The updated funding liquidity and term structure factors based on the Bond Liquidity Premia paper are available.

The factors can be found here: FundingLiquiditFactor_19862017Q1.

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# Jean-Sébastien Fontaine

## Updated Funding Liquidity Factor: 2017Q1

## What do Fed Funds Futures tell us about Monetary Policy Uncertainty

## Recent Advances in Old Fixed-Income Topics: Liquidity, Learning, and the Lower Bound

## Funding Risk, Market Liquidity, Market Volatility and the Cross-Section of Asset Returns

## Bond Risk Premia and Gaussian Term Structure Models

## Estimating the Policy Rule from Money Market Rates when Target Rate Changes Are Lumpy

## Competition and Strategic Control of a Central Counterparty: When Lower Risk Increases Profit

## Non-Markov Gaussian Term Structure Models: The Case of Inflation

## Discrete Choice Term Structure Models : Theory and Applications

## The Equity Premium, Variance Premium and the Maturity Structure of Uncertainty

## The Equity Premium and The Volatility Spread : The Role of Risk-Neutral Skewness

## Bond Liquidity Premia

Economics, Finance and Econometrics

The updated funding liquidity and term structure factors based on the Bond Liquidity Premia paper are available.

The factors can be found here: FundingLiquiditFactor_19862017Q1.

The paper is here. I find that the uncertainty around future changes to the Federal Reserve target rate varies over time. In our results, the main driver of uncertainty is a “path” factor signaling information about future policy actions, which is filtered from federal funds futures data. The uncertainty is lowest when the path factor signals a loosening cycle and highest when it signals a loosening cycle. The uncertainty raises the risk premium in a loosening cycle, reducing the transmission of target changes to longer maturities. Our results trace the information content of federal funds futures to hedging demand.

in Handbook of Fixed-Income Securities, 2016.

In this chapter, we discuss old topics that have returned to the forefront in fixed-income research: liquidity, learning and the bound underlying nominal yields. Progress has been been rapid in these fields in recent years.

We find strong evidence of a funding risk premium in the cross-section of asset returns. Using funding shocks identified in our previous paper, we obtain estimates for the price of funding risk that are robust across Treasury bonds, corporate bonds, equities, and hedge funds. Funding shocks pose a risk to investors because they exacerbate the illiquidity and volatility of securities, increase the dispersions of asset illiquidity and volatility, and decrease contemporaneous returns. Our price-of-risk estimates are also robust to using mimicking portfolio returns, alternative portfolio sorts, traditional test assets, monthly returns or quarterly returns. Funding shocks are not subsumed by common proxies for market-wide illiquidity or dealers’ balance sheet risk.

The paper can be found here. The funding liquidity factor used in this paper can be found here: FundingLiquiditFactor_19862015Q4.

Management Science, 2016.

You can find the paper here.

We extend results in Dai and Singleton (2002). Cochrane and Piazzesi (2005) show that (i) lagged forward rates improve the predictability of annual bond returns, adding to current forward rates, and that (ii) a Markovian model for monthly forward rates cannot generate the pattern of predictability in annual returns. These results stand as a challenge to modern Markovian dynamic term structure models (DTSMs). We develop the family of Conditional Mean DTSMs where the yield dynamics depend on current yields and their history. We match these bond risk premium stylized facts. A small Markovian factor “hidden” in measurement error (Duffee, 2011) helps but it is not sufficient to match the evidence.

in Developments in Macro-Finance Yield Curve Modelling, 2014.

Most central banks effect changes to their target or policy rate in discrete increments (e.g., multiples of 0.25%) following public announcements on scheduled dates. Still, it is common to rely on the assumption that the policy rate changes continuously with economic conditions. Most applications also do not distinguish between dates with and without scheduled announcements. This assumption is not innocuous when estimating the policy rule based on daily frequency. I find that accounting for discrete changes leads to economically different estimates of an otherwise standard term structure model.

Working paper version available here.

*Updated June 2013.*

A recent paper with Hector Perez and Joshua Slive, also from the Bank of Canada. We study how CCP membership rules and risk controls affect the competitive structure of an OTC market. These constraints reduce systemic risk (i.e., CCP default risk) but they also increase the market power of its members. We find that risk controls can serve as an anticompetitive instrument, allowing members to coordinate around a more profitable outcome at the expense of non-members. You can find the paper here : Competition and Strategic Control of a Central Counterparty

*Review of Finance, 2014.*

With Bruno Feunou. Previous versions circulated as *Forecasting Inflation and the Inflation Risk Premium.*

Standard Gaussian macro-finance term structure models impose that the conditional mean is a function of the risk factors. We relax this assumption: yields are linear in the conditional mean (but not in the risk factors). To illustrate, if inflation is one of the factors, then yields should span expected inflation but not inflation. Second, expected and surprise yield changes can have opposite contemporaneous effects on expected inflation. Third, the inflation survey forecasts and the inflation rate can be used consistently within the state equation. These three features are inconsistent with the Markov assumption. Our results hold for the US and for Canada. The paper is here.

With Bruno Feunou. Central banks around the world restrict changes in their policy rate to a small set of possibilities. Typically, these are a few multiples of 0.25% away from the current policy rate. In economics, this is an example of the well-known class of ordered choice problems. This paper bridges the gap between discrete-choice problems and asset pricing. We generalize the standard probit-logit case and provide the implications from the absence of arbitrage to develop a wide class of term structure models. Empirically, we find significant non-linearitites in the Fed’s policy function and in US interest rates. The paper is here, Discrete Choice Term Structure Models, and the abstract follows.

Continue reading “Discrete Choice Term Structure Models : Theory and Applications”

*Review of Finance 18 (1), 2014*

With Bruno Feunou, Abderrahim Taamouti and Roméo Tédongap. The paper is here: The Equity Premium and the Maturity Structure of Uncertainty. The trade-offs between risk and returns vary with the investment horizons. The term structure of moments, measurable from option prices, can reveal the risk-returns trade-off associated with Long-Run Risk or Stochastic Volatility factors. Intuitively, we can study the dynamics of hard-to-measure risk factors via their impacts on the term structure of variance and other moments. As predicted by theory, we extract a small number of risk factors with substantial predictive power for the Equity Premium and the Variance Premium. These summarize the compensation for risks implicit in the Equity Premium and the Variance Premium at different horizons.

*Review of Derivatives Research, 2016*.

The Homoscedastic Gamma [HG] model for the distribution of returns is characterized by its mean, variance and an independent skewness parameter. We check the model’s implications for option pricing. The information content of skewness leads to improved in-sample, out-of-sample pricing and hedging performances. Our results imply that expanding around the Gaussian density is restrictive and does not offer sufficient flexibility to match the skewness and kurtosis implicit in option data. The HG model predicts that the spread between historical and risk-neutral volatilities is a function of the risk premium and of skewness. We measure skewness from option prices and test these predictions. We find that conditioning on skewness increases the predictive power of the volatility spread and that coefficient estimates accord with theory.

*Review of Financial Studies, 2012. *

*Funding Risk Factor updated to March 2017*

With René Garcia at the EDHEC Business School. Our main contribution is to show that the value of funding liquidity is an aggregate risk factor driving a substantial share of risk premia across fixed-income markets. The paper can be found here : Bond Liquidity Premia, here is the online appendix and the abstract follows.

Here is the factors used in the published version of the paper: FundingLiquidityFactor19862009. The updated funding liquidity and term structure factors can be found here: FundingLiquiditFactor_19862017Q1. These factors are obtained using the model in the paper above, but re-estimated with recent data. The funding liquidity factor is standardized to a mean of zero and standard deviation of one. Strikingly, using parameters estimated using recent data yield very similar results to that usin data until to the end of 2007 (as in the paper). This Figure –*CompareFundingLiquidityFactors*– compares the series obtained with data until 2007, 2009 and 2012. The model continue to perform exceptionally well out-of-sample.