At last week’s Municipal Finance Conference in Boston, I made a brief presentation in response to a paper1 that described a new approach to deriving yield curves from municipal bond trade data. The authors have applied neural network techniques to the problem of finding underlying term structures for different segments of the municipal market.
I wanted to encourage researchers into the muni market to always be mindful of its unique quirks and complexity. To make my point, I shared this chart2 with the audience:
I explained that the chart represented the average yield spreads3 for recent trades of certain bonds. The bonds have the same issuer and the same remaining maturity of approximately twelve years.
The bonds traded on only a few days last month. Notice the extreme swings in the reported average yield spreads. One day the average yield is 26 basis points (0.26%) over a relevant index, and a few days later it almost 60 basis points below the index. The average yield continued to bounce around violently for the rest of July.
I asked the audience what might explain this erratic pricing. What would you say?
1Integrating Big Data, Neuroeconomics, and Learning Networks to Model the US Municipal Bond Term Structure by Gordon Dash, Nina Kajiji and Domenic Vonella.
2The chart I presented at the conference included some details that are omitted here.
3On all days when trades occurred, these are the simple averages, for all trades, of the spread of yield-to-worst to a 12-year municipal bond index. Yield-to-worst is the lowest of the yields, corresponding to the traded price, for the various scenarios in which the bond either runs to maturity or is called on any of its call dates.
Revised August 9, 2014 with changes to penultimate paragraph.