Synthesis of N3- and 2-NH2-substituted 6,7-diphenylpterins and their use as intermediates for the preparation of oligonucleotide conjugates designed to target photooxidative damage on single-stranded DNA representing the bcr-abl chimeric gene

Two 17-mer oligodeoxynucleotide-5'-linked-(6,7-diphenylpterin) conjugates, 2 and 3, were prepared as photosensitisers for targeting photooxidative damage to a 34-mer DNA oligodeoxynucleotide (ODN) fragment 1 representing the chimeric bcr-abl gene that is implicated in the pathogenesis of chronic myeloid leukaemia (CML). The base sequence in the 17-mer was 3'G G T A G T T A T T C C T T C T T5'. In the first of these ODN conjugates (2) the pterin was attached at its N3 atom, via a -(CH2)3OPO(OH)- linker, to the 5'-OH group of the ODN. Conjugate 2 was prepared from 2-amino-3-(3-hydroxypropyl)-6,7-diphenyl-4(3H)-pteridinone 10, using phosphoramidite methodology. Starting material 10 was prepared from 5-amino-7-methylthiofurazano[3,4-d]pyrimidine 4 via an unusual highly resonance stabilised cation 8, incorporating the rare 2H,6H-pyrimido[6,1-b][1,3]oxazine ring system. In the characterisation of 10 two pteridine phosphazenes, 15 and 29, were obtained, as well as new products containing two uncommon tricyclic ring systems, namely pyrimido[2,1-b]pteridine (20 and 24) and pyrimido[1,2-c]pteridine (27). In the second ODN conjugate the linker was -(CH2)5CONH(CH2)6OPO(OH)- and was attached to the 2-amino group of the pterin. In the preparation of 3, the N-hydroxysuccinimide ester 37 of 2-(5-carboxypentylamino)-6,7-diphenyl-4(3H)-pteridinone was condensed with the hexylamino-modified 17-mer. Excitation of 36 with near UV light in the presence of the single-stranded target 34-mer, 5'T G A C C A T C A A T A A G14 G A A G18 A A G21 C C C T T C A G C G G C C3' 1 caused oxidative damage at guanine bases, leading to alkali-labile sites which were monitored by polyacrylamide gel electrophoresis. Cleavage was observed at all guanine sites with a marked preference for cleavage at G14. In contrast, excitation of ODN-pteridine conjugate 2 in the presence of 1 caused oxidation of the latter predominantly at G18, with a smaller extent of cleavage at G15 and G14 (in the double-stranded portion) and G21. These results contrast with our previous observation of specific cleavage at G21 with ruthenium polypyridyl sensitisers, and suggest that a different mechanism, probably one involving Type 1 photochemical electron transfer, is operative. Much lower yields were found with the ODN-pteridine conjugate 3, perhaps as a consequence of the longer linker between the ODN and the pteridine in this case.

Market impact of international sporting and cultural events

We study market reaction to the announcements of the selected country hosting the Summer and Winter Olympic Games, the World Football Cup, the European Football Cup and World and Specialized Exhibitions. We generalize previous results analyzing a large number and different types of mega-events, evaluate the effects for winning and losing countries, investigate the determinants of the observed market reaction and control for the ex ante probability of a country being a successful bidder. Average abnormal returns measured at the announcement date and around the event are not significantly different from zero. Further, we find no evidence supporting that industries, that a priori were more likely to extract direct benefits from the event, observe positive significant effects. Yet, when we control for anticipation, the stock price reactions around the announcements are significant.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Jeotgalicoccus pinnipedialis sp nov., from a southern elephant seal (Mirounga leonina)

A previously unknown Gram-positive, catalase-positive, facultatively anaerobic, non-spore-forming, coccus-shaped bacterium (A/G14/99/10(T)), originating from the mouth of a female southern elephant seal, was subjected to a taxonomic analysis. Comparative 16S rRNA gene-sequencing showed that the organism formed a hitherto unknown subline within the catalase-positive, low-G+C, Gram-positive cocci, exhibiting a specific association with species of the genus Jeotgalicoccus. Sequence divergence values of approximately 7%, together with phenotypic differences, showed the unknown bacterium to be distinct from the two described species of this genus, Jeotgalicoccus halotolerans and Jeotgalicoccus psychrophilus. Based on phenotypic and phylogenetic considerations, it is proposed that strain A/G14/99/10(T)=CCUG 42722(T)=CIP 107946(T) from the mouth of a seal be classified as the type strain of a novel species of the genus Jeotgalicoccus, Jeotgalicoccus pinnipedialis sp. nov.

Modelling regulatory change V's volume of trading effects in HSIF and HSI volatility

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

Forecasting stock index volatility : comparing implied volatility and the intraday high-low price range

The intraday high–low price range offers volatility forecasts similarly efficient to high-quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in-sample and out-of-sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.

Estimating expected excess returns using historical and option-implied volatility

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward-to-risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option-implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option-implied volatility risk measure yields a positive, but statistically insignificant, risk-return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal-to-noise ratio in the latter subperiod.

Investor heterogeneity and the cross-sectional stock returns in China

We examine the relationship between divergence of opinion and the cross-sectional stock returns in Chinese A share market where short-selling of stocks is prohibited by law. Using a proxy for divergence of opinion among the entire investor base, we document a positive relationship between divergent beliefs and future stock returns. This is in sharp contrast to Miller's (1977) prediction of a negative relationship between the two. The result is likely to be driven by the dominance of individual investors and their speculative trading behaviors in China. Miller's prediction is confirmed when divergence of opinion is measured using data on mutual fund holdings. Our results are robust to a number of common return predictors. We also find a significantly negative relationship between the fraction of tradable shares in listed Chinese companies and future stock returns. Increase in the fraction of tradable shares tends to reduce the predictability of stock returns using divergence of opinion.

Estimating Forward Pricing Function: How Efficient is Indian Stock Index Futures Market?

This paper uses Indian stock futures data to explore unbiased expectations and efficient market hypothesis. Having experienced voluminous transactions within a short time span after its establishment, the Indian stock futures market provides an unparalleled case for exploring these issues involving expectation and efficiency. Besides analyzing market efficiency between cash and futures prices using cointegration and error correction frameworks, the efficiency hypothesis is also investigated after explicitly modeling the underlying state of the market (expansion or contraction) through the first-order Markov switching set-up. The results based on Markov switching analysis show that relatively longer time horizon is more effective in eliminating arbitrage opportunities than the short run.

No news is not good news: evidence from the intra-day return volatility–volume relationship in Shanghai Stock Exchange

Through this research, we find that the asymmetric volatility phenomenon is reversed in the Shanghai Stock Exchange during bull markets. That is, volatility increases more with good news than with bad news. This evidence is inconsistent with the US markets. Further examination of this phenomenon reveals that the positive impact of good news on volatility is driven by the return-chasing behaviour of investors during bull markets. We also find that volatility increases after stock price declines in bear markets. After controlling for liquidity shifts, we observe similar patterns in volatility in both bull and bear markets. We posit that institutional and behavioural factors are the major driving forces of observed volatility patterns in the Chinese stock market.