Search Results (6)

This study develops a model to predict and explain short-term fluctuations in the Brazilian local currency interest rate term structure. The model relies on the potential relationship between these movements and key macroeconomic factors. The methodology consists of two stages. First, the Svensson model is applied to fit the daily yield curve data. This involves maximizing the R² statistic in an OLS regression, following the Nelson–Siegel approach. The median decay parameters are then fixed for subsequent estimations. In the second stage, with the daily yield curve estimates in hand, another OLS regression is conducted. This regression incorporates the idea that Svensson’s betas are influenced by macroeconomic variables. Full article

This work studies the term structure dynamics in the natural gas futures market, focusing on the Dutch Title Transfer Facility (TTF) daily futures prices. At first, using the whole dataset, we compared the in-sample fitting performance of three models: the four-factor dynamic Nelson–Siegel–Svensson (4F-DNSS) model, the five-factor dynamic De Rezende–Ferreira (5F-DRF) model, and the B-spline model. Our findings suggest that B-spline is the method that achieves the best in-line fitting results. Then, we turned our attention to forecasting, using data from 20 January 2011 to 13 May 2022 as the training set and the remaining data, from 16 May to 13 June 2022, for day-ahead predictions. In this second part of the work we combined the above mentioned models (4F-DNSS, 5F-DRF and B-spline) with a Nonlinear Autoregressive Neural Network (NAR-NN), asking the NAR-NN to provide parameter tuning. All the models provided accurate out-of-sample prediction; nevertheless, based on extensive statistical tests, we conclude that, as in the previous case, B-spline (combined with an NAR-NN) ensured the best out-of-sample prediction. Full article

Peritendinous adhesions are a frequent occurrence following tenolysis and present a major clinical challenge regarding prevention and management, with no recovery assured from conservative or surgical approaches. Herein, we investigated the effectiveness of Hyaloglide^®, a hyaluronan gel-based product with a novel autocross-linked technology, in a rabbit model affected by tenolysis on the flexor digitorum communis tendon (FDC). A 1.5-cm-long scrubbing of the tendon surface was performed bilaterally to induce peritendinous adhesion on FDC of 30 animals with subsequent application of Hyaloglide^® on the surrounding injured area, in one randomly chosen tendon. The contralateral tendon was treated with saline solution as the control. We sacrificed the rabbit models after 45 days of surgery and quantitatively assessed the generation of peritendinous adherence and regeneration of the tendon sheaths using histological (hematossyline-eosine, masson’s trichromic), histomorphometrical (Tang score, Soslowsky Svesson, and Cook score), light electron microscopic, and gene expression investigations. Four rabbits were devoted to biomechanical analysis. Peritendinous adhesions were limited in Hyaloglide^®-treated tendons; moreover, well-regenerated tendon sheaths were observed conversely to untreated tendons presenting with extensive areas of adhesions on the tendon surface. Histomorphometrical analysis revealed an adhesion score (Tang score) significantly better in the treated group (p = 0.001 *) compared to the control group. Moreover, the Soslowsky, Svensson, and Cook score parameters revealed a significantly improved regeneration for fiber structure, cellularity, and vascularity in the treated group (p = 0.001 *). No differences were reported for cartilaginous formation (p = 0.08). Gene expression analysis showed a significant increase in collagen type I expression in the treated group compared to the control group, while metalloprotease 1 and 9 were significantly increased in the control group. Biomechanical analysis did not show significant differences in both groups. Hyaloglide^® treatment was safe and well-tolerated, generating improved tissue status. Local application of Hyaloglide^® prevents adhesion formation after tenolysis and promotes normal healing with regeneration of the synovial sheath in a rabbit model. Full article

The paper presents an alternative approach to measuring systemic illiquidity applicable to countries with frontier and emerging financial markets, where other existing methods are not applicable. We develop a novel Systemic Illiquidity Noise (SIN)-based measure, using the Nelson–Siegel–Svensson methodology in which we utilize the curve-fitting error as an indicator of financial system illiquidity. We empirically apply our method to a set of 10 divergent Central and Eastern Europe countries—Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia—in the period of 2006–2020. The results show three periods of increased risk in the sample period: the global financial crisis, the European public debt crisis, and the COVID-19 pandemic. They also allow us to identify three divergent sets of countries with different systemic liquidity risk characteristics. The analysis also illustrates the impact of the introduction of the euro on systemic illiquidity risk. The proposed methodology may be of consequence for financial system regulators and macroprudential bodies: it allows for contemporaneous monitoring of discussed risk at a minimal cost using well-known models and easily accessible data. Full article

A dynamic version of the Nelson-Siegel-Svensson term structure model with time-varying factors is considered for predicting out-of-sample maturity yields. Simple linear interpolation cannot be applied to recover yields at the very short- and long- end of the term structure where data are often missing. This motivates the use of dynamic parametric term structure models that exploit both time series and cross-sectional variation in yield data to predict missing data at the extreme ends of the term structure. Although the dynamic Nelson–Siegel–Svensson model is weakly identified when the two decay factors become close to each other, their predictions may be more accurate than those from more restricted models depending on data and maturity. Full article

In this paper, we examined and compared the forecast performances of the dynamic Nelson–Siegel (DNS), dynamic Nelson–Siegel–Svensson (DNSS), and arbitrage-free Nelson–Siegel (AFNS) models after the financial crisis period. The best model for the forecast performance is the DNSS model in the middle and long periods. The AFNS is inferior to the DNS model for long-period forecasting. In U.S. bond markets, AFNS is shown to be superior to DNS in the U.S. However, for Japanese data, there is no evidence that the AFNS is superior to the DNS model in the long forecast horizon. Full article