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Risk Premia Dynamics in Treasury Bond Markets

Paper Session

Sunday, Jan. 5, 2020 1:00 PM - 3:00 PM (PDT)

Manchester Grand Hyatt, Harbor A
Hosted By: American Finance Association
  • Chair: Anh Le, Pennsylvania State University

Unspanned Risks, Negative Local Time Risk Premiums, and Empirical Consistency of Models of Interest-Rate Claims

Gurdip Bakshi
,
Temple University
John Crosby
,
University of Maryland
Xiaohui Gao Bakshi
,
Temple University

Abstract

We formalize the notion of local time risk premium in the context of a theory in which the pricing kernel is a general diffusion process with spanned and unspanned components. We derive results on the expected excess return of options on bond futures. These results are organized around our new empirical finding that the average returns of out-of-the-money puts and calls, on Treasury bond futures, are negative. Our theoretical reconciliation warrants a negative local time risk premium, and our treatment considers models with market incompleteness and sources of volatility uncertainty.

Informed Trading in Government Bond Markets

Robert Czech
,
Bank of England
Shiyang Huang
,
University of Hong Kong
Dong Lou
,
London School of Economics
Tianyu Wang
,
Tsinghua University

Abstract

We show that both hedge funds and mutual funds contribute to the price discovery in government bond markets using comprehensive administrative data from the UK. Our sample covers virtually all secondary market trades in gilts and contains detailed information on each individual transaction, including the identities of both counterparties. Hedge funds’ daily trading positively forecasts gilt returns in the following one to five days, which is fully reversed in the following month. Part of this short-term return predictive pattern is due to hedge funds’ ability to forecast other investors’ future order flows. Mutual funds’ trading also positively forecasts bond returns, but it operates at a longer horizon—over the next one to two months; this return pattern does not revert in the following year. Additional analyses reveal that mutual funds’ superior performance is partly due to their ability to forecast future changes in short-term rates

Bond Risk Premia with Machine Learning

Daniele Bianchi
,
University of Warwick
Matthias Buchner
,
University of Warwick
Andrea Tamoni
,
Rutgers University

Abstract

We propose, compare, and evaluate a variety of machine learning methods for bond return predictability in the context of regression-based forecasting and contribute to a growing literature that aims to understand the usefulness of machine learning in empirical asset pricing. The main results show that non-linear methods can be highly useful for the out-of-sample prediction of bond excess returns compared to benchmarking data compression techniques such as linear principal component regressions. Also, the empirical evidence show that macroeconomic information has substantial incremental out-of-sample forecasting power for bond excess returns across maturities, especially when complex non-linear features are introduced via ensembled deep neural networks.

Macro Risks and the Term Structure of Interest Rates

Geert Bekaert
,
Columbia University
Eric Engstrom
,
Federal Reserve Board
Andrey Ermolov
,
Fordham University

Abstract

We use non-Gaussian features in U.S. macroeconomic data to identify aggregate supply and demand shocks while imposing minimal economic assumptions. Macro risks represent the variables that govern the time-varying variance, skewness and higher-order moments of these two shocks, with “good” (“bad”) variance associated with positive (negative) skewness. We document that macro risks significantly contribute to the variation of yields and risk premiums for nominal bonds. While overall bond risk premiums are counter-cyclical, an increase in aggregate demand variance significantly lowers risk premiums. Macro risks also significantly predict future realized bond return variances.
Discussant(s)
Scott Joslin
,
University of Southern California
Giang Nguyen
,
Pennsylvania State University
Jingzhi Huang
,
Pennsylvania State University
Greg Duffee
,
Johns Hopkins University
JEL Classifications
  • G1 - General Financial Markets