On the Assessment of Risk

Blume, Marshall E.

doi:10.2307/2325736

Cited by 200 publications

(203 citation statements)

References 0 publications

Supporting

Mentioning

179

Contrasting

Unclassified

Order By: Relevance

“…The other riskfree rates in existence-rf1 and rf2-are utilized only in equation (2) where the expected returns for "securities" N + 1 through 8 Since almost all securities have positive betas, the requirement that 8i3 be nonnegative is not an unrealistic one. See, for example, Blume (1971). 9 Not considered here is the case where riskfree borrowing is allowed but is subject to a margin constraint at the individual security level instead of at the aggregate portfolio level.…”

Section: Riskfree Borrowingmentioning

confidence: 99%

Short Selling and Efficient Sets

Alexander¹

1993

The Journal of Finance

View full text Add to dashboard Cite

The effect of short selling on the composition and location of the efficient set has been analyzed in a variety of ways. However, the situation typically facing investors where the initial margin requirement is less than 100 percent and the riskfree interest rate that is paid on the short proceeds is less than the rate paid on initial margin has not previously been considered. The Elton‐Gruber‐Padberg algorithm (1976, 1978), subject to certain modifications, is shown here to be capable of identifying the efficient set under such conditions.

show abstract

Section: Riskfree Borrowingmentioning

confidence: 99%

Short Selling and Efficient Sets

Alexander¹

1993

The Journal of Finance

View full text Add to dashboard Cite

show abstract

“…In these studies, no attempt was made to examine the intertemporal stability of the volatility measures. Blume did investigate the stationarity of the beta coefficient derived from a regression equation which correlated individual stock returns [2] and also portfolio returns [3] with aggregate market returns. While his study dealt with coefficients computed for much longer time spans (7 years) than those we employ, Blume concluded that the beta parameter is, in fact, quite stationary over time.…”

Section: Prior Volatility Studiesmentioning

confidence: 99%

Volatility Behavior of Industrial Stock Price Indices

Schwartz¹,

Altman²

1973

The Journal of Finance

View full text Add to dashboard Cite

THIS PAPER is part of our continuing analysis of common stock price fluctuations, which we call volatility patterns.' Here, our focus is upon industrial stock price indices. We investigate (1) whether or not an industry can be classified according to its stock price volatility-i.e. whether a series of volatility statistics, each computed for a relatively short time span, will be fairly constant for individual industries over time, and (2) if inter-industry volatility differentials remain constant over time.Part I presents a brief discussion of general issues related to the topic of volatility. Part II summarizes the computational models, and Part III presents the empirical results yielded by the industry study. Part IV summarizes our findings.I. GENERAL ISSUES ON VOLATILITY In considering volatility, our goal is not to predict the direction of future price changes on the basis of historical data,2 but rather to assess whether price fluctuations (regardless of direction) are likely to occur. We hypothesize that, over a succession of time periods, the volatility of prices will be reasonably constant. In our earlier firm specific analysis of price fluctuations [1], we did observe that, for some firms, stock prices were quite volatile relative to the fluctuations exhibited by other firms. In this present paper, we extend the analysis to industry groupings of firms. Evidence that some industries are consistently more volatile than others would suggest that traditional groupings of firms by industry are useful for analytical purposes.3 In addition, knowledge of volatility patterns would be useful information for the formulation of investment strategies and for portfolio management.This study of short run volatility patterns should be of particular interest to * Associate Professors of Economics and Finance, Graduate School of Business Administration, New York University. Edward I. Altman was a Visiting Professor of Finance at the Center d'Enseignement Superieure des Affaires in France during the 1971-72 academic year. The authors wish to acknowledge the research assistance of Allistair Hunter Henderson, and the helpful comments of an unknown referee.1. In a prior paper [1], we present two basic models employed to measure volatility, and several .approaches to testing for stability. In this earlier work, we were concerned with the empirical measurement of the volatility behavior of specific firms' stock price movements. We utilize the same measures of volatility in the current paper, and will review them in Part II. See Cootner[51 for a collection of major historical works in the random walk literature, and Fama [7] for a discussion of the literature on efficient capital market models and related empirical tests. 3. Sharpe [17] and King [11], among others, have shown that economic and financial information relates, primarily, either to specific firms, to industries, or to the aggregate market. Also see Elton and Gruber [6] for a further analysis of the meaningfulness of industry groupings. 957 958 The Journal of Finance

show abstract

“…'3 Both of these adjustments lessened over time for the NYSE sample, suggesting an adaptation to the ban. In the absence of an acceptable control group,'4 a tentative 9. Successive five day volumes were formed for the last two five day intervals before imposition and the first four five day intervals after imposition.…”

Section: Discussionmentioning

confidence: 99%

“…The details are not presented here. 9 The empirical results indicate that, prior to the imposition date, large increases in relative volume were observed. Again, this is to be expected, since a volume surge is a prerequisite for the credit ban.…”

Section: Volume Behaviormentioning

confidence: 95%

“…More elaborate procedures, such as the generation of performance residuals based on the linear market model (see [8]) are not used. Besides lacking the necessary data, there is still some question of the stationarity of the regression coefficients (see [9], [10]). Further, the main aim of this paper is to examine the generalizability of Largay's results, and a retention of the same basic experimental design was felt to be desirable.…”

Section: The Nyse Composite Index Is Used For the Nyse Sample And Thmentioning

confidence: 99%

See 1 more Smart Citation