The yield on the 10-year U.S. Treasury Note is among the most cited interest rates by investors, policymakers, and fnancial institutions. We show that the 10-year Treasury yield's forward-looking volatility, a VIX-style measure that is a proxy for uncertainty about future interest rates, is a...

This paper provides empirical evidence that volatility markets are integrated through the time-varying term structure of variance risk premia. These risk premia predict the returns from selling volatility for different horizons, maturities, and products, including variance swaps, straddles, and...

The slope of the implied volatility term structure is positively related to future option returns. We rank firms based on the slope of the volatility term structure and analyze the returns for straddle portfolios. Straddle portfolios with high slopes of the volatility term structure outperform...

Some countries have announced national benchmark rates while some others have been working on the recent trend in which the London Interbank Offered Rate (LIBOR) will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this...

The main goal of this paper is to better understand the behavior of credit spreads in the past and the potential risk of unexpected future credit spread changes. One important consideration to note regarding credit spreads is the fact that bond spreads contain a liquidity premium, which...

Increasing oil and food prices and persistent supply chain disruptions in 2022 contributed to inflation in advanced economies that had not been seen in decades. This pushed up interest rates, which in turn led to higher yields in global bond markets. This study examines two distinct channels...

Increases in the federal funds rate aimed at stabilizing the economy have inevitably been followed by recessions. Recently, peaks in the federal funds rate have occurred 6-16 months before the start of recessions; reductions in interest rates apparently occurred too late to prevent those...

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...