Time-Varying Risk Premiums and the Output Gap

CFA Digest ◽  
2010 ◽  
Vol 40 (1) ◽  
pp. 61-62
Author(s):  
C. Mitchell Conover
Keyword(s):  
Output Gap ◽  
Time Varying ◽  
Risk Premiums

Time-Varying Risk Premiums and the Output Gap

2008 ◽  
Vol 22 (7) ◽  
pp. 2801-2833 ◽  
Author(s):  
Ilan Cooper ◽  
Richard Priestley
Keyword(s):  
Output Gap ◽  
Time Varying ◽  
Risk Premiums

Time-Varying Uncertainty of the Federal Reserve's Output Gap Estimate

2021 ◽  
pp. 1-38
Author(s):  
Travis J. Berge
Keyword(s):  
Aggregate Demand ◽  
Stochastic Volatility Model ◽  
Output Gap ◽  
Time Varying ◽  
Common Component ◽  
Macroeconomic Variables ◽  
Macroeconomic Uncertainty ◽  
Volatility Model ◽  
The Common ◽  
Gap Estimate

Abstract A factor stochastic volatility model estimates the common component to output gap estimates produced by the staff of the Federal Reserve, its time-varying volatility, and time-varying, horizon-specific forecast uncertainty. The output gap estimates are uncertain even well after the fact. Nevertheless, the common component is clearly procyclical, and positive innovations to the common component produce movements in macroeconomic variables consistent with an increase in aggregate demand. Heightened macroeconomic uncertainty, as measured by the common component's volatility, leads to persistently negative economic responses.

Tests of Asset Pricing with Time-Varying Expected Risk Premiums and Market Betas

1987 ◽  
Vol 42 (2) ◽  
pp. 201-220 ◽  
Author(s):  
WAYNE E. FERSON ◽  
SHMUEL KANDEL ◽  
ROBERT F. STAMBAUGH
Keyword(s):  
Asset Pricing ◽  
Time Varying ◽  
Risk Premiums

Bond Risk Premiums with Machine Learning

2020 ◽  
Author(s):  
Daniele Bianchi ◽  
Matthias Büchner ◽  
Andrea Tamoni
Keyword(s):  
Machine Learning ◽  
Yield Curve ◽  
Return Predictability ◽  
Time Varying ◽  
Short Term ◽  
Risk Premiums ◽  
Machine Learning Methods ◽  
Yield Information ◽  
Bond Return ◽  
Bond Risk

Abstract We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.

Can a Taylor rule better explain the Fed’s monetary policy through the 1920s and 1930s? A nonlinear cliometric analysis

2018 ◽  
Vol 22 (5) ◽  
Author(s):  
Olivier Damette ◽  
Fredj Jawadi ◽  
Antoine Parent
Keyword(s):  
Monetary Policy ◽  
Business Cycle ◽  
Interwar Period ◽  
Taylor Rule ◽  
Structural Break ◽  
Output Gap ◽  
Time Varying ◽  
Working Paper ◽  
The Great Depression ◽  
Monetary Regimes

Abstract This paper investigates whether a variant of a Taylor rule applied to historical monetary data of the interwar period is useful to gain a better understanding of the Fed’s conduct of monetary policy over the period 1920–1940. To this end, we considered a standard Taylor rule (using two drivers: output gap and inflation gap) and proxied them differently for robustness. Further, we extended this Taylor rule to a nonlinear framework while enabling its coefficient to be time-varying and to change with regard to the phases in business cycle, in order to better capture any further asymmetry in the data and the structural break induced by the Great Depression. Accordingly, we showed two important findings. First, the linearity hypothesis was rejected, and we found that an On/Off Taylor Rule is appropriate to reproduce the conduct of monetary policy during the interwar period more effectively (the activation of drivers only occurs per regime). Second, unlike Field [Field, A. 2015. “The Taylor Rule in the 1920s.” Working Paper], we validated the use of a Taylor rule to explain the conduct of monetary policy in history more effectively. Consequently, this nonlinear Taylor rule specification provides interesting results for a better understanding of monetary regimes during the interwar period, and offers useful complements to narrative monetary history.

Industry Characteristics, Risk Premiums, and Debt Pricing

2016 ◽  
Vol 92 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Dan Amiram ◽  
Alon Kalay ◽  
Gil Sadka
Keyword(s):  
Industry Structure ◽  
Empirical Literature ◽  
Time Varying ◽  
External Shocks ◽  
Expected Loss ◽  
Firm Level ◽  
Risk Premiums ◽  
Industry Level ◽  
Debt Pricing ◽  
Loan Portfolios

ABSTRACT Despite theoretical and anecdotal evidence highlighting the importance of industry-level analyses to lenders, the empirical literature on debt pricing has focused almost exclusively on firm-level forces that affect expected loss. This paper provides empirical evidence that industry-level characteristics relate to debt pricing through risk premiums. We address the empirical challenges that arise when testing these theories by using a proprietary dataset of time-varying and forward-looking measures of industry characteristics. These characteristics include growth, sensitivity to external shocks, and industry structure, all measured at the six-digit NAICS level. Our results show that lenders demand higher spreads to bear industry-level risk. The relation exists within subsamples with constant credit ratings, and strengthens when lenders' loan portfolios are less diversified and during periods when diversification is difficult. Therefore, our results suggest that industry characteristics relate to debt pricing by informing lenders not only about expected loss, but also about risk premiums. JEL Classifications: G31; G32; G33; M21.

scholarly journals Hedge Funds, Arbitrage, and Timing

2020 ◽  
Vol 13 (1) ◽  
pp. 45
Author(s):  
Daniel T. Lawson ◽  
Robert L. Schwartz ◽  
Seth D. Thomas
Keyword(s):  
Hedge Funds ◽  
Small Firms ◽  
Hedge Fund ◽  
Time Varying ◽  
Risk Premiums ◽  
Growth Anomaly ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Asset Growth ◽  
Abnormal Profits

This paper is an extension of the work of Lawson and Schwartz (2018) which analyzes the risk-adjusted performance of hedge funds by employing a collection of four, five, seven, and eight-factor models. The purpose is to evaluate how well the top and bottom performing subset of hedge fund strategies have profited on known asset pricing anomalies during two unique time periods, 1994 to 2000 and 2001 to 2008. The bifurcation of the data into two distinct periods allows for a deeper exploration of the potential time-varying significance of estimated factor arbitrage. Our empirical testing suggests that both the top and bottom performing funds did utilize the asset growth anomaly to generate abnormal profits. Top performers tended to invest with a long emphasis on low asset growth, value firms while the bottom-five performing hedge fund strategies tested positive for a predilection towards going long small firms with low asset growth characteristics. Arguably, these outcomes probably align with the nature of the investment philosophy of each fund strategy. Interestingly, however, the time-varying significance of estimated coefficients for the value and returns momentum factors between the two distinct timeframes suggests either intentional or unintentional rotation between the use of available pricing anomalies and risk premiums.

Sign in / Sign up

Export Citation Format

Share Document