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Much ado about nothing : a study of differential pricing and liquidity of short and long term bonds
(2018)
Are yields of long-maturity bonds distorted by demand pressure of clientele investors, regulatory effects, or default, flight-to-safety or liquidity premiums? Using data on German nominal bonds between 2005 and 2015, we study the differential pricing and liquidity of short and long maturity bonds. We find statistically significant, but economically negligible segmentation in yields and some degree of liquidity segmentation of short-term versus long-term bonds. These results have important policy implications for the e17.5 trillion European pension and insurance industries: long maturity bond yields seem appropriate for the valuation of long-term liabilities.
Fleckenstein et al. (2014) document that nominal Treasuries trade at higher prices than inflation-swapped indexed bonds, which exactly replicate the nominal cash flows. We study whether this mispricing arises from liquidity premiums in inflation-indexed bonds (TIPS) and inflation swaps. Using US data, we show that the level of liquidity affects TIPS, whereas swap yields include a liquidity risk premium. We also allow for liquidity effects in nominal bonds. These results are based on a model with a systematic liquidity risk factor and asset-specific liquidity characteristics. We show that these liquidity (risk) premiums explain a substantial part of the TIPS underpricing.
In the last decade, central bank interventions, flights to safety, and the shift in derivatives clearing resulted in exceptionally high demand for high quality liquid assets, such as German treasuries, in the securities lending market besides the traditional repo market activities. Despite the high demand, the realizable securities lending income has remained economically negligible for most beneficial owners. We provide empirical evidence of pricing inefficiencies in the non-transparent, oligopolistic securities lending market for German treasuries from 2006 to 2015. Consistent with Duffie, Gârleanu and Pedersen (2005)’s theory, we find that the less connected market participants’ interests are underrepresented, evident in the longer maturity segment, where lenders are more likely to be conservative passive investors, such as pension funds and insurance firms. The low price elasticity in this segment hinders these beneficial owners to fully capitalize on the additional income from securities lending, giving rise to important negative welfare implications.
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is una↵ected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is unaffected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.