Global patterns and extreme events in sovereign risk premia: a fuzzy vs deep learning comparative
Abstract
Investment in foreign countries has become more common nowadays and this implies that there may be risks inherent to these investments, being the sovereign risk premium the measure of such risk. Many studies have examined the behaviour of the sovereign risk premium, nevertheless, there are limitations to the current models and the literature calls for further investigation of the issue as behavioural factors are necessary to analyse the investor’s risk perception. In addition, the methodology widely used in previous research is the regression model, and the literature shows it as scarce yet. This study provides a model for a new of the drivers of the government risk premia in developing countries and developed countries, comparing Fuzzy methods such as Fuzzy Decision Trees, Fuzzy Rough Nearest Neighbour, Neuro-Fuzzy Approach, with Deep Learning procedures such as Deep Recurrent Convolution Neural Network, Deep Neural Decision Trees, Deep Learning Linear Support Vector Machines. Our models have a large effect on the suitability of macroeconomic policy in the face of foreign investment risks by delivering instruments that contribute to bringing about financial stability at the global level.
First published online 17 April 2024
Keyword : sovereign risk premium, fuzzy decision trees, neuro-fuzzy approach, deep neural decision trees, deep recurrent convolutional neural networks
How to Cite
Alaminos, D., Salas, M. B., & Fernández-Gámez, M. A. (2024). Global patterns and extreme events in sovereign risk premia: a fuzzy vs deep learning comparative. Technological and Economic Development of Economy, 30(3), 753–782. https://doi.org/10.3846/tede.2024.20488
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alaminos, D., Esteban, I., & Fernández-Gámez, M. A. (2020). Financial performance analysis in European football clubs. Entropy, 22(9), Article 1056. https://doi.org/10.3390/e22091056
Alaminos, D., Salas, M. B., & Fernández-Gámez, M. A. (2022). Quantum computing and deep learning methods for GDP growth forecasting. Computional Economics, 59, 803–829. https://doi.org/10.1007/s10614-021-10110-z
Andrade, S. C., Ekponon, A., & Jeanneret, A. (2023). Sovereign risk premia and global macroeconomic conditions. Journal of Financial Economics, 147(1), 172–197. https://doi.org/10.1016/j.jfineco.2022.07.003
Arellano, C., Bai, Y., & Mihalache, G. P. (2020). Monetary policy and sovereign risk in emerging economies (NK-default) (Working paper No. w26671). National Bureau of Economic Research. https://doi.org/10.3386/w26671
Aristei, D., & Martelli, D. (2014). Sovereign bond yield spreads and market sentiment and expectations: Empirical evidence from Euro area countries. Journal of Economics and Business, 76, 55–84. https://doi.org/10.1016/j.jeconbus.2014.08.001
Augustin, P., Boustanifar, H., Breckenfelder, J., & Schnitzler, J. (2018). Sovereign to corporate risk spillovers. Journal of Money, Credit and Banking, 50(5), 857–891. https://doi.org/10.1111/jmcb.12497
Augustin, P., Chernov, M., & Song, D. (2020). Sovereign credit risk and exchange rates: Evidence from CDS quanto spreads. Journal of Financial Economics, 137(1), 129–151. https://doi.org/10.1016/j.jfineco.2019.12.005
Badaoui, S., Cathcart, L., & El-Jahel, L. (2016). Implied liquidity risk premium in the term structure of sovereign credit default swap and bond spreads. The European Journal of Finance, 22(10), 825–853. https://doi.org/10.1080/1351847X.2014.996297
Badarau, C., Huart, F., & Sangaré, I. (2014). Sovereign risk premium and divergent fiscal policies in a Monetary Union. Revue d’économie politique, 124, 867–898. https://doi.org/10.3917/redp.246.0867
Baldacci, E., & Manmohan, K. (2010). Fiscal deficits, public debt and sovereign bond yields (IMF Working Paper WP/10/184). International Monetary Fund. https://doi.org/10.5089/9781455202188.001
Balima, W. H., Combes, J. L., & Minea, A. (2017). Sovereign debt risk in emerging market economies: Does inflation targeting adoption make any difference? Journal of International Money and Finance, 70, 360–377. https://doi.org/10.1016/j.jimonfin.2016.10.005
Bi, H. (2012). Sovereign default risk premia, fiscal limits, and fiscal policy. European Economic Review, 56(3), 389–410. https://doi.org/10.1016/j.euroecorev.2011.11.001
Bianchi, B. (2016). Sovereign risk premia and the international balance sheet: Lessons from the European crisis. Open Economies Review, 27, 471–493. https://doi.org/10.1007/s11079-015-9382-8
Bizuneh, M., & Geremew, M. (2021). Assessing the impact of COVID-19 pandemic on emerging market economies’ (EMEs) sovereign bond risk premium and fiscal solvency. Eastern Economic Journal, 47, 519–545. https://doi.org/10.1057/s41302-021-00201-y
Boitan, I. A., & Marchewka-Bartkowiak, K. (2022). Climate change and the pricing of sovereign debt: Insights from European markets. Research in International Business and Finance, 62, Article 101685. https://doi.org/10.1016/j.ribaf.2022.101685
Cathcart, L., Gotthelf, N. M., Uhl, M., & Shi, Y. (2020). News sentiment and sovereign credit risk. European Financial Management, 25(2), 261–287. https://doi.org/10.1111/eufm.12219
Cecchetti, S. (2020). An analysis of sovereign credit risk premia in the euro area: Are they explained by local or global factors? (Bank of Italy Temi di Discussione Working Paper No 1271). https://doi.org/10.2139/ssrn.3612941
Chen, K., Zhou, Y., & Dai, F. (2015, October). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE International Conference on Big Data (Big Data) (pp. 2823–2824). Santa Clara, CA, USA. IEEE. https://doi.org/10.1109/BigData.2015.7364089
Chen, Z., & Reitz, S. (2020). Dynamics of the European sovereign bonds and the identification of crisis periods. Empirical Economics, 58, 2761–2781. https://doi.org/10.1007/s00181-019-01653-0
Ciżkowicz, P., Parosa, G., & Rzońca, A. (2022). Fiscal tensions and risk premium. Empirica, 49(3), 833–896. https://doi.org/10.1007/s10663-022-09532-1
Comelli, F. (2012). Emerging market sovereign bond spreads: Estimation and back-testing. Emerging Markets Review, 13(4), 598–625. https://doi.org/10.1016/j.ememar.2012.09.002
Corradin, S., & Schwaab, B. (2023). Euro area sovereign bond risk premia before and during the Covid-19 pandemic. European Economic Review, 153, Article 104402. https://doi.org/10.1016/j.euroecorev.2023.104402
De Grauwe, P., & Ji, Y. (2012). Mispricing of sovereign risk and macroeconomic stability in the Eurozone. Journal of Common Market Studies, 50(6), 866–880. https://doi.org/10.1111/j.1468-5965.2012.02287.x
De Spiegeleer, J., Madan, D. B., Reyners, S., & Schoutens, W. (2018). Machine learning for quantitative finance: Fast derivative pricing, hedging and fitting. Quantitative Finance, 18(10), 1635–1643. https://doi.org/10.1080/14697688.2018.1495335
Della Corte, P., Jeanneret, A., & Patelli, E. D. (2023). A credit-based theory of the currency risk premium. Journal of Financial Economics, 149(3), 473–496. https://doi.org/10.1016/j.jfineco.2023.06.002
Di Cesare, A., Grande, G., Manna, M., & Taboga, M. (2012). Recent estimates of sovereign risk premia for euro-area countries (Bank of Italy Occasional Paper No 128). https://doi.org/10.2139/ssrn.2159218
Doshi, H., Jacobs, K., & Zurita, V. (2017). Economic and financial determinants of credit risk premiums in the sovereign CDS market. The Review of Asset Pricing Studies, 7(1), 43–80. https://doi.org/10.1093/rapstu/rax009
Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In A. Prieditis & S. Russell, (Eds.), Machine learning proceedings 1995 (pp. 194–202). Morgan Kaufmann. https://doi.org/10.1016/B978-1-55860-377-6.50032-3
Erdem, O., & Varli, Y. (2014). Understanding the sovereign credit ratings of emerging markets. Emerging Markets Review, 20, 42–57. https://doi.org/10.1016/j.ememar.2014.05.004
Fayek, A. R. (2020). Fuzzy logic and fuzzy hybrid techniques for construction engineering and management. Journal of Construction Engineering and Management, 146(7), Article 04020064. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001854
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Fontana, A., & Langedijk, S. (2019). The bank-sovereign loop and financial stability in the Euro area (JRC Working Papers in Economics and Finance No. 2019/10). Publications Office of the European Union.
Ghoddusi, H., Creamer, G. G., & Rafizadeh, N. (2019). Machine learning in energy economics and finance: A review. Energy Economics, 81, 709–727. https://doi.org/10.1016/j.eneco.2019.05.006
Gilchrist, S., Wei, B., Yue, V. Z., & Zakrajšek, E. (2022). Sovereign risk and financial risk. Journal of International Economics, 136, Article 103603. https://doi.org/10.1016/j.jinteco.2022.103603
Gumus, I. (2011). Exchange rate policy and sovereign spreads in emerging market economies. Review of International Economics, 19(4), 649–663. https://doi.org/10.1111/j.1467-9396.2011.00972.x
Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196–210. https://doi.org/10.1016/j.asoc.2014.12.028
Hamrouni, C., & Chaoui, S. (2022). 5G smart mobility management based fuzzy logic controller unit. Computers, Materials & Continua, 71(3), 4941–4953. https://doi.org/10.32604/cmc.2022.023732
Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. https://doi.org/10.1109/34.709601
Hofmann, B., Shim, I., & Shin, H. S. (2020). Bond risk premia and the exchange rate. Journal of Money, Credit and Banking, 52(S2), 497–520. https://doi.org/10.1111/jmcb.12760
Huang, C. W., & Narayanan, S. S. (2017, July). Deep convolutional recurrent neural network with attention mechanism for robust speech emotion recognition. In 2017 IEEE International Conference on Multimedia and Expo (ICME) (pp. 583–588). Hong Kong, China. IEEE. https://doi.org/10.1109/ICME.2017.8019296
Iara, A., & Wolff, G. (2014). Rules and risk in the Euro area. European Journal of Political Economy, 34, 222–236. https://doi.org/10.1016/j.ejpoleco.2014.02.002
Kadiric, S. (2022). The determinants of sovereign risk premiums in the UK and the European government bond market: The impact of Brexit. International Economics and Economic Policy, 19(2), 267–298. https://doi.org/10.1007/s10368-022-00535-8
Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 15(4), 580–585. https://doi.org/10.1109/TSMC.1985.6313426
Konopczak, K., & Konopczak, M., (2017). Impact of International capital flows on emerging markets’ sovereign risk premium – demand vs. vulnerability effect. Finance Research Letters, 23(C), 239–245. https://doi.org/10.1016/j.frl.2017.07.010
Lee, J., Kim, S., & Park, Y. J. (2017a). Investor sentiment and credit default swap spreads during the global financial crisis. Journal of Futures Markets, 37(7), 660–688. https://doi.org/10.1002/fut.21828
Lee, Y. C., Chung, P. H., & Shyu, J. Z. (2017b). Performance evaluation of medical device manufacturers using a hybrid fuzzy MCDM. Journal of Scientific and Industrial Research, 76(1), 28–31.
Linciano, N., Giordano, L., & Soccorso, P. (2013). Sovereign risk premia in the Euro Area and the role of contagion. Journal of Financial Management, Markets and Institutions, 1(1), 85–114.
Ma, M., & Mao, Z. (2019, June). Deep recurrent convolutional neural network for remaining useful life prediction. In Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1–4). San Francisco. https://doi.org/10.1109/ICPHM.2019.8819440
Malliaropulos, D., & Migiakis, P. (2018). The re-pricing of sovereign risks following the Global Financial Crisis. Journal of Empirical Finance, 49, 39–56. https://doi.org/10.1016/j.jempfin.2018.09.003
Maltritz, D., & Molchanov, A. (2013). Analyzing determinants of bond yield spreads with Bayesian Model Averaging. Journal of Banking and Finance, 37(12), 5275–5284. https://doi.org/10.1016/j.jbankfin.2013.07.007
Marshall, M. G., & Elzinga-Marshall, G. (2017). Global report 2017: Conflict, governance, and state fragility. Center for Systemic Peace.
Martinez, L. B., Terceño, A., & Teruel, M. (2013). Sovereign bond spreads determinants in Latin American countries: Before and during the XXI financial crisis. Emerging Markets Review, 17, 60–75. https://doi.org/10.1016/j.ememar.2013.08.004
Mpapalida, J., & Malikane, C. (2019). The determinants of sovereign risk premium in African countries. Journal of Risk and Financial Management, 12(1), Article 29. https://doi.org/10.3390/jrfm12010029
Nauck, D., & Kruse, R. (1997). New learning strategies for NEFCLASS. In Proceedings Seventh International Fuzzy Systems Association World Congress IFSA´97 (vol. IV, pp. 50–55). Academia Prague.
Nauck, D., Klawonn F., & Kruse, R. (1997). Foundations of neuro-fuzzy systems. Wiley.
Norouzi, M., Collins, M. D., Johnson, M., Fleet, D. J., & Kohli, P. (2015). Efficient non-greedy optimization of decision trees. In Advances in Neural Information Processing Systems 28 (NIPS 2015). The MIT Press.
Orlov, V. (2019) Solvency risk premia and the carry trades. Journal of International Financial Markets, Institutions and Money, 60, 50–67. https://doi.org/10.1016/j.intfin.2018.12.001
Özmen, M. U. (2019). Economic complexity and sovereign risk premia. Economics Bulletin, 39(3), 1714–1726.
Palić, P., Šimović, P. P., & Vizek, M. (2017). The determinants of country risk premium volatility: Evidence from a panel VAR model. Croatian Economic Survey, 19(1), 37–66. https://doi.org/10.15179/ces.19.1.2
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert System with Applications, 42(4), 2162–2172. https://doi.org/10.1016/j.eswa.2014.10.031
Prashanth, K. D., Parthiban, P., & Dhanalakshmi, R. (2018). Evaluation and ranking of criteria affecting the supplier’s performance of a heavy industry by fuzzy AHP method. Journal of Scientific and Industrial Research, 77(5), 268–270.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc.
Rawal, B., & Agarwal, R. (2019). Improving accuracy of classification based on C4.5 decision tree algorithm using big data analytics. In Advances in intelligent systems and computing: Vol. 711. Computational intelligence in data mining (pp. 203–211). Springer. https://doi.org/10.1007/978-981-10-8055-5_19
Rundo, F., Trenta, F., di Stallo, A. L., & Battiato, S. (2019). Machine learning for quantitative finance applications: A survey. Applied Sciences, 9(24), Article 5574. https://doi.org/10.3390/app9245574
Salas, M. B., Alaminos, D., Fernández-Gámez, M. A., & Callejón, A. M. (2020). Forecasting foreign exchange reserves using Bayesian model averaging-Naïve Bayes. The Singapore Economic Review, 1–22. https://doi.org/10.1142/S021759082048001X
Saltelli, A. (2002). Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145(2), 280–297. https://doi.org/10.1016/S0010-4655(02)00280-1
Sánchez-Roger, M., Oliver-Alfonso, M. D., & Sanchís-Pedregosa, C. (2019). Fuzzy logic and its uses in finance: A systematic review exploring its potential to deal with banking crises. Mathematics, 7(11), Article 1091. https://doi.org/10.3390/math7111091
Seoane, H. D. (2019). Time-varying volatility, default, and the sovereign risk premium. International Economic Review, 60(1), 283–301. https://doi.org/10.1111/iere.12353
Siklos, P. (2011). Emerging market yield spreads: Domestic, external determinants, and volatility spillovers. Global Finance Journal, 22(2), 83–100. https://doi.org/10.1016/j.gfj.2011.10.001
Sirignano, J., & Cont, R. (2019). Universal features of price formation in financial markets: Perspectives from deep learning. Quantitative Finance, 19(9), 1449–1459. https://doi.org/10.1080/14697688.2019.1622295
Stolbov, M. (2017). Determinants of sovereign credit risk: The case of Russia. Post-Communist Economies, 29(1), 51–70. https://doi.org/10.1080/14631377.2016.1237045
Thornton, J., & Vasilakis, C. (2017). The impact of fiscal rules on sovereign risk premia: International evidence. Finance Research Letters, 20, 63–67. https://doi.org/10.1016/j.frl.2016.09.008
Tkalec, M., Vizek, M., & Verbic, M. (2014). Balance sheet effects and original sinners’ risk premiums. Economic Systems, 38(4), 597–613. https://doi.org/10.1016/j.ecosys.2014.05.005
Alaminos, D., Salas, M. B., & Fernández-Gámez, M. A. (2022). Quantum computing and deep learning methods for GDP growth forecasting. Computional Economics, 59, 803–829. https://doi.org/10.1007/s10614-021-10110-z
Andrade, S. C., Ekponon, A., & Jeanneret, A. (2023). Sovereign risk premia and global macroeconomic conditions. Journal of Financial Economics, 147(1), 172–197. https://doi.org/10.1016/j.jfineco.2022.07.003
Arellano, C., Bai, Y., & Mihalache, G. P. (2020). Monetary policy and sovereign risk in emerging economies (NK-default) (Working paper No. w26671). National Bureau of Economic Research. https://doi.org/10.3386/w26671
Aristei, D., & Martelli, D. (2014). Sovereign bond yield spreads and market sentiment and expectations: Empirical evidence from Euro area countries. Journal of Economics and Business, 76, 55–84. https://doi.org/10.1016/j.jeconbus.2014.08.001
Augustin, P., Boustanifar, H., Breckenfelder, J., & Schnitzler, J. (2018). Sovereign to corporate risk spillovers. Journal of Money, Credit and Banking, 50(5), 857–891. https://doi.org/10.1111/jmcb.12497
Augustin, P., Chernov, M., & Song, D. (2020). Sovereign credit risk and exchange rates: Evidence from CDS quanto spreads. Journal of Financial Economics, 137(1), 129–151. https://doi.org/10.1016/j.jfineco.2019.12.005
Badaoui, S., Cathcart, L., & El-Jahel, L. (2016). Implied liquidity risk premium in the term structure of sovereign credit default swap and bond spreads. The European Journal of Finance, 22(10), 825–853. https://doi.org/10.1080/1351847X.2014.996297
Badarau, C., Huart, F., & Sangaré, I. (2014). Sovereign risk premium and divergent fiscal policies in a Monetary Union. Revue d’économie politique, 124, 867–898. https://doi.org/10.3917/redp.246.0867
Baldacci, E., & Manmohan, K. (2010). Fiscal deficits, public debt and sovereign bond yields (IMF Working Paper WP/10/184). International Monetary Fund. https://doi.org/10.5089/9781455202188.001
Balima, W. H., Combes, J. L., & Minea, A. (2017). Sovereign debt risk in emerging market economies: Does inflation targeting adoption make any difference? Journal of International Money and Finance, 70, 360–377. https://doi.org/10.1016/j.jimonfin.2016.10.005
Bi, H. (2012). Sovereign default risk premia, fiscal limits, and fiscal policy. European Economic Review, 56(3), 389–410. https://doi.org/10.1016/j.euroecorev.2011.11.001
Bianchi, B. (2016). Sovereign risk premia and the international balance sheet: Lessons from the European crisis. Open Economies Review, 27, 471–493. https://doi.org/10.1007/s11079-015-9382-8
Bizuneh, M., & Geremew, M. (2021). Assessing the impact of COVID-19 pandemic on emerging market economies’ (EMEs) sovereign bond risk premium and fiscal solvency. Eastern Economic Journal, 47, 519–545. https://doi.org/10.1057/s41302-021-00201-y
Boitan, I. A., & Marchewka-Bartkowiak, K. (2022). Climate change and the pricing of sovereign debt: Insights from European markets. Research in International Business and Finance, 62, Article 101685. https://doi.org/10.1016/j.ribaf.2022.101685
Cathcart, L., Gotthelf, N. M., Uhl, M., & Shi, Y. (2020). News sentiment and sovereign credit risk. European Financial Management, 25(2), 261–287. https://doi.org/10.1111/eufm.12219
Cecchetti, S. (2020). An analysis of sovereign credit risk premia in the euro area: Are they explained by local or global factors? (Bank of Italy Temi di Discussione Working Paper No 1271). https://doi.org/10.2139/ssrn.3612941
Chen, K., Zhou, Y., & Dai, F. (2015, October). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE International Conference on Big Data (Big Data) (pp. 2823–2824). Santa Clara, CA, USA. IEEE. https://doi.org/10.1109/BigData.2015.7364089
Chen, Z., & Reitz, S. (2020). Dynamics of the European sovereign bonds and the identification of crisis periods. Empirical Economics, 58, 2761–2781. https://doi.org/10.1007/s00181-019-01653-0
Ciżkowicz, P., Parosa, G., & Rzońca, A. (2022). Fiscal tensions and risk premium. Empirica, 49(3), 833–896. https://doi.org/10.1007/s10663-022-09532-1
Comelli, F. (2012). Emerging market sovereign bond spreads: Estimation and back-testing. Emerging Markets Review, 13(4), 598–625. https://doi.org/10.1016/j.ememar.2012.09.002
Corradin, S., & Schwaab, B. (2023). Euro area sovereign bond risk premia before and during the Covid-19 pandemic. European Economic Review, 153, Article 104402. https://doi.org/10.1016/j.euroecorev.2023.104402
De Grauwe, P., & Ji, Y. (2012). Mispricing of sovereign risk and macroeconomic stability in the Eurozone. Journal of Common Market Studies, 50(6), 866–880. https://doi.org/10.1111/j.1468-5965.2012.02287.x
De Spiegeleer, J., Madan, D. B., Reyners, S., & Schoutens, W. (2018). Machine learning for quantitative finance: Fast derivative pricing, hedging and fitting. Quantitative Finance, 18(10), 1635–1643. https://doi.org/10.1080/14697688.2018.1495335
Della Corte, P., Jeanneret, A., & Patelli, E. D. (2023). A credit-based theory of the currency risk premium. Journal of Financial Economics, 149(3), 473–496. https://doi.org/10.1016/j.jfineco.2023.06.002
Di Cesare, A., Grande, G., Manna, M., & Taboga, M. (2012). Recent estimates of sovereign risk premia for euro-area countries (Bank of Italy Occasional Paper No 128). https://doi.org/10.2139/ssrn.2159218
Doshi, H., Jacobs, K., & Zurita, V. (2017). Economic and financial determinants of credit risk premiums in the sovereign CDS market. The Review of Asset Pricing Studies, 7(1), 43–80. https://doi.org/10.1093/rapstu/rax009
Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In A. Prieditis & S. Russell, (Eds.), Machine learning proceedings 1995 (pp. 194–202). Morgan Kaufmann. https://doi.org/10.1016/B978-1-55860-377-6.50032-3
Erdem, O., & Varli, Y. (2014). Understanding the sovereign credit ratings of emerging markets. Emerging Markets Review, 20, 42–57. https://doi.org/10.1016/j.ememar.2014.05.004
Fayek, A. R. (2020). Fuzzy logic and fuzzy hybrid techniques for construction engineering and management. Journal of Construction Engineering and Management, 146(7), Article 04020064. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001854
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Fontana, A., & Langedijk, S. (2019). The bank-sovereign loop and financial stability in the Euro area (JRC Working Papers in Economics and Finance No. 2019/10). Publications Office of the European Union.
Ghoddusi, H., Creamer, G. G., & Rafizadeh, N. (2019). Machine learning in energy economics and finance: A review. Energy Economics, 81, 709–727. https://doi.org/10.1016/j.eneco.2019.05.006
Gilchrist, S., Wei, B., Yue, V. Z., & Zakrajšek, E. (2022). Sovereign risk and financial risk. Journal of International Economics, 136, Article 103603. https://doi.org/10.1016/j.jinteco.2022.103603
Gumus, I. (2011). Exchange rate policy and sovereign spreads in emerging market economies. Review of International Economics, 19(4), 649–663. https://doi.org/10.1111/j.1467-9396.2011.00972.x
Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196–210. https://doi.org/10.1016/j.asoc.2014.12.028
Hamrouni, C., & Chaoui, S. (2022). 5G smart mobility management based fuzzy logic controller unit. Computers, Materials & Continua, 71(3), 4941–4953. https://doi.org/10.32604/cmc.2022.023732
Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. https://doi.org/10.1109/34.709601
Hofmann, B., Shim, I., & Shin, H. S. (2020). Bond risk premia and the exchange rate. Journal of Money, Credit and Banking, 52(S2), 497–520. https://doi.org/10.1111/jmcb.12760
Huang, C. W., & Narayanan, S. S. (2017, July). Deep convolutional recurrent neural network with attention mechanism for robust speech emotion recognition. In 2017 IEEE International Conference on Multimedia and Expo (ICME) (pp. 583–588). Hong Kong, China. IEEE. https://doi.org/10.1109/ICME.2017.8019296
Iara, A., & Wolff, G. (2014). Rules and risk in the Euro area. European Journal of Political Economy, 34, 222–236. https://doi.org/10.1016/j.ejpoleco.2014.02.002
Kadiric, S. (2022). The determinants of sovereign risk premiums in the UK and the European government bond market: The impact of Brexit. International Economics and Economic Policy, 19(2), 267–298. https://doi.org/10.1007/s10368-022-00535-8
Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 15(4), 580–585. https://doi.org/10.1109/TSMC.1985.6313426
Konopczak, K., & Konopczak, M., (2017). Impact of International capital flows on emerging markets’ sovereign risk premium – demand vs. vulnerability effect. Finance Research Letters, 23(C), 239–245. https://doi.org/10.1016/j.frl.2017.07.010
Lee, J., Kim, S., & Park, Y. J. (2017a). Investor sentiment and credit default swap spreads during the global financial crisis. Journal of Futures Markets, 37(7), 660–688. https://doi.org/10.1002/fut.21828
Lee, Y. C., Chung, P. H., & Shyu, J. Z. (2017b). Performance evaluation of medical device manufacturers using a hybrid fuzzy MCDM. Journal of Scientific and Industrial Research, 76(1), 28–31.
Linciano, N., Giordano, L., & Soccorso, P. (2013). Sovereign risk premia in the Euro Area and the role of contagion. Journal of Financial Management, Markets and Institutions, 1(1), 85–114.
Ma, M., & Mao, Z. (2019, June). Deep recurrent convolutional neural network for remaining useful life prediction. In Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1–4). San Francisco. https://doi.org/10.1109/ICPHM.2019.8819440
Malliaropulos, D., & Migiakis, P. (2018). The re-pricing of sovereign risks following the Global Financial Crisis. Journal of Empirical Finance, 49, 39–56. https://doi.org/10.1016/j.jempfin.2018.09.003
Maltritz, D., & Molchanov, A. (2013). Analyzing determinants of bond yield spreads with Bayesian Model Averaging. Journal of Banking and Finance, 37(12), 5275–5284. https://doi.org/10.1016/j.jbankfin.2013.07.007
Marshall, M. G., & Elzinga-Marshall, G. (2017). Global report 2017: Conflict, governance, and state fragility. Center for Systemic Peace.
Martinez, L. B., Terceño, A., & Teruel, M. (2013). Sovereign bond spreads determinants in Latin American countries: Before and during the XXI financial crisis. Emerging Markets Review, 17, 60–75. https://doi.org/10.1016/j.ememar.2013.08.004
Mpapalida, J., & Malikane, C. (2019). The determinants of sovereign risk premium in African countries. Journal of Risk and Financial Management, 12(1), Article 29. https://doi.org/10.3390/jrfm12010029
Nauck, D., & Kruse, R. (1997). New learning strategies for NEFCLASS. In Proceedings Seventh International Fuzzy Systems Association World Congress IFSA´97 (vol. IV, pp. 50–55). Academia Prague.
Nauck, D., Klawonn F., & Kruse, R. (1997). Foundations of neuro-fuzzy systems. Wiley.
Norouzi, M., Collins, M. D., Johnson, M., Fleet, D. J., & Kohli, P. (2015). Efficient non-greedy optimization of decision trees. In Advances in Neural Information Processing Systems 28 (NIPS 2015). The MIT Press.
Orlov, V. (2019) Solvency risk premia and the carry trades. Journal of International Financial Markets, Institutions and Money, 60, 50–67. https://doi.org/10.1016/j.intfin.2018.12.001
Özmen, M. U. (2019). Economic complexity and sovereign risk premia. Economics Bulletin, 39(3), 1714–1726.
Palić, P., Šimović, P. P., & Vizek, M. (2017). The determinants of country risk premium volatility: Evidence from a panel VAR model. Croatian Economic Survey, 19(1), 37–66. https://doi.org/10.15179/ces.19.1.2
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert System with Applications, 42(4), 2162–2172. https://doi.org/10.1016/j.eswa.2014.10.031
Prashanth, K. D., Parthiban, P., & Dhanalakshmi, R. (2018). Evaluation and ranking of criteria affecting the supplier’s performance of a heavy industry by fuzzy AHP method. Journal of Scientific and Industrial Research, 77(5), 268–270.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc.
Rawal, B., & Agarwal, R. (2019). Improving accuracy of classification based on C4.5 decision tree algorithm using big data analytics. In Advances in intelligent systems and computing: Vol. 711. Computational intelligence in data mining (pp. 203–211). Springer. https://doi.org/10.1007/978-981-10-8055-5_19
Rundo, F., Trenta, F., di Stallo, A. L., & Battiato, S. (2019). Machine learning for quantitative finance applications: A survey. Applied Sciences, 9(24), Article 5574. https://doi.org/10.3390/app9245574
Salas, M. B., Alaminos, D., Fernández-Gámez, M. A., & Callejón, A. M. (2020). Forecasting foreign exchange reserves using Bayesian model averaging-Naïve Bayes. The Singapore Economic Review, 1–22. https://doi.org/10.1142/S021759082048001X
Saltelli, A. (2002). Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145(2), 280–297. https://doi.org/10.1016/S0010-4655(02)00280-1
Sánchez-Roger, M., Oliver-Alfonso, M. D., & Sanchís-Pedregosa, C. (2019). Fuzzy logic and its uses in finance: A systematic review exploring its potential to deal with banking crises. Mathematics, 7(11), Article 1091. https://doi.org/10.3390/math7111091
Seoane, H. D. (2019). Time-varying volatility, default, and the sovereign risk premium. International Economic Review, 60(1), 283–301. https://doi.org/10.1111/iere.12353
Siklos, P. (2011). Emerging market yield spreads: Domestic, external determinants, and volatility spillovers. Global Finance Journal, 22(2), 83–100. https://doi.org/10.1016/j.gfj.2011.10.001
Sirignano, J., & Cont, R. (2019). Universal features of price formation in financial markets: Perspectives from deep learning. Quantitative Finance, 19(9), 1449–1459. https://doi.org/10.1080/14697688.2019.1622295
Stolbov, M. (2017). Determinants of sovereign credit risk: The case of Russia. Post-Communist Economies, 29(1), 51–70. https://doi.org/10.1080/14631377.2016.1237045
Thornton, J., & Vasilakis, C. (2017). The impact of fiscal rules on sovereign risk premia: International evidence. Finance Research Letters, 20, 63–67. https://doi.org/10.1016/j.frl.2016.09.008
Tkalec, M., Vizek, M., & Verbic, M. (2014). Balance sheet effects and original sinners’ risk premiums. Economic Systems, 38(4), 597–613. https://doi.org/10.1016/j.ecosys.2014.05.005