Deep Neural Networks to Ensure the Quality of Calculated Yield Curves in Banking

Sammanfattning: Yield curves are of great importance within the financial sector and are, among other things, used as indicators of future economic growth. A curve that is upward sloping implies that investors expect positive economic growth, whereas a downward sloping curve is considered as a warning of a forthcoming recession. It is critical that these curves are actual reflections of the market. Unexpected changes in some parts of the curves should only occur if there have been actual changes in the market, however, this is not always the case and the curves are therefore continuously monitored and maintained. A potential solution to further ensure the quality of the curves is by the application of deep neural networks. The purpose of this study is to examine whether deep architectures are capable of predicting yield curves accurately. If this can be shown, the predictions can be further used to detect anomalies in yield curves estimated by the banks. Three models are compared in short-term and long-term predictions of yield curves; the Random Walk approach (RW) serving as the baseline and a point of reference, a Long Short-Term Memory Network (LSTM) and a Temporal Convolutional Network (TCN). The latter two have shown state-of-the-art results within time series forecasting and sequences modelling tasks and were therefore chosen to further investigate in this study. According to the experiments of this study, the RW approach was most accurate in one-day-ahead predictions, however, the method was statistically outperformed by the deep architectures in longer forecast horizons. For instance, in the case of 120-days-ahead forecasts, the TCN showed an increase of 82% in performance (Root Mean Squared Error) in comparison with the RW approach and the LSTM network an increase of 56%. It was concluded that the RW approach should be the default option in case of one-day-ahead forecasts, but that deep architectures have great potential in providing further assurance of the quality of yield curves in case of longer forecast horizons.