top of page
  • Writer's pictureWilliam Webster

Financial Product Valuation: Part Three-Models


The third instalment of our series on financial valuations delves into the intricate realm of using models for determining the value of financial assets. This exploration is crucial for anyone involved in finance, as it sheds light on both the potential and the pitfalls of relying on valuation models.


Defining Financial Models

At its core, a financial model is an algorithm into which data is input to generate an output—in this case, the value of a financial position. The adage "garbage in, garbage out" rings particularly true in the context of financial models. The accuracy of the output is directly contingent on the quality of the inputs.


The Complexity of Option Valuation

Take, for instance, the valuation of options, which can be either call or put options. Various models can be applied to value these, with the Black-Scholes model being one of the most renowned. The chosen model depends on the underlying asset being valued. Essential inputs for these models include the strike price, underlying price, time to expiry, implied volatility, and interest rate.


The Accuracy of Inputs

In markets where options are deeply liquid, like foreign exchange or interest rates, the adoption of a standard valuation model minimizes room for error. Since everyone uses the same model with similar inputs, the discrepancy in valuations is reduced. However, this does not guarantee absolute accuracy. Errors may arise more from operational risks related to data collection rather than from the model assumptions.


The Temptation and Risks of Financial Models

The seeming simplicity of these models can be misleading. While they offer a seemingly straightforward solution for valuing options, they can lead to significant issues when applied to more complex or less transparent financial assets.


The Challenge of Non-Market Observables

A critical issue arises with inputs that are not market observables. For instance, in models relying on correlation data, historical inputs do not necessarily predict future behaviours accurately. This limitation is particularly glaring in models designed for valuing bespoke and structured products, where the accuracy of inputs is less certain.


Discrepancy Between Model and Market Values

Another significant concern is the potential discrepancy between a model's theoretical price and the actual market price, especially for complex, illiquid financial products. In rapidly declining markets, the lack of buyers can lead to substantial discounts from the so-called 'fair value.'


Types of Models and Their Implications


We can categorize models into two types:

  1. Widely Used Models: These are generally safe as the market uses them, and their inputs are clear and transparent.

  2. Custom In-House Models: These are developed to value complex and potentially illiquid positions. However, they are riskier due to potential discrepancies in valuations and liquidity issues.


Potential Risks and Audit Concerns

Valuing illiquid products using in-house models can result in receiving a price far below expectations, impacting the profit and loss account directly. Additionally, such models are often subject to scrutiny by auditors. If found deficient, they may necessitate adjustments, leading to adverse effects on the profit and loss account.


The Devil in the Detail

In summary, while using models for valuation is a legitimate and often necessary practice in finance, the effectiveness and reliability of these models depend heavily on the details. It is crucial to scrutinize the inputs, understand the model's assumptions, and assess its applicability to the specific financial asset in question. Only then can one ensure that the valuation process is robust and fit for purpose.


The next article in this series will look at valuation by breaking the asset down into component parts.

4 views0 comments

Comments


bottom of page