Practitioners' Corner

The first 150 words of the full text of this article appear below. The year 1982 was not a particularly good period for the worldeconomy. At year's end, the Organization for Economic Cooperationand Development (OECD) revised its growth figures for membernations from slightly over 1% to –0.5%, with some 32 millionunemployed in its 24 member states. In the United States thejobless rate was 11% and 30% of plant capacity stood idle. OttoEckstein found the economy in its worst shape in nearly halfa century. Truly the year belonged to Scrooge. Yet 1982 was a very good year indeed for financial econometrics,the debut of an explosion of activity in the area that continuesvigorously 20 years later, as the emergence of the Journal ofFinancial Econometrics attests. In fact, it can convincinglybe argued that 1982 heralded the beginning of our subject, andperhaps with the recent awarding of the Nobel Prize in Economicsto Robert Engle . . . [Full Text of this Article]     

Practitioners' Corner

The first 150 words of the full text of this article appear below. Spring at last. Here in Montreal, the transition from winteris particularly delicious. There is more light, the world looksless grey, and the trepidation that another storm may be aroundthe corner is put away with the winter tires. In this issueof JFEC, the editors appear to be underscoring seasonal changein an issue whose unifying theme involves parameter and modelstability. Parameter stability is an important issue in econometrics. Achanging economic environment may be captured by allowing theparameters of a reduced form model to vary to reflect changingconditions. The challenge for the econometrician is to constructmodels that do more than reflect changes in an ad hoc manner.For want of more insightful approaches, the challenge is oftensidestepped in practice by focusing on shorter samples whereno structural change can be assumed to have occurred. However,in so constricting the sample size, . . . [Full Text of this Article]     

Practitioners' Corner: Introduction to the Special Issue

The first 150 words of the full text of this article appear below. The material in this volume is the result of a call for papersto the participants of the "Conference on Analysis of High-FrequencyFinancial Data and Market Microstructure" held in December 2003in Taipei, Taiwan. Jeffrey Russell and Ruey Tsay have actedas guest editors for this special issue, together with the editorsRené Garcia and Eric Renault. The availability of high-frequency data has spawned considerableliterature on volatility measurement and forecasting. The materialis mathematically delicate and perhaps "Practitioners’Corner" would be well advised to let the dust settle a bit tosee what emerges at the end of the day. On the other hand, thepractically minded may well be served by a good road map ofthe issues. So with only mild apology do we take up the cartographyof some difficult terrain. To fix ideas, let S(t) denote the price process of a . . . [Full Text of this Article]     

Practitioners' Corner

The first 150 words of the full text of this article appear below. In our discussion in the last issue of Journal of FinancialEconometrics (JFEC) of the nonparametric methods developed byBarndorff-Nielsen and Shephard (2006) to detect jumps in thelocal behavior of the continuous time path of a price process,we observed these tests were not designed to detect major pricediscontinuity events such as the 1987 crash, since the testingmethodology precludes jumps in adjacent time intervals. Indeed,a major event such as Black Monday is characterized by a sequenceof jumps in consecutive time intervals throughout the day. Inthe interest of thematic continuity, let’s pursue thematter of jumps further. The first article in the current issue by Hossein Asgharianand Chistoffer Bengtsson addresses directly the detection ofbig events in stock prices. More particularly, the authors analyzethe spillover of jumps across international stock markets. Tomeasure jumps, the authors formulate a parametric model in . . . [Full Text of this Article]     

Practitioners' Corner

The first 150 words of the full text of this article appear below. During the 1980s, the early stages of modeling financial timeseries focused on the striking stylized fact that while returnswere themselves not serially correlated, squared returns were.This history has been nicely documented in the influential bookby Taylor (1986) and, indeed, the opening chapters of contemporaryfinancial econometrics open with Engle (1982) and Bollerslev(1986) who provided a specific ARMA structure of squared returnsvia the celebrated [G]ARCH models. This general orientationin effect acknowledged that there was some room for predictingrisk, as measured by squared values or absolute values of returns,while at the same time maintaining the hypothesis that returnsthemselves were hardly predictable in keeping with some versionof market efficiency. However, this paradigmatic view has beenchallenged over the subsequent 20 years in at least three regards. First, with Nelson (1991), it has been widely acknowledged thatalthough GARCH modeling is about . . . [Full Text of this Article]     

Practitioners' Corner

The first 150 words of the full text of this article appear below. This issue of the Journal of Financial Econometrics containspapers that were presented at the (EC)² conference Econometricsof Financial and Insurance Risks held in Istanbul on December15–17, 2005. Launched in 1990, (EC)² is an annual seriesof international conferences on research in quantitative economicsand econometrics. The acronym stands for European Conferencesof the Econom[etr]ics Community. Its primary aim is to providea vibrant forum where both senior and junior European researchersin quantitative economics and econometrics can discuss the resultsand progress of their research. (EC)² conferences are of relativelysmall scale (less than 200 participants) and quite intensive.Each year a different topic is selected as the major theme ofthe conference. A few leading quantitative economists or econometriciansare invited as keynote speakers, such as Eric Ghysels, who alsoacts as co-Guest editor of this issue; the other speakers areselected . . . [Full Text of this Article]     

Practitioners' Corner

The first 150 words of the full text of this article appear below. The transition from some preliminary qualitative assessmentsto quantitative assertions is a hallmark of scientific progress.One dollar today is more valuable than one dollar availabletomorrow, but the scientific issue is by how much and why thisamount? Similarly a random gain with unit expectation and significantrisk is less valuable than one dollar available with certainty.Financial theories of the discount rate and the mean-variancetrade-off have afforded helpful quantitative answers to thesecrucial issues in asset pricing, investment, and risk management.Meanwhile, modern financial econometrics has characterized thedynamic features of interest rates as well as of risk and returnthrough state variables models with volatility clustering, mean-volatilityfeedback, and dynamic correlations. From this perspective, a striking common feature of all thearticles in the current issue of the Journal of Financial Econometricsis the reintroduction of qualitative variables previously treatedusing purely quantitative approaches. The first . . . [Full Text of this Article]     

Practitioners' Corner

The first 150 words of the full text of this article appear below. The analysis of volatility remains a preoccupation. In our veryfirst issue, "Practitioners' Corner" offered a brief retrospectiveon volatility modeling, surveying several strategies in thehistory of volatility modeling and locating the contributionsof the first issue within these broad themes. Of course, notall the highlights of this voluminous literature could be visitedor all noteworthy references cited. Nonetheless, we should havereferred to the venerable literature on mixture models introducedby the polymath Simon Newcomb in the late 19th century and subsequentlystudied by Karl Pearson. A neglected reminder was certainlysupplied by Lanne and Saikkonen (2003), who in this same firstissue of JFEC offered the wry understatement that the conditionalheteroskedasticity inherent in mixture autoregressive modelsmay not adequately capture the time-series properties of financialdata. The point is made again in the contribution to this issueby Markus Haas, Stefan Mittnik, and Marc . . . [Full Text of this Article]