Term Structure Models in the Banking Sector

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Creating the environment in which financial operations can flow in a free an uninhibited manner is a crucial step toward the enhancement of a bank’s overall performance. However, the identified goal requires introducing certain control tools that will allow embracing the external and internal factors, thus, modelling possible outcomes (Wu & Xia 2015). The concept of a term structure allows exploring the possibilities that lie ahead of a financial organisation based on the sum of factors to which the firm is exposed in the context of the global economy realm (Bauer & Rudebusch 2015). According to the existing definition, a term structure model is the visualisation of “the relationship between yield to maturity and time to maturity” (Whittington 2015, p. 231). In other words, the framework allows determining the maturity levels and, thus, contributes to not only a detailed analysis of the existing financial options but also the identification of possible outcomes of choosing a specific one.

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At this point, one must note that, though often conflated, the notions of a term structure and a yield curve are not entirely synonymous. The yield curve is typically viewed as the graphic representation of the relationships between the variables of the term structure (Whittington 2015). The two concepts, thus, often intersect and are typically considered in tandem.

The phenomenon of Term Structure Models (TSMs) appeared as a result of the implementation of the Negative Interest Rate Policy (NIRP) (Nielsen 2016). By definition, the identified regulation implies setting the target interest rates below the level of zero percent. The specified policy allows addressing the situation in which banks experience stagnation due to the unwillingness to invest financial resources that can be observed at the time of deflation (Garcıa-Schmidt & Woodford 2015). In the identified scenario, even reducing the interest rate to its bare minimum may fail to compel people to spend and invest their financial resources. Herein lies the significance of term structures as the means of measuring the effectiveness of the proposed policies. Implying an analysis of the correlation between the current interest rates of bond yields and the maturity levels thereof, the approach allows defining the threats to which banks are currently exposed and, therefore, develop an elaborate policy that will help retain the current assets, at the same time avoiding significant losses (Allen, Barlevy & Gale 2017).

There is an array of term structure models that allow identifying the available investment options and improving the performance of banks. The existing models are typically classified based on their ability to capture a particular time period in the market development and define the further changes therein. Static models, which focus on a particular point in time, are traditionally juxtaposed to dynamic ones, which allow exploring the change in interest rates in a certain time slot (Camagni, Capello & Caragliu 2014).

As far as the choice of a TSM is concerned, one must mention the efficacy of the existing taxonomy that draws a line between different types of models based on the effects that they produce and the purpose that they are supposed to serve in the context of an economic environment. For example, Affine Term Structures (ATSs) tend to view nominal interest rates as typically non-negative data (Hamilton & Wu 2014). Examples of early types of ATSs include the Nelson-Siegel Model, the Affine Short Rate Model, Vasicek-type models, where x is Gaussian, Cox–Ingersoll–Ross (CIR)-type models, in which x incorporates independent square-root processes, and Mixture models, where x includes affine processes that can potentially be correlated (Jafari & Abbasian 2017). While one must give the identified frameworks credit for providing extensive opportunities for determining the changes in interest rates in the future, the inability to embrace several factors at once during the analysis makes the specified approaches rather weak (Shao 2017). Therefore, a comprehensive framework that could embrace the benefits of all approaches, and in which the flaws of previous models could be addressed, must be designed. It is suggested that the incorporation of the Gaussian Affine Model (GAM) and the Black Model (BM) should be deemed as the key components of a new and improved tool for determining the changes in interest rates (Chung, Hui & Li 2016). Nelson-Siegel Term Structure Model (NSTSM) is, perhaps, one of the best-known ATSs. According to the existing definition, the framework serves as the tool for identifying and studying the term structure of interest rates (Chen & Niu 2014). Therefore, the approach allows delineating the yield curve of the bonds. Thus, the essential risks to which the bonds in question may be exposed in the realm of the target market can be assessed successfully. The stochastic volatility models, which are typically considered an alternative to ATSs, in turn, suggest that the idea of the random distribution of essential stochastic processes should be incorporated into the assessment framework (Berg 2015).

Diffusion Short-Rate Models (DSRMs), in turn, also provide extensive opportunities for identifying the possible development of the short rate of derivatives. DSRMs are typically viewed as pioneers in the attempts at measuring the opportunities associated with the further evolution of the interest rate. Therefore, DSRMs, which imply taking the short, or instantaneous, interest rate as the foundation for the following analysis and forecast of the interest rate development, cannot be viewed as fully credible tools for defining the evolution of the subject matter, especially in the context of the global economy (Baldeaux, Fung, Ignatieva & Platen 2015).

Nevertheless, the adoption of the framework that incorporates the essential characteristics of GAM and BM should be regarded as the most promising strategy for enhancing the management of interest rates in the context of the contemporary banking environment. It should be borne in mind, though, that the identified approaches have their flaws, which can affect the accuracy of the forecasts of interest rate development. The framework that combines the elements of both will most likely require that x should follow a multi-factor Ornstein-Uhlenbeck process (Avalon et al. 2017). The latter, in turn, borrows heavily from the Vasicek model and implies that the parameter known as shrinkage drift should be incorporated into the identification of changes in the current interest rate levels. Indeed, a closer look at the contemporary banking environment will reveal that there is a range of factors defining the volatility of the interest rates. While previously, the evaluation of a single factor and the estimation of only one relevant parameter could contribute to the identification and delivery of plausible results, the modern financial realm demands a multi-factor analysis. Because of the array of factors determining the fluctuations in investment rates, from financial to economic to cultural and environmental ones, one must design the model that could embrace the identified phenomena and their impacts on the investment rates. BM, in turn, should be viewed as an essential addition to the design of a comprehensive framework that will allow taking a variety of factors affecting the interest rates in the context of the global economy into account. The approach, therefore, allows determining the caps and floors of pricing. Furthermore, BM is fully compatible with GAM, which means that it can be used for forecasting the changes in interest rates successfully. Consequently, the combination of the two approaches must be recognised as the foundation for developing an enhanced tool for making financial forecasts in the target area.

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One must admit that the suggested approach toward assessing the fluctuations in interest rates is going to be rather generalised. Indeed, because of the necessity to correlate different elements of GAM and BM, one will have to adjust the frameworks so that an extension of the two could be created. Nevertheless, the promoted approach is bound to have a large impact on the identification of opportunities for increasing interest rates. Particularly, the new approach will open chances for removing the rate of decline as a factor affecting the growth of interest rates. Consequently, the opportunities for an increase in financial gains can become open.

Creating opportunities for banks to thrive in the realm of the global economy is a crucial step toward improving the well-being of the state economy and, therefore, enhancing the process of the economic growth. For this purpose, the reconsideration of the approach toward the management of investment rates should be viewed as a necessity. The use of a combined framework for analysing the changes in investment rates and predicting possible outcomes of specific measures taken by banks will allow avoiding negative investment rates, which are typically viewed as undesirable. Herein lies the importance of designing the framework that could incorporate the advantages of GAM and BM so that the prognosis for interest rates could be determined accurately.

Reference List

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Baldeaux, J, Fung, MC, Ignatieva, K & Platen, E 2015, ‘A hybrid model for pricing and hedging of long-dated bonds’, Applied Mathematical Finance, vol. 22, no. 4, pp. 366-398.

Bauer, MD & Rudebusch, GD 2016, ‘Monetary policy expectations at the zero lower bound’, Journal of Money, Credit and Banking, vol. 48, no. 7, pp. 1439-1465.

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Hamilton, JD & Wu, JC 2014, ‘Testable implications of affine term structure models’, Journal of Econometrics, vol. 178, no. 2, pp. 231-242.

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Jafari, MA & Abbasian, S 2017, ‘The moments for solution of the Cox-Ingersoll-Ross Interest Rate Model’, Journal of Finance and Economics, vol. 5, no. 1, pp. 34-37.

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Wu, JC & Xia, FD 2016, ‘Measuring the macroeconomic impact of monetary policy at the zero lower bound’, Journal of Money, Credit and Banking, vol. 48, no. 2-3, pp. 253-291.

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