Speaking about financial modelling in its broadest meaning, it can be stated that modelling is about estimation and forecast. Estimation and forecast, rather than determination, is based upon the fact that the main purpose of financial modelling is decision making and “[u]ncertainties lie at the heart of business decision making in many different kinds of corporations” (Sungard, 2010a). Financial modelling is the capability of effectively analyzing complex and uncertain situations, related to making a strategic decision, which allows considering a large number of variants in the assumption, “what will happen, if?”, and implementing them without losing investments.
With the increased number of pressuring factors in making a decision, the significance of financial modelling as a financial instrument becomes specifically important. In that regard, this paper provides a general overview of financial models, specifically focusing on risk models, related to aspects such as their usage in finances, strengths and weaknesses, and their operations and benefits, based on the example of Value at Risk (VAR) model.
The definition of the financial model can go beyond the broad explanation provided in the introduction. In that regard, the usage of the term model in financial context implies three categories, fundamental, phenomenological and statistical model (Derman, 1996). A statistical model can be defined as “a regression or best-fit between different data sets”, while a phenomenological model is a descriptive visualization of a phenomenon that cannot be directly observed (Derman, 1996). The fundamental model, on the other hand, implies drawing dynamical interferences from a system of postulates and data. Regardless of the type, the use of models can carry various types of risks. The model risks are the risk arising from the use of a model which cannot perform its function accurately, i.e. evaluation of market prices in pricing models, estimation of the probability of future losses in measurement models (Kato and Yoshiba, 2000). There are several types of model risks, which can be related to the possible source of error. The types of model risks can be classified as follows:
- Incorrect model – incorrect models imply such instances as omitting one or several factors influencing the valuation. Incorrect assumptions, e.g. dynamics, relationships, factors, etc.
- Correct model, incorrect solution – such type implies technical mistake in finding the analytical solution to a model.
- Correct model, inappropriate use -such risk might arise when expanding their scope of the intended model use, where “a model will be used in ways its creator never intended” (Derman, 1996, p.7).
- Bad Approximation – such instance implies a correctly formulated problem, which might contain errors in its numerical solution.
- Bad data – such source of risk can be seen common, where the data used in estimation can be determinant in model evaluation. The data might be unstable, i.e. historical data not providing a good estimation of future values (Derman, 1996, p.8), or as in the case of pricing models, market data might contain errors (Kato and Yoshiba, 2000, p.129).
In that regard, such sources of the risks can provide a definition of model risk, which can be understood from a situation, in which identically calibrated models produce different outcomes, with one only being true. In such a case, the risk of using the wrong outcome can be regarded as a risk model (Rebonatop.1).
The Use of Models in Finance
The implementation of financial models is generally a simulation of activities, where activities, cash flows, investments, etc, serve as variables, which causality is assumed as well as the stability of such causal relation. Identifying the outcome of the dependent variable affecting the independent, such relation can be represented mathematically, which embodiment in software and the human environment represents a financial model (Derman, 1996, p.5). The use of the model in finance can be seen through supporting the decision-making process in a financial problem, through the provision of recommendations that support a particular solution. An example of a relationship between financial modelling and a financial problem can be seen through the cases of JP Morgan and SK Securities, in which investment strategies were based on financial models that evaluated trade-offs in risks and return (Ho and Yi, 2004, p.6). Such evaluation can be a decisive factor in making a decision whether to invest or not.
Strengths and Weaknesses
The strengths of financial models are their ability to translate such abstract aspects as risks into quantifiable entities, which can be compared and cross-examined against other variables, such as the return on investments. Accordingly, financial models allow enabling decision-makers to see the relationship between the different variables, which are based on large volumes of data, which cannot be observed otherwise. In that regard, financial models are mostly mathematical formulas translated to software, handling large databases, and thus, the insight such models provide has the advantage of saving time for the decision-makers. Finally, on a more global scale, financial models enable managers to build a framework, which plays a significant role “in the optimal functioning of the market” (Ho and Yi, 2004, p.6).
The main weakness of the financial model is that it is simply a model, i.e. a hypothesis or a theory, and thus, they are overwhelmed with uncertainty and assumptions. Additionally, despite the fact that mathematics is an exact science, in the case of financial modelling it is only a language of representation, and thus, the accuracy of the model is based on the understanding of the market, its dynamics, and the risk factors, many of which are indirectly measurable. Additionally, financial models are mostly uni-purpose, i.e. serving only a single task, and thus, there are no ultimate financial models, where the use of the model for other purposes is one of the sources of model risks.
Value at Risk
One example of a financial model is VAR, which “measure the worst expected loss under normal market conditions over a specific time interval” (Benninga and Wiener, 1998). Such model of risk assessment gained popularity from regulators, investors and management, is expressed in dollars and calculating risks for different securities from the short and long positions (Ho and Yi, 2004, p.527). Another benefit can be seen in that VAR provides an estimation of a worst-case scenario, which representation across different securities and portfolios provide management with a comparable perspective over individual or a group of portfolios, securities, or trading desks (Ho and Yi, 2004). The operation of the model is based on providing a particular confidence level (95%-95%) estimation over a specific time period of a probability of a loss of a dollar value, i.e. VAR of a portfolio over 6 months being equal to $ 100,000 at 95% means that there is 95% probability that there will not be a loss bigger than $100,000 over six months, or there is 5% probability that a loss bigger than $100,000 will occur.
Such worst-case scenarios can be regarded as risk events, which are mostly related to operational disasters in financial institutions. Thus, a risk event can be perceived as a combination of risks of something going unexpectedly wrong. The common element of such risks is the difficulty for the management to influence whether the risk event takes place (Sungard, 2010b). In financial modelling use of risk, events can be used in risk calculation, and although it cannot be estimated the probability of the occurrence of such event, the severity of the impact can be assessed (Sungard, 2010a).
It can be concluded that financial modelling is a useful instrument in business. The significance of such an instrument is specifically evident in the decision-making process. Risk management is a common financial model, among which VAR can be specifically outlined. Managing risk through financial models is an effective finance tool, given that all their advantages and disadvantages are acknowledged and taken into consideration.
BENNINGA, S. & WIENER, Z. 1998. Value-at-Risk (VaR). Mathematica in Education and Research, 7, 1-8.
DERMAN, E. 1996. Quantitative Strategies Research Notes: Model Risk. Goldman, Sachs & Co. Web.
HO, T. S. Y. & YI, S.-B. 2004. The Oxford guide to financial modeling : applications for capital markets, corporate finance, risk management, and financial institutions, Oxford ; New York, Oxford University Press.
KATO, T. & YOSHIBA, T. 2000. Model Risk and Its Control. Institute of Monetary and Economic Studies. Web.
REBONATO, R. Theory and Practice of Model Risk Management. Quantitative Research Centre (QUARC) of the Royal Bank of Scotland. Web.
SUNGARD. 2010a. Business Risk. Web.
SUNGARD. 2010b. Operational Risk. Web.