation of financial variables, such as prices, interest rates and shares, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital.

**Financial engineering** is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance.

**Volatility risk** is the risk of a change of price of a portfolio as a result of changes in the volatility of a risk factor. It usually applies to portfolios of derivatives instruments, where the volatility of its underlying is a major influencer of prices.

**Monte Carlo methods are used in corporate finance and mathematical finance** to value and analyze (complex) instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes. This is usually done by help of stochastic asset models. The advantage of Monte Carlo methods over other techniques increases as the dimensions of the problem increase.

In financial economics, **asset pricing** refers to a formal treatment and development of two main pricing principles, outlined below, together with the resultant models. There have been many models developed for different situations, but correspondingly, these stem from general equilibrium asset pricing or rational asset pricing, the latter corresponding to risk neutral pricing.

In finance, a **lattice model** is a technique applied to the valuation of derivatives, where a discrete time model is required. For equity options, a typical example would be pricing an American option, where a decision as to option exercise is required at "all" times before and including maturity. A continuous model, on the other hand, such as Black–Scholes, would only allow for the valuation of European options, where exercise is on the option's maturity date. For interest rate derivatives lattices are additionally useful in that they address many of the issues encountered with continuous models, such as pull to par. The method is also used for valuing certain exotic options, where because of path dependence in the payoff, Monte Carlo methods for option pricing fail to account for optimal decisions to terminate the derivative by early exercise., though methods now exist for solving this problem.

**Frank J. Fabozzi** is an American economist, educator, writer, and investor, currently Professor of Finance at EDHEC Business School and a Member of Edhec Risk Institute. He was previously a Professor in the Practice of Finance and Becton Fellow in the Yale School of Management. He has authored and edited many acclaimed books, three of which were coauthored with Nobel laureates, Franco Modigliani and Harry Markowitz. He has been the editor of the *Journal of Portfolio Management* since 1986 and is on the board of directors of the BlackRock complex of closed-end funds.

**Valuation using discounted cash flows** is a method of estimating the current value of a company based on projected future cash flows adjusted for the time value of money. The cash flows are made up of the cash flows within the forecast period together with a continuing or terminal value that represents the cash flow stream after the forecast period. In several contexts, DCF valuation is referred to as the "income approach".

The following outline is provided as an overview of and topical guide to finance:

In finance, an **option** is a contract which gives the buyer the right, but not the obligation, to buy or sell an underlying asset or instrument at a specified strike price prior to or on a specified date, depending on the form of the option. The strike price may be set by reference to the spot price of the underlying security or commodity on the day an option is taken out, or it may be fixed at a discount or at a premium. The seller has the corresponding obligation to fulfill the transaction – to sell or buy – if the buyer (owner) "exercises" the option. An option that conveys to the owner the right to buy at a specific price is referred to as a call; an option that conveys the right of the owner to sell at a specific price is referred to as a put. Both are commonly traded, but the call option is more frequently discussed.

**Moorad Choudhry** was formerly Head of Business Treasury, Global Banking and Markets at Royal Bank of Scotland.

In finance, a **bullet strategy** is followed by a trader investing in intermediate-duration bonds, but not in long- and short-duration bonds.

In finance, **model risk** is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card transactions, and computing the probability of air flight passenger being a terrorist. Rebonato in 2002 defines model risk as "the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market".

A **master's degree in Financial Economics** provides a rigorous understanding of theoretical finance and the economic framework upon which that theory is based. The degree is postgraduate, and usually incorporates a thesis or research component. Programs may be offered jointly by the business school and the economics department.

**Quantitative analysis** is the use of mathematical and statistical methods in finance. Those working in the field are **quantitative analysts**. Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management. The occupation is similar to those in industrial mathematics in other industries. The process usually consists of searching vast databases for patterns, such as correlations among liquid assets or price-movement patterns. The resulting strategies may involve high-frequency trading.

**Mathematical finance**, also known as **quantitative finance** and **financial mathematics**, is a field of applied mathematics, concerned with mathematical modeling of financial markets. Generally, mathematical finance will derive and extend the mathematical or numerical models without necessarily establishing a link to financial theory, taking observed market prices as input. Mathematical consistency is required, not compatibility with economic theory. Thus, for example, while a financial economist might study the structural reasons why a company may have a certain share price, a financial mathematician may take the share price as a given, and attempt to use stochastic calculus to obtain the corresponding value of derivatives of the stock. The fundamental theorem of arbitrage-free pricing is one of the key theorems in mathematical finance, while the Black–Scholes equation and formula are amongst the key results.

In finance, a **contingent claim** is a derivative whose future payoff depends on the value of another “underlying” asset, or more generally, that is dependent on the realization of some uncertain future event. These are so named, since there is only a payoff under certain contingencies. Any derivative instrument that is not a contingent claim is called a **forward commitment**. The prototypical contingent claim is an option, the right to buy or sell the underlying asset at a specified exercise price by a certain expiration date; whereas (vanilla) swaps, forwards, and futures are forward commitments, since these grant no such optionality. Contingent claims are applied under financial economics in developing models and theory, and in corporate finance as a valuation framework.

**Corporate finance** is an area of finance that deals with sources of funding, the capital structure of corporations, the actions that managers take to increase the value of the firm to the shareholders, and the tools and analysis used to allocate financial resources. The primary goal of corporate finance is to maximize or increase shareholder value.

**Riccardo Rebonato** is Professor of Finance at EDHEC Business School and EDHEC-Risk Institute, and author of journal articles and books on Mathematical Finance, covering derivatives pricing, risk management and asset allocation. Prior to this, he was Global Head of Rates and FX Analytics at PIMCO.

- 1 2 http://www.investopedia.com/terms/f/financialmodeling.asp
- ↑ Low, R.K.Y.; Tan, E. (2016). "The Role of Analysts' Forecasts in the Momentum Effect" (PDF).
*International Review of Financial Analysis*.**48**: 67–84. doi:10.1016/j.irfa.2016.09.007. - ↑ Nick Crawley (2010).
*Which industry sector would benefit the most from improved financial modelling standards?*, fimodo.com. - ↑ Joel G. Siegel; Jae K. Shim; Stephen Hartman (1 November 1997).
*Schaum's quick guide to business formulas: 201 decision-making tools for business, finance, and accounting students*. McGraw-Hill Professional. ISBN 978-0-07-058031-2 . Retrieved 12 November 2011. §39 "Corporate Planning Models". See also, §294 "Simulation Model". - ↑ See for example, Valuing Companies by Cash Flow Discounting: Ten Methods and Nine Theories, Pablo Fernandez: University of Navarra - IESE Business School
- ↑ Danielle Stein Fairhurst (2009). Six reasons your spreadsheet is NOT a financial model Archived 2010-04-07 at the Wayback Machine , fimodo.com
- 1 2 Best Practice, European Spreadsheet Risks Interest Group
- ↑ Krishna G. Palepu; Paul M. Healy; Erik Peek; Victor Lewis Bernard (2007).
*Business analysis and valuation: text and cases*. Cengage Learning EMEA. pp. 261–. ISBN 978-1-84480-492-4 . Retrieved 12 November 2011. - ↑ Richard A. Brealey; Stewart C. Myers; Brattle Group (2003).
*Capital investment and valuation*. McGraw-Hill Professional. pp. 223–. ISBN 978-0-07-138377-6 . Retrieved 12 November 2011. - ↑ Peter Coffee (2004).
*Spreadsheets: 25 Years in a Cell*, eWeek. - ↑ Prof. Aswath Damodaran.
*Probabilistic Approaches: Scenario Analysis, Decision Trees and Simulations*, NYU Stern Working Paper - ↑ Blayney, P. (2009). Knowledge Gap? Accounting Practitioners Lacking Computer Programming Concepts as Essential Knowledge. In G. Siemens & C. Fulford (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2009 (pp. 151-159). Chesapeake, VA: AACE.
- ↑ Loren Gary (2003).
*Why Budgeting Kills Your Company*, Harvard Management Update, May 2003. - ↑ Michael Jensen (2001).
*Corporate Budgeting Is Broken, Let's Fix It*, Harvard Business Review, pp. 94-101, November 2001. - ↑ Low, R.K.Y.; Faff, R.; Aas, K. (2016). "Enhancing mean–variance portfolio selection by modeling distributional asymmetries" (PDF).
*Journal of Economics and Business*.**85**: 49–72. doi:10.1016/j.jeconbus.2016.01.003. - ↑ Low, R.K.Y.; Alcock, J.; Faff, R.; Brailsford, T. (2013). "Canonical vine copulas in the context of modern portfolio management: Are they worth it?" (PDF).
*Journal of Banking & Finance*.**37**(8): 3085–3099. doi:10.1016/j.jbankfin.2013.02.036. - 1 2 3 Mark S. Joshi,
*On Becoming a Quant*. - ↑ Riccardo Rebonato (N.D.).
*Theory and Practice of Model Risk Management*. - ↑ http://www.fooledbyrandomness.com/Triana-fwd.pdf
- ↑ "Archived copy" (PDF). Archived from the original (PDF) on 2010-12-07. Retrieved 2010-06-15.CS1 maint: archived copy as title (link)

**General**

- Benninga, Simon (1997).
*Financial Modeling*. Cambridge, MA: MIT Press. ISBN 0-585-13223-2. - Benninga, Simon (2006).
*Principles of Finance with Excel*. New York: Oxford University Press. ISBN 0-19-530150-1. - Fabozzi, Frank J. (2012).
*Encyclopedia of Financial Models*. Hoboken, NJ: Wiley. ISBN 978-1-118-00673-3. - Ho, Thomas; Sang Bin Lee (2004).
*The Oxford Guide to Financial Modeling*. New York: Oxford University Press. ISBN 978-0-19-516962-1. - Sengupta, Chandan (2009).
*Financial Analysis and Modeling Using Excel and VBA, 2nd Edition*. Hoboken, NJ: John Wiley & Sons. ISBN 9780470275603. - Winston, Wayne (2014).
*Microsoft Excel 2013 Data Analysis and Business Modeling*. Microsoft Press. ISBN 978-0735669130. - Yip, Henry (2005).
*Spreadsheet Applications to securities valuation and investment theories*. John Wiley and Sons Australia Ltd. ISBN 0470807962.

**Corporate finance**

- Day, Alastair (2007).
*Mastering Financial Modelling in Microsoft Excel*. London: Pearson Education. ISBN 978-0-273-70806-3. - Lynch, Penelope (1997).
*Financial Modelling for Project Finance, 2nd Edition*. Euromoney Trading. ISBN 9781843745488. - Mayes, Timothy R.; Todd M. Shank (2011).
*Financial Analysis with Microsoft Excel, 6th Edition*. Boston: Cengage Learning. ISBN 978-1111826246. - Peter K Nevitt; Frank J. Fabozzi (2000).
*Project Financing*. Euromoney Institutional Investor PLC. ISBN 978-1-85564-791-6. - Ongkrutaraksa, Worapot (2006).
*Financial Modeling and Analysis: A Spreadsheet Technique for Financial, Investment, and Risk Management, 2nd Edition*. Frenchs Forest: Pearson Education Australia. ISBN 0-7339-8474-6. - Palepu, Krishna G.; Paul M. Healy (2012).
*Business Analysis and Valuation Using Financial Statements, 5th Edition*. Boston: South-Western College Publishing. ISBN 978-1111972288. - Pignataro, Paul (2003).
*Financial Modeling and Valuation: A Practical Guide to Investment Banking and Private Equity*. Hoboken, NJ: Wiley. ISBN 978-1118558768. - Proctor, Scott (2009).
*Building Financial Models with Microsoft Excel: A Guide for Business Professionals, 2nd Edition*. Hoboken, NJ: Wiley. ISBN 978-0-470-48174-5. - Rees, Michael (2008).
*Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level*. Hoboken, NJ: Wiley. ISBN 978-0-470-99744-4. - Soubeiga, Eric (2013).
*Mastering Financial Modeling: A Professional's Guide to Building Financial Models in Excel*. New York: McGraw-Hill. ISBN 978-0071808507. - Swan, Jonathan (2007).
*Financial Modelling Special Report*. London: Institute of Chartered Accountants in England & Wales. - Swan, Jonathan (2008).
*Practical Financial Modelling, 2nd Edition*. London: CIMA Publishing. ISBN 978-0-7506-8647-1. - Tham, Joseph; Ignacio Velez-Pareja (2004).
*Principles of Cash Flow Valuation: An Integrated Market-Based Approach*. Amsterdam: Elsevier. ISBN 0-12-686040-8. - Tjia, John (2003).
*Building Financial Models*. New York: McGraw-Hill. ISBN 0-07-140210-1.

**Quantitative finance**

- Brooks, Robert (2000).
*Building Financial Derivatives Applications with C++*. Westport: Praeger. ISBN 978-1567202878. - Brigo, Damiano; Fabio Mercurio (2006).
*Interest Rate Models - Theory and Practice with Smile, Inflation and Credit*(2nd ed.). London: Springer Finance. ISBN 978-3-540-22149-4. - Clewlow, Les; Chris Strickland (1998).
*Implementing Derivative Models*. New Jersey: Wiley. ISBN 0-471-96651-7. - Duffy, Daniel (2004).
*Financial Instrument Pricing Using C++*. New Jersey: Wiley. ISBN 978-0470855096. - Fabozzi, Frank J. (1998).
*Valuation of fixed income securities and derivatives, 3rd Edition*. Hoboken, NJ: Wiley. ISBN 978-1-883249-25-0. - Fabozzi, Frank J.; Sergio M. Focardi; Petter N. Kolm (2004).
*Financial Modeling of the Equity Market: From CAPM to Cointegration*. Hoboken, NJ: Wiley. ISBN 0-471-69900-4. - Shayne Fletcher; Christopher Gardner (2010).
*Financial Modelling in Python*. John Wiley and Sons. ISBN 978-0-470-74789-6. - Fusai, Gianluca; Andrea Roncoroni (2008).
*Implementing Models in Quantitative Finance: Methods and Cases*. London: Springer Finance. ISBN 978-3-540-22348-1. - Haug, Espen Gaarder (2007).
*The Complete Guide to Option Pricing Formulas, 2nd edition*. McGraw-Hill. ISBN 978-0071389976. - Hilpisch , Yves (2015).
*Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging*. New Jersey: Wiley. ISBN 978-1-119-03799-6. - Jackson, Mary; Mike Staunton (2001).
*Advanced modelling in finance using Excel and VBA*. New Jersey: Wiley. ISBN 0-471-49922-6. - Jondeau, Eric; Ser-Huang Poon; Michael Rockinger (2007).
*Financial Modeling Under Non-Gaussian Distributions*. London: Springer. ISBN 978-1849965996. - Joerg Kienitz; Daniel Wetterau (2012).
*Financial Modelling: Theory, Implementation and Practice with MATLAB Source*. Hoboken, NJ: Wiley. ISBN 978-0470744895. - Kwok, Yue-Kuen (2008).
*Mathematical Models of Financial Derivatives, 2nd edition*. London: Springer Finance. ISBN 978-3540422884. - Levy, George (2004).
*Computational Finance: Numerical Methods for Pricing Financial Instruments*. Butterworth-Heinemann. ISBN 978-0750657228. - London, Justin (2004).
*Modeling Derivatives in C++*. New Jersey: Wiley. ISBN 978-0471654643. - Löeffler, G; Posch, P. (2011).
*Credit Risk Modeling using Excel and VBA*. Hoboken, NJ: Wiley. ISBN 978-0470660928. - Rouah, Fabrice Douglas; Gregory Vainberg (2007).
*Option Pricing Models and Volatility Using Excel-VBA*. New Jersey: Wiley. ISBN 978-0471794646. - Antoine Savine and Jesper Andreasen (2018).
*Modern Computational Finance: Scripting for Derivatives and xVA*. Wiley. ISBN 978-1119540786. - Vladimirou, Hercules (2007). "Financial Modeling".
*Annals of Operations Research*. Norwell, MA: Springer.**151**. - Mantegna, Rosario N.; Kertesz, Janos (2010). "Focus on Statistical Physics Modelling in Economics and Finance".
*New Journal of Physics*.

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