Determining a fair price and an appropriate timescale to trade municipal debt is a complex decision. This research uses data informatics to explore transaction characteristics and trading activity of investment grade US municipal bonds. Using the relatively recent data stream distributed by the Municipal Securities Rulemaking Board, we provide an institutional summary of market participants and their trading behavior. Subsequently, we focus on a sample of AAA bonds to derive a new methodology to estimate a trade-weighted benchmark municipal yield curve. The methodology integrates the study of ridge regression, artificial neural networks, and support vector regression. We find an enhanced radial basis function artificial neural network outperforms alternate methods used to estimate municipal term structure. This result forms the foundation for establishing a decision theory on optimal municipal bond trading. Using multivariate modeling of a liquidity domain measured across three dependent variables, we investigate the proposed decision theory by estimating weekly production-theoretic bond liquidity returns to scale. Across the three liquidity measures and for almost all weeks investigated, bond trading liquidity is elastic with respect to the modeled factors. This finding leads us to conclude that an optimal trading policy for municipal debt can be implemented on a weekly timescale using the elasticity estimates of bond price, trade size, risk, days-to-maturity, and the macroeconomic influences of labor in the workforce and building activity.
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Creal and Wu (2014) provide an alternative research strain based on affine models to demonstrate how spanned stochastic volatility captures either the cross section of yields or the fitted volatility.
For a complete listing, see: http://bit.ly/2eZvyY5.
All term structure curves are available at: http://bit.ly/2eZvyY5.
B-spline or basis spline is a spline function that has minimal support with respect to a given degree, smoothness, and domain partition (Wikipedia). It is provided here only as a base comparative method.
Aguilar LA (2015) Statement on making the municipal securities market more transparent, liquid, and fair US securities and exchange commission, http://bit.ly/2uOsamN
Almeida C, Ardison K, Kubudi D, Simonsen A, Vicente J (2017) Forecasting bond yields with segmented term structure models. J Financ Econ 16(1):1–33
Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5:31–56
Ang A, Piazzesi M (2003) A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. J Mon Econ 50:745–787
Annaert J, Claes AGP, De Ceuster MJK, Zhang H (2010) Estimating the yield curve using the nelson-siegel model. European Financial Management Association, Aarhus
Bao J, Pan J, Wang J (2011) The illiquidity of corporate bonds. J Financ 66:911–946
Bowsher CG, Meeks R (2013) Stationarity and the term structure of interest rates: a characterization of stationary and unit root yield curves
Cobb CW, Douglas PH (1928) A theory of production. Am Econ Rev 18:139–165
Cox JC, Ingersoll JE, Ross SA (1985) A theory of the term structure of interest rates. Econometrica 53:385–408
Creal DD, Wu JC (2015) Estimation of affine term structure models with spanned or unspanned stochastic volatility. J Econ 85(1):60–81
Dai Q, Singleton KJ (2000) Specification analysis of affine term structure models. J Financ 55:1943–1978
Dai Q, Singleton KJ, Yang W (2007) Regime shifts in a dynamic term structure model of U.S treasury bond yields. Rev Financ Stud Soc Financ Stud 20:1669–1706
Daniels K, Ejara DD (2009) Impact of information asymmetry on municipal bond yields: an empirical analysis. Am J Econ Bus Admin 1:11–20
Dash GH Jr, Kajiji N (2003) New evidence on the predictability of South African Fx volatility in heterogenous bilateral markets. Afr Financ J 5:1–15
Dash GH Jr, Kajiji N (2008) Engineering a generalized neural network mapping of volatility spillovers in european government bond markets. In: Zopounidis C, Doumpos M, Pardalos PM (eds) Handbook of financial engineering, optimization and its applications, vol 18. Springer, Berlin
Dash Jr. GH, Kajiji N Modeling FX (2002) Volatility: a comparative analysis of the rbf neural network topology. In: 9th international conference on forecasting financial markets, London, England
Dash Jr. GH, Hanumara C, Kajiji N (2003) Neural network architectures for modeling fx futures options volatility. In: Annual Meetings of the Northeast Decision Sciences Institute, Providence, Rhode Island
De Pooter M (2007) Examining the Nelson-Siegel class of term structure models. Tinbergen Institute, Amsterdam
De Pooter M, Ravazzolo F, van Dijk D (2010) Term structure forecasting using macro factors and forecast combination. Board of governors of the federal reserve system, Discussion paper 993
Diebold FX, Li C (2006) Forecasting the term structure of government bond yields. J Econ 130:337–364
Diebold FX, Rudebusch GD, Boragan AS (2006) The macroeconomy and the yield curve: a dynamic latent factor approach. J Econ 131:309–338
Drucker H, Burges CJ, Kaufam L, Smola A, Vapnik V (1996) Support vector regression machines. In: NIPS’96 proceedings of the 9th international conference on neural information processing systems, Denver, CO, 1996. MIT Press, Cambridge, MA
Edwards AK, Harris LE, Piwowar MS (2007) Corporate bond market transaction costs and transparency. J Financ 62:1421–1451
Fabozzi FJ, Martellini L, Priaulet P (2005) Predictability in the Shape of the Term Structure of Interest Rates. J Fixed Income 15:40–53
Feldhütter P (2012) The same bond at different prices: identifying search frictions and selling pressures. Rev Financ Stud 25:1155–1206
Geldera LV, Dasb P, Janssena H, Roelsa S (2014) Comparative study of metamodelling techniques in building energy simulation: guidelines for practitioners. Simul Model Pract Theory 49:245–257
Goldstein M, Hotchkiss ES (2015) Dealer behavior in highly illiquid risky assets. Queens University, Belfast
Gonzalez-Rozada M, Sola M, Hevia C, Spagnolo F (2012) Estimating and forecasting the yield curve using a markov switching dynamic nelson and siegel model. Universidad Torcuato Di Tella, Buenos Aires
Harris LE, Piwowar MS (2006) Secondary trading costs in the municipal bond market. J Financ 61:1361–1397
Iglewicz B, Hoaglin D (1993) How to detect and handle outliers. In: Mykytka EF (ed) The ASQC basic reference in quality control: statistical techniques, vol 16. American Society for Quality Control Statistics Division, Milwaukee
Juillard M, Villemot S (2011) Multi-country real business cycle models: accuracy tests and test bench. J Econ Dyn Control 35:178–185
Kajiji N (2001) Adaptation of alternative closed form regularization parameters with prior information to the radial basis function neural network for high frequency financial time series. University of Rhode Island
Kajiji N, Dash GH Jr (2013) On the behavioral specification and multivariate neural network estimation of cognitive scale economies. J Appl Operat Res 5:30–40
Kalotay AJ, Dorigan MP (2009) What makes the municipal yield curve rise? J Fixed Income 18(3):65
Lin H, Liu S, Wang J, Wu C (2010) Liquidity and the pricing of municipal bonds. In: Proceedings of the 2010 China international conference in finance, Beijing, China
Mizrach B (2015) Analysis of corporate bond liquidity. FINRA Office of the Chief Economist. http://bit.ly/2wbO9Dw
Moench E (2008) Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented var approach. J Econ 146:26–43
Moraux F, Pakulyak O (2016) Which term structure of interest rates model performs the best to price the government bonds in euro area? Paper presented at the forecasting financial markets, Hannover, Germany
Nelson CR, Siegel A (1987) Parismoious modeling of yield curves. J Bus 60:473–489
Olej V, Hájek P (2009) Municipal creditworthiness modelling by radial basis function neural networks and sensitive analysis of their input parameters. In: Alippi C, Polycarpou M, Panayiotou C, Ellinas G (eds) Artificial neural networks—ICANN 2009. Lecture notes in computer science. Springer, Berlin, pp 505–514
Piazzesi M (2010) Affine term structure models. In: Ait-Sahalia Y, Hansen L (eds) Handbook of financial econometrics: tools and techniques, vol 1. Handbooks in Finance, Elesvier
Ratrout NT, Gazder U (2014) Factors affecting performance of parametric and non-parametric models for daily traffic forecasting. In: 5th international conference on ambient systems, networks and technologies, Procedia Computer Science 32:285-292
Steenbarger BN (2003) The psychology of trading: tools and techniques for minding the markets. Wiley, Hoboken
Vapnik V (1998) Statistical learning theory. Wiley, New York
Vasicek O (1977) An equilibrium characterization of the term structure. J Financ Econ 5:177–188
Xiang J, Zhu X (2013) A regime-switching Nelson-Siegel term structure model and interest rate forecasts. J Financ Econ 11:522–555
Submitted for publication consideration in the Feature Issue on “Financial Decision Support”, Euro Journal on Decision Processes, 30 July 2017.
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Dash, G.H., Kajiji, N. & Vonella, D. The role of supervised learning in the decision process to fair trade US municipal debt. EURO J Decis Process 6, 139–168 (2018). https://doi.org/10.1007/s40070-018-0079-2
- Decision theory
- Municipal bonds
- Supervised learning
- Production theory