Out-of-sample Predictions of Bond Excess Returns and Forward Rates: An Asset-Allocation Perspective

2010 ◽  
Author(s):  
Daniel L. Thornton ◽  
Giorgio Valente
Keyword(s):  
Asset Allocation ◽  
Excess Returns ◽  
Forward Rates ◽  
Out Of Sample

scholarly journals Mind the (Convergence) Gap: Bond Predictability Strikes Back!

2021 ◽  
Author(s):  
Andrea Berardi ◽  
Michael Markovich ◽  
Alberto Plazzi ◽  
Andrea Tamoni
Keyword(s):  
Asset Allocation ◽  
Predictive Ability ◽  
The United States ◽  
Excess Returns ◽  
Natural Rate ◽  
Forward Rates ◽  
Bond Risk Premia ◽  
Out Of Sample ◽  
Natural Rate Of Interest ◽  
The Difference

We show that the difference between the natural rate of interest and the current level of monetary policy stance, which we label Convergence Gap (CG), contains information that is valuable for bond predictability. Adding CG in forecasting regressions of bond excess returns significantly raises the R2, and restores countercyclical variation in bond risk premia that is otherwise missed by forward rates. Consistent with the argument that CG captures the effect of real imbalances on the path of rates, our factor has predictive ability for real bond excess returns. The importance of the gap remains robust out-of-sample and in countries other than the United States. Furthermore, its inclusion brings significant economic gains in the context of dynamic conditional asset allocation. This paper was accepted by Gustavo Manso, finance.

Modeling the Cross Section of Stock Returns: A Model Pooling Approach

2012 ◽  
Vol 47 (6) ◽  
pp. 1331-1360 ◽  
Author(s):  
Michael O’Doherty ◽  
N. E. Savin ◽  
Ashish Tiwari
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Asset Allocation ◽  
Economic Stress ◽  
Predictive Score ◽  
Asset Pricing Model ◽  
Out Of Sample ◽  
Competing Models ◽  
The Cross ◽  
Allocation Decisions

AbstractModel selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.

scholarly journals Long-Term Strategic Asset Allocation: An Out-of-Sample Evaluation

2015 ◽  
Vol 61 (9) ◽  
pp. 2185-2202 ◽  
Author(s):  
Bart Diris ◽  
Franz Palm ◽  
Peter Schotman
Keyword(s):  
Asset Allocation ◽  
Strategic Asset Allocation ◽  
Out Of Sample

scholarly journals Optimal Option Portfolio Strategies: Deepening the Puzzle of Index Option Mispricing

2017 ◽  
Vol 52 (1) ◽  
pp. 277-303 ◽  
Author(s):  
José Afonso Faias ◽  
Pedro Santa-Clara
Keyword(s):  
Asset Allocation ◽  
Risk Premia ◽  
Volatility Risk ◽  
Short Sample ◽  
Option Returns ◽  
Portfolio Strategies ◽  
Portfolio Strategy ◽  
Out Of Sample ◽  
Index Option ◽  
Mean Variance

Traditional methods of asset allocation (such as mean–variance optimization) are not adequate for option portfolios because the distribution of returns is non-normal and the short sample of option returns available makes it difficult to estimate their distribution. We propose a method to optimize a portfolio of European options, held to maturity, with a myopic objective function that overcomes these limitations. In an out-of-sample exercise incorporating realistic transaction costs, the portfolio strategy delivers a Sharpe ratio of 0.82 with positive skewness. This performance is mostly obtained by exploiting mispricing between options and not by loading on jump or volatility risk premia.

Humans, Econs and Portfolio Choice

2016 ◽  
Vol 07 (02) ◽  
pp. 1750001 ◽  
Author(s):  
Michael J. Best ◽  
Robert R. Grauer
Keyword(s):  
Prospect Theory ◽  
Portfolio Choice ◽  
Loss Aversion ◽  
Asset Allocation ◽  
Power Utility ◽  
Numerical Example ◽  
Portfolio Choices ◽  
Out Of Sample ◽  
Finance Theory ◽  
Mean Variance

We compare the portfolio choices of Humans — prospect theory investors — to the portfolio choices of Econs — power utility and mean-variance (MV) investors. In a numerical example, prospect theory portfolios are decidedly unreasonable. In an in-sample asset allocation setting, the prospect theory results are consistent with myopic loss aversion. However, the portfolios are extremely unstable. The power utility and MV results are consistent with traditional finance theory, where the portfolios are stable across decision horizons. In an out-of-sample asset allocation setting, the power utility and portfolios outperform the prospect theory portfolios. Nonetheless the prospect theory portfolios with loss aversion coefficients of 2.25 and 2 perform well.

Strategic Asset Allocation: The Role of Corporate Bond Indices?

2011 ◽  
Vol 01 (02) ◽  
pp. 355-422 ◽  
Author(s):  
Antonios Sangvinatsos
Keyword(s):  
Asset Allocation ◽  
Corporate Bond ◽  
High Yield ◽  
Portfolio Performance ◽  
Lower Grade ◽  
Out Of Sample ◽  
Bond Indices ◽  
The Business Cycle ◽  
Liquidity Premia ◽  
Investment Grade

This paper studies dynamic asset allocations across stocks, Treasury bonds, and corporate bond indices. We employ a new model where liquidity plays an important role in forecasting excess returns. We document the significant utility benefits an investor gains by optimally including corporate bond indices in his portfolio. The benefits are bigger for lower-grade bonds. We also find that investment-grade indices are different from high-yield indices in that different risks are priced in these two asset classes. One important difference is that there exist positive "flight-to-liquidity" premia in investment-grade bonds, but we find no such premia in high-yield bonds. We calculate the portfolio behavior and the utility benefits for three types of investors, the "sophisticated", the "average" and the "lazy" investor. We provide practical portfolio advice on investing throughout the business cycle and we study how the total allocations and hedging demands vary with the business conditions. In addition, utilizing our model, we evaluate the significance of the liquidity variable information for the investor. We find that the liquidity information greatly enhances the investor's portfolio performance. Finally, further support in the optimality of the strategies is provided by calculating their in- and out-of-sample realized returns for the last decade.

Australian Bond Excess Returns: An Asset Allocation Perspective

2017 ◽  
Vol 56 (2) ◽  
pp. 163-173
Author(s):  
Rui Chen ◽  
Meng Wang ◽  
Jiri Svec
Keyword(s):  
Asset Allocation ◽  
Excess Returns
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