Formació financera i productes d’estalvi a llarg termini

[cat]Arran de l’última crisi financera i econòmica viscuda al nostre país s’han alçat veus que indiquen que una de les causes que ha pogut agreujar els seus efectes ha estat la baixa formació financera de la població. Recents estudis ho confirmen i propugnen la incorporació de programes formatius per als més joves. No obstant això, probablement el sector de la població més afectat per la comercialització de productes financers inadequats ha estat la població de més edat. Per això, en aquest treball es planteja la qüestió inversa, és a dir, si no haurien de ser les pròpies entitats financeres les que haurien de procurar distribuir, sobretot entre els més grans, productes adequats al seu nivell de formació i aversió al risc. En aquest context, s’analitza en quina mesura alguns productes financers dissenyats per gestionar l’estalvi (especialment entre els més grans o de cara a la jubilació) aconsegueixen els objectius desitjables. Finalment, es llança una proposta perquè siguin els estalviadors mateixos els que puguin gestionar els seus recursos i, d’aquesta manera, evitar, al menys en part, alguns dels costos de gestió que comporten aquests productes i així fomentar la competència en el sector.[spa]A raíz de la última crisis financiera y económica vivida en nuestro país se han alzado voces que indican que una de las causas que ha podido agravar sus efectos ha sido la baja formación financiera de la población. Recientes estudios apuntan en este sentido y propugnan la incorporación de programas formativos para los más jóvenes. Sin embargo, probablemente el sector de la población más afectado por la comercialización de productos financieros inadecuados ha sido la población de mayor edad. Por eso, en este trabajo se plantea la cuestión inversa, es decir, si no deberían ser las propias entidades financieras las que deberían procurar distribuir, sobre todo entre los mayores, productos adecuados a su nivel de formación y aversión al riesgo. En este contexto, se analiza en qué medida algunos productos financieros diseñados para gestionar el ahorro (especialmente entre los mayores o de cara a la jubilación) están alcanzando los objetivos deseables. Por último, en este trabajo se lanza una propuesta para que sean los propios ahorradores los que puedan gestionar sus recursos y, de esta forma, evitar, al menos en parte, algunos de los costes de gestión que llevan aparejados aquellos productos y así fomentar la competencia en el sector

Especificaciones alternativas de la estructura temporal de volatilidades

En este trabajo se describe y analiza la estructura temporal de volatilidades instantáneas de los tipos de interés forward correspondientes al mercado español durante el periodo 1999-2002. Este estudio se realiza en el contexto del Modelo de Mercado LIBOR proponiéndose, además, una nueva fórmula para describir la volatilidad instantánea de los tipos de interés forward con parámetros fácilmente interpretables. Junto a este modelo se calibran otros dos alternativos propuestos en la literatura que son utilizados como benchmark para contrastar su validez. Esta contrastación se lleva a cabo a partir de datos del mercado de caps proporcionando resultados satisfactorios para el modelo aquí presentado.In this paper we examine the term structure of instantaneous volatilities of forward rates for the Spanish market covering the period 1999-2002. This analysis is undertaken within the LIBOR Market Model framework. A new model with easily understandable parameters is proposed to describe the behaviour of the instantaneous volatility of forward rates. Two other alternatives are calibrated using data from the cap market and used as benchmarks to test the accuracy of the new [email protected]

Equity, commodity and interest rate volatility derivatives

A new methodology to construct synthetic volatility derivatives is presented. The underlying asset price process is very general, since equity, commodities and interest rates are included. The focus is on volatility swaps and volatility swap options, but much more derivatives may be considered. The proposed methods optimize the conditional value at risk of the non-hedged risk, and yields both bid and ask prices, as well as optimal hedging strategies for both purchases and sales. Upper bounds for the broker capital losses under very negative scenarios are given. Numerical experiments are presented so as to illustrate the performance in practice of this new approach.Research partially supported by “Comunidad Autónoma de Madrid” (Spain, Grant S2009/ESP −1594) and “MEyC” (Spain, Grants ECO2009−14457−C04 and ECO2012−39031−C02−01)

The problem of estimating the volatility of zero coupon bond interest rate

Financial literature and financial industry use often zero coupon yield curves as input for testing hypotheses, pricing assets or managing risk. They assume this provided data as accurate. We analyse implications of the methodology and of the sample selection criteria used to estimate the zero coupon bond yield term structure on the resulting volatility of spot rates with different maturities. We obtain the volatility term structure using historical volatilities and Egarch volatilities. As input for these volatilities we consider our own spot rates estimation from GovPX bond data and three popular interest rates data sets: from the Federal Reserve Board, from the US Department of the Treasury (H15), and from Bloomberg. We find strong evidence that the resulting zero coupon bond yield volatility estimates as well as the correlation coefficients among spot and forward rates depend significantly on the data set. We observe relevant differences in economic terms when volatilities are used to price derivatives

Consumer Confidence and Yield Spreads in Europe

This paper shows the extraordinary capacity of yield spreads to anticipate consumption growth as proxy by the Economic Sentiment Indicator elaborated by the European Commission in order to predict turning points in business cycles. This new evidence complements the well known results regarding the usefulness of the slope of the term structure of interest rates to predict real economic conditions and, in particular, recessions by using a direct measure of expectations. A linear combination of European yield spreads explains a surprising 93.7% of the variability of the Economic Sentiment Indicator. Yield spreads seem to be a key determinant of consumer confidence in Europe.consumer confidence, yield spreads, expected real activity, economic sentiment indicator

A Comparison of Forecasting Mortality Models Using Resampling Methods

[EN] The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies' profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee-Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.The research of David Atance was supported by a grant (Contrato Predoctoral de Formacion Universitario) from the University of Alcala. This work is partially supported by a grant from the MEIyC (Ministerio de Economia, Industria y Competitividad, Spain project ECO2017-89715-P).Atance, D.; Debón Aucejo, AM.; Navarro, E. (2020). A Comparison of Forecasting Mortality Models Using Resampling Methods. Mathematics. 8(9):1-21. https://doi.org/10.3390/math8091550S12189BOOTH, H., MAINDONALD, J., & SMITH, L. (2002). 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North American Actuarial Journal, 13(1), 1-35. doi:10.1080/10920277.2009.10597538Renshaw, A. E., & Haberman, S. (2003). Lee–Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255-272. doi:10.1016/s0167-6687(03)00138-0Renshaw, A. E., & Haberman, S. (2006). A cohort-based extension to the Lee–Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38(3), 556-570. doi:10.1016/j.insmatheco.2005.12.001Hainaut, D. (2018). A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST. ASTIN Bulletin, 48(02), 481-508. doi:10.1017/asb.2017.45Levantesi, S., & Pizzorusso, V. (2019). Application of Machine Learning to Mortality Modeling and Forecasting. Risks, 7(1), 26. doi:10.3390/risks7010026Pascariu, M. D., Lenart, A., & Canudas-Romo, V. (2019). The maximum entropy mortality model: forecasting mortality using statistical moments. 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Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133. doi:10.1111/j.2517-6161.1974.tb00994.xBergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70-83. doi:10.1016/j.csda.2017.11.003Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1). doi:10.1214/aos/1176344552Brouhns, N., Denuit *, M., & Van Keilegom, I. (2005). Bootstrapping the Poisson log-bilinear model for mortality forecasting. Scandinavian Actuarial Journal, 2005(3), 212-224. doi:10.1080/03461230510009754D’Amato, V., Haberman, S., Piscopo, G., & Russolillo, M. (2012). Modelling dependent data for longevity projections. Insurance: Mathematics and Economics, 51(3), 694-701. doi:10.1016/j.insmatheco.2012.09.008Debón, A., Martínez-Ruiz, F., & Montes, F. (2012). Temporal Evolution of Mortality Indicators. North American Actuarial Journal, 16(3), 364-377. doi:10.1080/10920277.2012.10590647Debón, A., Montes, F., Mateu, J., Porcu, E., & Bevilacqua, M. (2008). Modelling residuals dependence in dynamic life tables: A geostatistical approach. Computational Statistics & Data Analysis, 52(6), 3128-3147. doi:10.1016/j.csda.2007.08.006Koissi, M.-C., Shapiro, A. F., & Högnäs, G. (2006). Evaluating and extending the Lee–Carter model for mortality forecasting: Bootstrap confidence interval. Insurance: Mathematics and Economics, 38(1), 1-20. doi:10.1016/j.insmatheco.2005.06.008Liu, X., & Braun, W. J. (2010). Investigating Mortality Uncertainty Using the Block Bootstrap. Journal of Probability and Statistics, 2010, 1-15. doi:10.1155/2010/813583Härdle, W., Horowitz, J., & Kreiss, J. (2003). Bootstrap Methods for Time Series. International Statistical Review, 71(2), 435-459. doi:10.1111/j.1751-5823.2003.tb00485.xBergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213. doi:10.1016/j.ins.2011.12.028Booth, H., Hyndman, R. J., Tickle, L., & de Jong, P. (2006). Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions. Demographic Research, 15, 289-310. doi:10.4054/demres.2006.15.9Delwarde, A., Denuit, M., & Eilers, P. (2007). Smoothing the Lee–Carter and Poisson log-bilinear models for mortality forecasting. Statistical Modelling, 7(1), 29-48. doi:10.1177/1471082x0600700103Debón, A., Montes, F., & Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operational Research, 189(3), 624-637. doi:10.1016/j.ejor.2006.07.050Currie, I. D., Durban, M., & Eilers, P. H. (2004). Smoothing and forecasting mortality rates. Statistical Modelling, 4(4), 279-298. doi:10.1191/1471082x04st080oaChen, K., Liao, J., Shang, X., & Li, J. S.-H. (2009). «A Quantitative Comparison of Stochastic Mortality Models Using Data from England and Wales and the United States,» Andrew J. G. Cairns, David Blake, Kevin Dowd, Guy D. Coughlan, David Epstein, Alen Ong, and Igor Balevich, Vol. 13, No. 1, 2009. North American Actuarial Journal, 13(4), 514-520. doi:10.1080/10920277.2009.10597572Plat, R. (2009). On stochastic mortality modeling. Insurance: Mathematics and Economics, 45(3), 393-404. doi:10.1016/j.insmatheco.2009.08.006Debón, A., Martínez-Ruiz, F., & Montes, F. (2010). A geostatistical approach for dynamic life tables: The effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327-336. doi:10.1016/j.insmatheco.2010.07.007Yang, S. S., Yue, J. C., & Huang, H.-C. (2010). Modeling longevity risks using a principal component approach: A comparison with existing stochastic mortality models. Insurance: Mathematics and Economics, 46(1), 254-270. doi:10.1016/j.insmatheco.2009.09.013Haberman, S., & Renshaw, A. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35-55. doi:10.1016/j.insmatheco.2010.09.003Mitchell, D., Brockett, P., Mendoza-Arriaga, R., & Muthuraman, K. (2013). Modeling and forecasting mortality rates. Insurance: Mathematics and Economics, 52(2), 275-285. doi:10.1016/j.insmatheco.2013.01.002Danesi, I. L., Haberman, S., & Millossovich, P. (2015). Forecasting mortality in subpopulations using Lee–Carter type models: A comparison. Insurance: Mathematics and Economics, 62, 151-161. doi:10.1016/j.insmatheco.2015.03.010Yang, B., Li, J., & Balasooriya, U. (2014). Cohort extensions of the Poisson common factor model for modelling both genders jointly. Scandinavian Actuarial Journal, 2016(2), 93-112. doi:10.1080/03461238.2014.908411Neves, C., Fernandes, C., & Hoeltgebaum, H. (2017). 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Diseño de investigación de análisis de la eficiencia en la central hidroeléctrica Mopa por medición de caudal con método ultrasónico para maximizar la producción energética, Quetzaltenango, Guatemala

Evaluar el análisis de la eficiencia en la central hidroeléctrica Mopa por medición de caudal con método ultrasónico para maximizar la producción energética, así analizar cuáles son los parámetros que influyen en la eficiencia e identificar cuáles son los beneficios de la utilización de un medidor de caudal ultrasónico

Consumer Confidence and Yield Spreads in Europe

This paper shows the extraordinary capacity of yield spreads to anticipate consumption growth as proxy by the Economic Sentiment Indicator elaborated by the European Commission in order to predict turning points in business cycles. This new evidence complements the well known results regarding the usefulness of the slope of the term structure of interest rates to predict real economic conditions and, in particular, recessions by using a direct measure of expectations. A linear combination of European yield spreads explains a surprising 93.7% of the variability of the Economic Sentiment Indicator. Yield spreads seem to be a key determinant of consumer confidence in Europe.Eva Ferreira and Gonzalo Rubio acknowledge the financial support provided by Ministerio de Ciencia y Tecnología grant BEC2001-0636; the latter also thanks the Fundación BBVA research grant 1-BBVA 00044.321-15466/2002. Maria Isabel Martínez and Eliseo Navarro acknowledge the financial support provided by Ministerio de Ciencia y Tecnología grant BEC2001-1599

Zero-coupon interest rates: Evaluating three alternative datasets

The zero-coupon yield curve is a common input for most financial purposes. We consider three popular yield curve datasets and explore the extent to which the decision as to what dataset to use for a particular application may have an impact on the results. Many term structure papers evaluate alternative models for estimating zero coupon bonds based on their ability to replicate bond prices. However, in this paper we take a step forward by analyzing the consequences of using these alternative datasets in estimates of other moments and variables such as interest rate volatilities or the resulting forward rates and their correlations. After finding significant differences, we also explore the existence of volatility spillover effects among these three datasets. Finally, we illustrate the relevance of the choice of one particular dataset by examining the differences that may arise when testing the expectations hypothesis. In the conclusions, we provide guidance to end users in selecting a particular dataset