A survey of sampling-based Bayesian analysis of financial data

References

• Aggarwal , R. and Aggarwal , R. 1993 . Security return distributions and market structure: evidence from the NYSE/AMEX and the NASDAQ Markets . Journal of Financial Research , XVI : 209 – 220 .
   Google Scholar
• Barber , B. and Lyon , J. 1997 . Detecting long-run abnormal stock returns: the empirical power and specification of test statistics . Journal of Financial Economics , 43 : 341 – 372 .
   Web of Science ®Google Scholar
• Black , F. and Scholes , M. 1973 . The pricing of options and corporate liabilities . Journal of Political Economy , 81 : 637 – 659 .
   Web of Science ®Google Scholar
• Boehmer , E. , Musumeci , J. and Poulsen , A. 1991 . Event-study methodology under conditions of event-induced variance . The Journal of Financial Economics , 30 : 253 – 272 .
   Web of Science ®Google Scholar
• Brav , A. 2000 . Inference in long-horizon event studies: a Bayesian approach with application to initial public offerings . Journal of Finance , 55 : 1979 – 2016 .
   Web of Science ®Google Scholar
• Brown , S. and Warner , J. 1980 . Measuring security price performance . Journal of Financial Economics , 8 : 205 – 258 .
   Web of Science ®Google Scholar
• Brown , S. and Warner , J. 1985 . Using daily stock returns: the case of event studies . Journal of Financial Economics , 14 : 1 – 31 .
   Web of Science ®Google Scholar
• Chib , S. and Greenberg , E. 1995 . Understanding the Metropolis-Hastings algorithm . The American Statistician , 49 ( 4 ) : 327 – 335 .
   Web of Science ®Google Scholar
• Conrad , J. 1989 . The price effect of option introduction . The Journal of Finance , XLIV ( 2 ) : 487 – 498 .
   Google Scholar
• Corrado , C. 1989 . A nonparametric test for abnormal security price perfromance in event studies . Journal of Financial Economics , 23 : 385 – 395 .
   Web of Science ®Google Scholar
• Corrado , C. and Zivney , T. 1992 . The specification and power of the sign test in event study hypothesis tests using daily stock returns . Journal of Quantitative and Financial Analysis , 27 : 465 – 478 .
   Web of Science ®Google Scholar
• Cowan , A. 1992 . Nonparametric event study tests . Review of Quantitative Finance and Accounting , 2 : 343 – 358 .
   Google Scholar
• Cowan , A. and Sergeant , A. 2001 . Interacting biases, non-normal return distributions and the performance of tests for long-horizon event studies . Journal of Banking and Finance , 25 : 741 – 765 .
   Web of Science ®Google Scholar
• Cowles , M. K. and Carlin , B. P. 1996 . Markov chain Monte Carlo convergence diagnostics: a comparative review . Journal of the American Statistical Association , 91 : 883 – 905 .
   Web of Science ®Google Scholar
• Dodd , P. and Wrner , J. 1983 . On corporate governance: a study of proxy contests . Journal of Financial Economics , 11 : 401 – 438 .
   Web of Science ®Google Scholar
• Fama , E. , Fisher , L. , Jonsen , M. and Roll , R. 1969 . The adjustment of stock prices to new information . International Economic Review , 10 : 1 – 21 .
   Google Scholar
• Fernandez , C. , Osicwalski , J. and Steel , M. 1997 . On the use of panel data in stochastic frontier models with improper priors . Journal of Econometrics , 79 : 169 – 173 .
   Web of Science ®Google Scholar
• Gumerman , D. 1997 . Markav Chain Monte Carlo. Stochastic Simulation for Bayesian Inference , New York : Chapman & Hall .
   Google Scholar
• Gelfand , A. and Smith , A. F. M. 1990 . Sampling-based approaches to calculating marginal densities . Journal of the American Statistical Association , 85 : 398 – 409 .
   Web of Science ®Google Scholar
• Gelfand , A. , Hills , S. , Racine-Poon , A. and Smith , A. 1990 . Illustration of Bayesiun inference in cnormal data models using Gibbs sampling . Journal of the American Statistical Association , 85 : 972 – 985 .
   Web of Science ®Google Scholar
• Gelfand , A. and Sfiridis , J. 1996 . “ Bayesian analysis of financial event studies data ” . In Advances in Econometrics: Bayesian Computational Methods and Applications , Edited by: Carter Hill , R. 25 – 62 . Greenwich, Conn : JAI Press . II. Part A
   Google Scholar
• Gelman , A. and Rubin , D. B. 1992 . Inference from iterative simulation using multiple sequences . Statistical Science , 7 : 457 – 472 .
   Google Scholar
• Gelman , A. , Carlin , J. , Stern , H. and Rubhin , D. B. 1996 . Bayesian Data Analysis , New York : Chapman & Hall .
   Google Scholar
• Giaecotto , C. and Sfiridis , J. 1996 . Hypothesis testing in event studies: the case of variance changes . Journal of Economics and Business , 48 : 349 – 370 .
   Google Scholar
• Harvey , C. R. and Zhou , H. 1993 . International asset pricing with alternative distributional specifications . Journal of Empirical Finance , 1 : 107 – 131 .
   Google Scholar
• Hastings , W. K. 1970 . Monte Carlo sampling methods using Markov chain and their applications . Biometrika , 87 : 97 – 109 .
   Web of Science ®Google Scholar
• Ikenberry , D. , Lakonishok , J. and Vermaelen , T. 1995 . Market underreaction to open market share reparchases . Journal of Financial Economic , 39 : 181 – 208 .
   Web of Science ®Google Scholar
• Jacquier , E. , Polson , N. and Rossi , P. 1994 . Bayesian analysis of stochastic volatility models . Journal of Business & Economic Statistics , 12 : 371 – 389 .
   Web of Science ®Google Scholar
• Kothari , S. P. and Warner , J. 1997 . Measuring long-horizon security performance . Journal of Financial Economics , 43 : 301 – 339 .
   Web of Science ®Google Scholar
• Loughran , T. and Ritter , J. 1995 . The new issues puzzle . Journal of Finance , 50 : 23 – 51 .
   Web of Science ®Google Scholar
• Metropolis , N. , Rosenbluth , A. W. , Rosenbluth , M. N. , Teller , A. H. and Teller , E. 1953 . Equations of state calculations by fast computing machines . Journal of Chemical Physics , 21 : 1087 – 1091 .
   Web of Science ®Google Scholar
• Poon , P. 1994 . An empirical examination of the return volatility-volume relation in related markets: the case of stocks an options . The Financial Review , 29 : 473 – 496 .
   Google Scholar
• Salinger , M. 1992 . Value event studies . Review of Economies and Statistics , 74 : 671 – 677 .
   Web of Science ®Google Scholar
• Smith , A. F. M. and Roberts , G. O. 1993 . Bayesian computations via the Gibbs samper and related Markov chain Monte Carlo methods . ournal of the Royal Statistical Surviety , 355 : 1 – 24 .
   Google Scholar
• Tierney , L. 1994 . Markov chains for exploring posterior distributions (with discussion) . Annals of Statistics , 22 : 1701 – 1762 .
   Web of Science ®Google Scholar
• Womack , L. K. 1996 . Do brokerage analysts' recommendations have investment value? . Journal of Finance. , 51 : 137 – 167 .
   Web of Science ®Google Scholar