ALBERT — All Library Books, journals and Electronic Records Telegrafenberg

Hits per page

hits 1 - 3 | 3 hits

Sorting

Electronic Resource

Predictive Evaluation of Econometric Forecasting Models in Commodity Futures Markets (1998)

Zeng, Tian ; Swanson, Norman R.

Cambridge, Mass. : Berkeley Electronic Press (now: De Gruyter)

Studies in nonlinear dynamics and econometrics 2.1998, 4, art6

add to mindlist on the mindlist

Details

ISSN: 1081-1826

Source: Berkeley Electronic Press Academic Journals

Topics: Mathematics , Economics

Notes: The predictive accuracy of various econometric models, including random walks, vector-autoregressive and vector-error-correction models, are investigated using daily futures prices of four commodities (the S&P 500 index, treasury bonds, gold, and crude oil). All models are estimated using a rolling-window approach, and evaluated by both in-sample and out-of-sample performance measures. The criteria considered include system criteria, where we evaluate multiequation forecasting models, and univariate forecast-accuracy criteria. The five univariate criteria are root mean square error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), confusion matrix (CM), and confusion rate (CR). The five system criteria used include the trace of second-moment matrix of the forecast-errors matrix (TMSE), the trace of second-moment matrix of percentage-forecast errors (TMAPE), the generalized forecast-error second-moment matrix (GFESM), and a trading-rule profit criterion (TPC) based on a maximum-spread trading strategy. An in-sample criterion, the mean Schwarz information criteria (MSIC), is also computed. Our results suggest that error-correction models perform better in shorter forecast horizons, when models are compared based on quadratic loss measures and confusion matrices. However, the error-correction models which we consider perform better at all forecast horizons (one to five steps ahead) when models are compared based on a profit-maximization loss function. Further, our error-correction model, where the error-correction term is constructed according to a cost-of-carry equilibrium condition, outperforms our alternative error-correction model, which uses the price spreads as the error-correction term.

Type of Medium: Electronic Resource

URL: http://www.bepress.com/snde/vol2/iss4/art6

Permalink

	Location	Call Number	Expected	Availability

Others were also interested in ...

Paper (German National Licenses)

Fulltext

Unknown

New Evidence of the Marginal Predictive Content of Small and Large Jumps in the Cross-Section (2020)

Yu, Bo ; Mizrach, Bruce ; Swanson, Norman R.

Molecular Diversity Preservation International

In: Econometrics . 2020; 8(2): 19. Published 2020 May 19. doi: 10.3390/econometrics8020019.

add to mindlist on the mindlist

Details

Publication Date: 2020-05-19

Description: We investigate the marginal predictive content of small versus large jump variation, when forecasting one-week-ahead cross-sectional equity returns, building on Bollerslev et al. (2020). We find that sorting on signed small jump variation leads to greater value-weighted return differentials between stocks in our highest- and lowest-quintile portfolios (i.e., high–low spreads) than when either signed total jump or signed large jump variation is sorted on. It is shown that the benefit of signed small jump variation investing is driven by stock selection within an industry, rather than industry bets. Investors prefer stocks with a high probability of having positive jumps, but they also tend to overweight safer industries. Also, consistent with the findings in Scaillet et al. (2018), upside (downside) jump variation negatively (positively) predicts future returns. However, signed (large/small/total) jump variation has stronger predictive power than both upside and downside jump variation. One reason large and small (signed) jump variation have differing marginal predictive contents is that the predictive content of signed large jump variation is negligible when controlling for either signed total jump variation or realized skewness. By contrast, signed small jump variation has unique information for predicting future returns, even when controlling for these variables. By analyzing earnings announcement surprises, we find that large jumps are closely associated with “big” news. However, while such news-related information is embedded in large jump variation, the information is generally short-lived, and dissipates too quickly to provide marginal predictive content for subsequent weekly returns. Finally, we find that small jumps are more likely to be diversified away than large jumps and tend to be more closely associated with idiosyncratic risks. This indicates that small jumps are more likely to be driven by liquidity conditions and trading activity.

Electronic ISSN: 2225-1146

Topics: Economics