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Out-of-sample Tests (out-of-sample + test)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	62%

Selected Abstracts

European Momentum Strategies, Information Diffusion, and Investor Conservatism

EUROPEAN FINANCIAL MANAGEMENT, Issue 3 2005
John A. Doukas
G1; G11; G14 Abstract In this paper we conduct an out-of-sample test of two behavioural theories that have been proposed to explain momentum in stock returns. We test the gradual-information-diffusion model of Hong and Stein (1999) and the investor conservatism bias model of Barberis et al. (1998) in a sample of 13 European stock markets during the period 1988 to 2001. These two models predict that momentum comes from the (i) gradual dissemination of firm-specific information and (ii) investors' failure to update their beliefs sufficiently when they observe new public information. The findings of this study are consistent with the predictions of the behavioural models of Hong and Stein's (1999) and Barberis et al. (1998). The evidence shows that momentum is the result of the gradual diffusion of private information and investors' psychological conservatism reflected on the systematic errors they make in forming earnings expectations by not updating them adequately relative to their prior beliefs and by undervaluing the statistical weight of new information. [source]

The pricing of electricity futures: Evidence from the European energy exchange

THE JOURNAL OF FUTURES MARKETS, Issue 4 2007
Sascha Wilkens
This study investigates the pricing of electricity futures at the European Energy Exchange (EEX) over the period 2002 through 2004. To calculate theoretical contract values, the reduced-form models of J. J. Lucia and E. S. Schwartz (2002) are used, and a thorough empirical analysis by means of an out-of-sample test is conducted for both one- and two-factor models, incorporating a constant non-zero price of risk. Although the models are proven to capture all basic spot market characteristics and provide an accurate in-the-sample fit to observed futures prices, the forecasting performance is subject to biases. For instance, it was found that the relative mispricing depends on both the spot price level and the remaining time-to-maturity of the futures contracts. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:387,410, 2007 [source]

Forecasting and Finite Sample Performance of Short Rate Models: International Evidence,

INTERNATIONAL REVIEW OF FINANCE, Issue 3-4 2005
SIRIMON TREEPONGKARUNA
ABSTRACT This paper evaluates the forecasting and finite sample performance of short-term interest rate models in a number of countries. Specifically, we run a series of in-sample and out-of-sample tests for both the conditional mean and volatility of one-factor short rate models, and compare the results to the random walk model. Overall, we find that the out-of-sample forecasting performance of one-factor short rate models is poor, stemming from the inability of the models to accommodate jumps and discontinuities in the time series data. In addition, we perform a series of Monte Carlo analyses similar to Chapman and Pearson to document the finite sample performance of the short rate models when ,3 is not restricted to be equal to one. Our results indicate the potential dangers of over-parameterization and highlight the limitations of short-term interest rate models. [source]

Prediction of protein folding rates from primary sequences using hybrid sequence representation

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 5 2009
Yingfu Jiang
Abstract The ability to predict protein folding rates constitutes an important step in understanding the overall folding mechanisms. Although many of the prediction methods are structure based, successful predictions can also be obtained from the sequence. We developed a novel method called prediction of protein folding rates (PPFR), for the prediction of protein folding rates from protein sequences. PPFR implements a linear regression model for each of the mainstream folding dynamics including two-, multi-, and mixed-state proteins. The proposed method provides predictions characterized by strong correlations with the experimental folding rates, which equal 0.87 for the two- and multistate proteins and 0.82 for the mixed-state proteins, when evaluated with out-of-sample jackknife test. Based on in-sample and out-of-sample tests, the PPFR's predictions are shown to be better than most of other sequence only and structure-based predictors and complementary to the predictions of the most recent sequence-based QRSM method. We show that simultaneous incorporation of several characteristics, including the sequence, physiochemical properties of residues, and predicted secondary structure provides improved quality. This hybridized prediction model was analyzed to reveal the complementary factors that can be used in tandem to predict folding rates. We show that bigger proteins require more time for folding, higher helical and coil content and the presence of Phe, Asn, and Gln may accelerate the folding process, the inclusion of Ile, Val, Thr, and Ser may slow down the folding process, and for the two-state proteins increased ,-strand content may decelerate the folding process. Finally, PPFR provides strong correlation when predicting sequences with low similarity. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009 [source]

Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 1 2009
Ke Chen
Abstract A computational model, IMP-TYPE, is proposed for the classification of five types of integral membrane proteins from protein sequence. The proposed model aims not only at providing accurate predictions but most importantly it incorporates interesting and transparent biological patterns. When contrasted with the best-performing existing models, IMP-TYPE reduces the error rates of these methods by 19 and 34% for two out-of-sample tests performed on benchmark datasets. Our empirical evaluations also show that the proposed method provides even bigger improvements, i.e., 29 and 45% error rate reductions, when predictions are performed for sequences that share low (40%) identity with sequences from the training dataset. We also show that IMP-TYPE can be used in a standalone mode, i.e., it duplicates significant majority of correct predictions provided by other leading methods, while providing additional correct predictions which are incorrectly classified by the other methods. Our method computes predictions using a Support Vector Machine classifier that takes feature-based encoded sequence as its input. The input feature set includes hydrophobic AA pairs, which were selected by utilizing a consensus of three feature selection algorithms. The hydrophobic residues that build up the AA pairs used by our method are shown to be associated with the formation of transmembrane helices in a few recent studies concerning integral membrane proteins. Our study also indicates that Met and Phe display a certain degree of hydrophobicity, which may be more crucial than their polarity or aromaticity when they occur in the transmembrane segments. This conclusion is supported by a recent study on potential of mean force for membrane protein folding and a study of scales for membrane propensity of amino acids. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009 [source]

Technical note: Prediction of sex based on five skull traits using decision analysis (CHAID)

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY, Issue 3 2009
Joan C. Stevenson
Abstract Osteologists commonly assess the sex of skeletal remains found in forensic and archaeological contexts based on ordinal scores of subjectively assessed sexually dimorphic traits. Using known-sex samples, logistic regression (LR) discriminant functions have been recently developed, which allow sex probabilities to be determined. A limitation of LR is that it emphasizes main effects and not interactions. Chi-square automatic interaction detection (CHAID) is an alternative classification strategy that emphasizes the information in variable interactions and uses decision trees to maximize the probability of correct sex determinations. We used CHAID to analyze the predictive value of the 31 possible combinations of five sexually dimorphic skull traits that Walker used previously to develop logistic regression sex determination equations. The samples consisted of 304 individuals of known sex of English, African American, and European American origin. Based on practical considerations, selection criteria for the best sex predictive trait combinations (SPTCs) were set at accuracies for both sexes of 75% or greater and sex biases lower than 5%. Although several of the trees meeting these criteria were produced for the English and European American samples, none met them for the African American sample. In the series of out-of-sample tests we performed, the trees from the English and combined sample of all groups predicted best. Am J Phys Anthropol, 2009. © 2009 Wiley-Liss, Inc. [source]

Dynamic hedging with futures: A copula-based GARCH model

THE JOURNAL OF FUTURES MARKETS, Issue 11 2008
Chih-Chiang Hsu
In a number of earlier studies it has been demonstrated that the traditional regression-based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies have emerged. In this study the authors propose a class of new copula-based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. With regard to the reduction of variance in the returns of hedged portfolios, the empirical results show that in both the in-sample and out-of-sample tests, with full flexibility in the distribution specifications, the copula-based GARCH models perform more effectively than other dynamic hedging models. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1095,1116, 2008 [source]

A Markov regime switching approach for hedging stock indices

THE JOURNAL OF FUTURES MARKETS, Issue 7 2004
Amir Alizadeh
In this paper we describe a new approach for determining time-varying minimum variance hedge ratio in stock index futures markets by using Markov Regime Switching (MRS) models. The rationale behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterized by regime shifts, which, in turn, suggests that by allowing the hedge ratio to be dependent upon the "state of the market," one may obtain more efficient hedge ratios and hence, superior hedging performance compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models such as GARCH, Error Correction and OLS in the FTSE 100 and S&P 500 markets. In and out-of-sample tests indicate that MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedging strategies only within sample. Overall, the results indicate that by using MRS models market agents may be able to increase the performance of their hedges, measured in terms of variance reduction and increase in their utility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:649,674, 2004 [source]