Return Data (return + data)

Distribution by Scientific Domains


Selected Abstracts


INDIVIDUAL EQUITY RETURN DATA FROM THOMSON DATASTREAM: HANDLE WITH CARE!

THE JOURNAL OF FINANCIAL RESEARCH, Issue 4 2006
Ozgur S. Ince
Abstract We compare individual U.S. equity return data from Thomson Datastream (TDS) with similar data from the Center for Research in Security Prices (CRSP) to evaluate TDS for use in studies involving large numbers of individual equities in markets outside the United States. We document important issues of coverage, classification, and data integrity and find that naive use of TDS data can have a large impact on economic inferences. We show that after careful screening of the TDS data, inferences drawn from TDS data are similar to those drawn from CRSP. We illustrate the importance of the screens we develop using U.S. TDS data by applying the screens to TDS data from four European equity markets. [source]


Do Stock Prices and Volatility Jump?

THE JOURNAL OF FINANCE, Issue 3 2004
Option Prices, Reconciling Evidence from Spot
This paper examines the empirical performance of jump diffusion models of stock price dynamics from joint options and stock markets data. The paper introduces a model with discontinuous correlated jumps in stock prices and stock price volatility, and with state-dependent arrival intensity. We discuss how to perform likelihood-based inference based upon joint options/returns data and present estimates of risk premiums for jump and volatility risks. The paper finds that while complex jump specifications add little explanatory power in fitting options data, these models fare better in fitting options and returns data simultaneously. [source]


Resuscitating the C-CAPM: empirical evidence from France and Germany

INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 4 2005
Stuart Hyde
Abstract In this paper we analyse whether the consumption based capital asset pricing model is consistent with asset return data from the French and German stock markets. We evaluate the performance of the C-CAPM by applying the non-parametric methodology of Hansen and Jagannathan and adopting five alternative specifications of utility. In addition to standard power utility we adopt the recursive preferences model proposed by Epstein and Zin. We also consider both internal and external habit formation (persistence) using the models proposed by Constantinides, Abel and Campbell and Cochrane. We evaluate our findings using the tests of Burnside and Hansen and Jagannathan. We find that the majority of models produce stochastic discount factors consistent with the data. However, high degrees of risk aversion are implied for the models to be consistent. Incorporating habit formation only partially reduces the implied levels of risk aversion. Copyright 2005 John Wiley & Sons, Ltd. [source]


Land of addicts? an empirical investigation of habit-based asset pricing models

JOURNAL OF APPLIED ECONOMETRICS, Issue 7 2009
Xiaohong Chen
This paper studies the ability of a general class of habit-based asset pricing models to match the conditional moment restrictions implied by asset pricing theory. We treat the functional form of the habit as unknown, and estimate it along with the rest of the model's finite dimensional parameters. Using quarterly data on consumption growth, assets returns and instruments, our empirical results indicate that the estimated habit function is nonlinear, that habit formation is better described as internal rather than external, and the estimated time-preference parameter and the power utility parameter are sensible. In addition, the estimated habit function generates a positive stochastic discount factor (SDF) proxy and performs well in explaining cross-sectional stock return data. We find that an internal habit SDF proxy can explain a cross-section of size and book-market sorted portfolio equity returns better than (i) the Fama and French (1993) three-factor model, (ii) the Lettau and Ludvigson (2001b) scaled consumption CAPM model, (iii) an external habit SDF proxy, (iv) the classic CAPM, and (v) the classic consumption CAPM. Copyright 2009 John Wiley & Sons, Ltd. [source]


Global planning on the Mars Exploration Rovers: Software integration and surface testing

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 4 2009
Joseph Carsten
In January 2004, NASA's twin Mars Exploration Rovers (MERs), Spirit and Opportunity, began searching the surface of Mars for evidence of past water activity. To localize and approach scientifically interesting targets, the rovers employ an onboard navigation system. Given the latency in sending commands from Earth to the Martian rovers (and in receiving return data), a high level of navigational autonomy is desirable. Autonomous navigation with hazard avoidance (AutoNav) is currently performed using a local path planner called GESTALT (grid-based estimation of surface traversability applied to local terrain) that incorporates terrain and obstacle information generated from stereo cameras. GESTALT works well at guiding the rovers around narrow and isolated hazards; however, it is susceptible to failure when clusters of closely spaced, nontraversable rocks form extended obstacles. In May 2005, a new technology task was initiated at the Jet Propulsion Laboratory to address this limitation. Specifically, a version of the Field D* global path planner was integrated into MER flight software, enabling simultaneous local and global planning during AutoNav. A revised version of AutoNav was then uploaded to the rovers during the summer of 2006. In this paper we describe how this integration of global planning into the MER flight software was performed and provide results from both the MER surface system test bed rover and five fully autonomous runs by Opportunity on Mars. 2009 Wiley Periodicals, Inc. [source]


Asset Pricing Information in Vintage REIT Returns: An Information Subset Test

REAL ESTATE ECONOMICS, Issue 1 2005
David H. Downs
REIT return data prior to the new REIT era offer important asset pricing information. At issue is whether empiricists should focus attention on returns series covering only the new period. We use a generalized asset pricing and information subset test to disentangle REIT information from information available in several benchmark series. Results indicate that REIT returns are informative about the discounting process during the pre,new-era period. Thus, the distribution of vintage REIT returns is not fully explained by either broad market indexes or from size-based anomalies. This study should be viewed as a useful empirical precedent for those studying REIT data preceding the new REIT era. [source]


INDIVIDUAL EQUITY RETURN DATA FROM THOMSON DATASTREAM: HANDLE WITH CARE!

THE JOURNAL OF FINANCIAL RESEARCH, Issue 4 2006
Ozgur S. Ince
Abstract We compare individual U.S. equity return data from Thomson Datastream (TDS) with similar data from the Center for Research in Security Prices (CRSP) to evaluate TDS for use in studies involving large numbers of individual equities in markets outside the United States. We document important issues of coverage, classification, and data integrity and find that naive use of TDS data can have a large impact on economic inferences. We show that after careful screening of the TDS data, inferences drawn from TDS data are similar to those drawn from CRSP. We illustrate the importance of the screens we develop using U.S. TDS data by applying the screens to TDS data from four European equity markets. [source]


Inducing normality from non-Gaussian long memory time series and its application to stock return data

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2010
Kyungduk Ko
Abstract Motivated by Lee and Ko (Appl. Stochastic Models. Bus. Ind. 2007; 23:493,502) but not limited to the study, this paper proposes a wavelet-based Bayesian power transformation procedure through the well-known Box,Cox transformation to induce normality from non-Gaussian long memory processes. We consider power transformations of non-Gaussian long memory time series under the assumption of an unknown transformation parameter, a situation that arises commonly in practice, while most research has been devoted to non-linear transformations of Gaussian long memory time series with known transformation parameter. Specially, this study is mainly focused on the simultaneous estimation of the transformation parameter and long memory parameter. To this end, posterior estimations via Markov chain Monte Carlo methods are performed in the wavelet domain. Performances are assessed on a simulation study and a German stock return data set. Copyright 2009 John Wiley & Sons, Ltd. [source]


One-way analysis of variance with long memory errors and its application to stock return data

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 6 2007
Jaechoul Lee
Abstract Recent empirical results indicate that many financial time series, including stock volatilities, often have long-range dependencies. Comparing volatilities in stock returns is a crucial part of the risk management of stock investing. This paper proposes two test statistics for testing the equality of mean volatilities of stock returns using the analysis of variance (ANOVA) model with long memory errors. They are modified versions of the ordinary F statistic used in the ANOVA models with independently and identically distributed errors. One has a form of the ordinary F statistic multiplied by a correction factor, which reflects slowly decaying autocorrelations, that is, long-range dependence. The other is a test statistic such that the degrees of freedom of the denominator in the ordinary F test statistic is calibrated by the so-called effective sample size. Empirical sizes and powers of the proposed test statistics are examined via Monte Carlo simulation. An application to German stock returns is presented. Copyright 2007 John Wiley & Sons, Ltd. [source]


Do Stock Prices and Volatility Jump?

THE JOURNAL OF FINANCE, Issue 3 2004
Option Prices, Reconciling Evidence from Spot
This paper examines the empirical performance of jump diffusion models of stock price dynamics from joint options and stock markets data. The paper introduces a model with discontinuous correlated jumps in stock prices and stock price volatility, and with state-dependent arrival intensity. We discuss how to perform likelihood-based inference based upon joint options/returns data and present estimates of risk premiums for jump and volatility risks. The paper finds that while complex jump specifications add little explanatory power in fitting options data, these models fare better in fitting options and returns data simultaneously. [source]


WINDOW DRESSING IN BOND MUTUAL FUNDS

THE JOURNAL OF FINANCIAL RESEARCH, Issue 3 2006
Matthew R. Morey
Abstract We examine portfolio credit quality holding and daily return patterns in a large sample of bond mutual funds and document evidence of window dressing. Using portfolio credit quality holdings data, we find that bond funds on average hold significantly more government bonds during disclosure than nondisclosure, presumably to present a safer portfolio to shareholders. Multiple-index market models estimated with daily returns data corroborate these findings. We detect differences in factor loadings on days surrounding disclosure dates that indicate systematic tilting of the portfolio toward higher quality instruments. [source]


Improved estimation of portfolio value-at-risk under copula models with mixed marginals

THE JOURNAL OF FUTURES MARKETS, Issue 10 2006
Douglas J. Miller
Portfolio value-at-risk (PVAR) is widely used in practice, but recent criticisms have focused on risks arising from biased PVAR estimates due to model specification errors and other problems. The PVAR estimation method proposed in this article combines generalized Pareto distribution tails with the empirical density function to model the marginal distributions for each asset in the portfolio, and a copula model is used to form a joint distribution from the fitted marginals. The copula,mixed distribution (CMX) approach converges in probability to the true marginal return distribution but is based on weaker assumptions that may be appropriate for the returns data found in practice. CMX is used to estimate the joint distribution of log returns for the Taiwan Stock Exchange (TSE) index and the associated futures contracts on SGX and TAIFEX. The PVAR estimates for various hedge portfolios are computed from the fitted CMX model, and backtesting diagnostics indicate that CMX outperforms the alternative PVAR estimators. 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:997,1018, 2006 [source]