2022
DOI: 10.3390/risks10020036
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Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques

Abstract: We compare parametric and machine learning techniques (namely: Neural Networks) for in–sample modeling of the yield curve of the BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied the Dynamic De Rezende–Ferreira five–factor model with time–varying decay parameters and a Feed–Forward Neural Network to the bond market data of the BRICS countries. To enhance the flexibility of the parametric model, we also introduce a new procedure to estimate the time varying parameters that si… Show more

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Cited by 8 publications
(8 citation statements)
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“…The choice of the method was based on the advantages it confers compared to other procedures. Thus, it was observed that NNs contribute to an increase in the predictability of stock prices compared to conventional methods (Sahiner et al, 2021), capture information in a more comprehensive manner (Chang et al, 2022), have a high error tolerance (Mijwel, 2018), accurately process sets of homogeneous data (Castello & Resta, 2022;Tripathi et al, 2022), demonstrate a better long-term predictive power compared to statistical methods (Jan & Ayub, 2019), and are superior to regression models or those based on the approach technique (Ozdemir & Tokmakcioglu, 2022). Compared to linear models, NNs can provide solutions to complex relationships without being reprogrammed (Caliskan Cavdar & Aydin, 2020;Talwar et al, 2022).…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…The choice of the method was based on the advantages it confers compared to other procedures. Thus, it was observed that NNs contribute to an increase in the predictability of stock prices compared to conventional methods (Sahiner et al, 2021), capture information in a more comprehensive manner (Chang et al, 2022), have a high error tolerance (Mijwel, 2018), accurately process sets of homogeneous data (Castello & Resta, 2022;Tripathi et al, 2022), demonstrate a better long-term predictive power compared to statistical methods (Jan & Ayub, 2019), and are superior to regression models or those based on the approach technique (Ozdemir & Tokmakcioglu, 2022). Compared to linear models, NNs can provide solutions to complex relationships without being reprogrammed (Caliskan Cavdar & Aydin, 2020;Talwar et al, 2022).…”
Section: Methodsmentioning
confidence: 99%
“…The use of variables in ANN processing by various researchers includes historical values (Chang et al, 2022), stock returns (Jan & Ayub, 2019), stock selection methods and portfolio optimisation (Ozdemir & Tokmakcioglu, 2022), traded derivative contracts (TFDCs), exchange rate, daily COVID-19 cases and deaths (Naveed et al, 2023), bond markets of BRICS countries (Castello & Resta, 2022), and capital markets indices (Sahiner et al, 2021). ANN modelling is preferred over GARCH and EGARCH models as neural network prediction models demonstrate improved forecast accuracy (Sahiner et al, 2021).…”
Section: Literature Reviewmentioning
confidence: 99%
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“…Concerning the estimation process of β, we applied an approach organized into two stages. At first, following [102], we identified for each time t the optimal combination [ τ1 (t), τ2 (t)], and hence β(t), as the weights vector associated with the lowest root mean square error (RMSE). Then, we determined the average values of τj (j = 1, 2) to derive the optimal estimate of β * (t) for each available day.…”
Section: Parametric Factor Modelsmentioning
confidence: 99%
“…After that, other bond yield curve modeling methods were developed by the researchers. Several bond yield curve modeling methods are widely used by researchers, including Dynamic Nelson-Siegel Model [3]- [6], Factor augmented VAR and the Nelson and Siegel [7], the Nelson-Siegel model with GARCH and EGARCH volatility [8], Segmented Term Structure Models [9], dynamic natural cubic spline model [10], tractable dynamic factor models [11], and Machine Learning Techniques [12]. In modeling the bond yield curve, the parsimony method is needed [2].…”
Section: Introductionmentioning
confidence: 99%