scholarly journals Break Risk

2020 ◽  
Author(s):  
Simon C Smith ◽  
Allan Timmermann
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Prediction Models ◽  
Return Predictability ◽  
Present Value ◽  
New Approach ◽  
Present Value Model ◽  
Out Of Sample ◽  
Rate Processes ◽  
Value Model

Abstract We develop a new approach to modeling and predicting stock returns in the presence of breaks that simultaneously affect a large cross-section of stocks. Exploiting information in the cross-section enables us to detect breaks in return prediction models with little delay and to generate out-of-sample return forecasts that are significantly more accurate than those from existing approaches. To identify the economic sources of breaks, we explore the asset pricing restrictions implied by a present value model which links breaks in return predictability to breaks in the cash flow growth and discount rate processes.

A cross-section test of the present value model

1996 ◽  
Vol 2 (4) ◽  
pp. 295-306 ◽  
Author(s):  
George Bulkley ◽  
Nick Taylor
Keyword(s):  
Cross Section ◽  
Present Value ◽  
Present Value Model ◽  
Value Model

A look into German housing markets: A bubble call?

2018 ◽  
Vol 21 (4) ◽  
pp. 289-301
Author(s):  
Jan R. Kim ◽  
Gieyoung Lim
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Markets ◽  
Present Value ◽  
Present Value Model ◽  
Price Bubbles ◽  
Steep Rise ◽  
Value Model ◽  
German Housing Market

The steep rise in German house prices in recent years raises the question of whether a speculative bubble has already emerged. Using a modified present-value model, we estimate the size of speculative house price bubbles in the German housing market. We do not find evidence for positive bubble accumulation in recent years, and interpret the current bullish run as reflecting the correction of house prices that have been undervalued for more than 10 years. With house prices close to their fair values as of 2018:Q1, our answer to the question is, ‘Not yet, but it is likely soon’.

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.

Realized Skewness and the Return Predictability

2016 ◽  
Vol 24 (1) ◽  
pp. 119-152
Author(s):  
Myounghwa Sim
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Realized Volatility ◽  
Return Predictability ◽  
Higher Moments ◽  
Firm Characteristics ◽  
Realized Variance ◽  
Negative Relation ◽  
Intraday Data ◽  
Skewness And Kurtosis

We explore the cross-section of realized variance, skewness, and kurtosis for stock returns obtained from intraday data. We investigate the properties of the realized higher moments, and more importantly, examine relations between the realized moments and subsequent stock returns. We find evidence of a negative relation between realized skewness and next week’s returns. A strategy buying stocks in the lowest realized skewness quintile and selling stocks in the highest realized skewness quintile earns 0.79 percent per week a risk-adjusted basis. Our results on the realized skewness are robust to controls for various firm characteristics such as size and book-to-market. Little evidence exists that either the realized volatility or the realized kurtosis is significantly related to next week’s returns.

Modeling Stock Prices without Knowing How to Induce Stationarity

1996 ◽  
Vol 12 (4) ◽  
pp. 739-740
Author(s):  
David N. Dejong ◽  
Charles H. Whiteman
Keyword(s):  
Stock Prices ◽  
Present Value ◽  
Posterior Odds ◽  
Knowing How ◽  
Present Value Model ◽  
Two Factors ◽  
Value Model

In “Modeling Stock Prices without Knowing How to Induce Stationarity” (1994, Econometric Theory 10, 701–719), we used posterior-odds calculations to evaluate restrictions imposed by a present-value model of stock prices across the equations of a VAR representation of stock prices and dividends. The results we reported are tainted by the omission of two factors: the Jacobians induced by the mapping of our priors over VAR parameters β into the restricted sample spaces relevant under hypotheses H2-H4 (hence, tainting our calculations of p(Hi|y,X) in (22) for i = 2–4), and an integrating constant needed in calculating the unrestricted probability p(Hi|y,X) in (22). Table 1 reports our revised calculations, which differ substantively from those reported previously.

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