“The Impact of Futures Trading on Stock Market Volatility: Review of the Theoretical and Empirical Studies”
This part of the study will be discussing the relevant literature connected with the study of the impact of futures trading on stock market volatility. This part of the study accounts the works that has been published on a topic by accredited scholars and researchers. All this would allow the readers to map the field and position the research within the context. Moreover, this part of the study justifies the reason for research. This is closely connected with demonstrating that is known in the field. It is the knowledge of the field that allows one to identify the gap, which the research could fill. Concurrently, it allows the researcher to establish the theoretical framework and methodological focus of the study.
This chapter shall be divided on two parts. The first part shall be discussing the theoretical models of stock market volatility. This shall cover the theories from Keynes to Fama and other noted scholars of stock market. The second part shall be discussing the empirical evidences that supported the theoretical claims of the earlier section.
Theoretical Debate on Stock Market Crashes
Neoclassical economists, especially those of the rational expectations persuasion, have experienced difficulty in explaining stock market crashes (Stiglitz 1990; Miller 1991, 87). In contrast, institutionalist and Post-Keynesian analyses of crashes can build on the solid foundation provided by the speculative market theories of Thorstein Veblen (1915), J. M. Keynes (1935), and J. K Galbraith (1988).
The Theory of Institutional Failure
The theory of institutional failure assumes that stock prices are normally determined by expected corporate earnings and attributes crash to developments that had effectively turned the separate markets for stocks, stock index futures, and stock options into one market that lacked sufficient regulations. The institutional market mechanisms had been weakened by new, dynamic, computerized trading strategies that linked the stock index futures market and the cash market for stocks. This created a new ability for speculative trading in the futures market to actually drive prices in the cash market (Barro 1989, 137).
Critics of the thesis that failure of institutional mechanisms caused the crash have argued that the cascade theory is seriously flawed in concept. Tosini (1988, 32), for example, noted that the cycle would tend to be broken as arbitrage buying would put upward pressure on futures prices. Miller (1991, 59-63) and others have argued that the volume of trade generated by index arbitrage and portfolio insurance was too small to explain the crash. Moreover, as the processing of transactions failed to provide the up-to-date information on prices needed to run the computerized trading programs, both the arbitrageurs and portfolio insurers were quickly sidelined as the crash developed.
The Rational Market Acuustment Theory
The rational market adjustment theory rests on the efficient markets hypothesis that all information about factors that affect stock prices is immediately impounded in market prices (Fama, 1970). Asset prices are viewed as rational assessments of expected future payoffs, i.e., they reflect the present value of economic agents’ unbiased calculations of expected corporate returns (Romer 1992). With current prices of assets always rational, there is no need for assumptions regarding the manner in which expectations of economic agents are formed. That simplifying feature is viewed as a strength of the theory by its proponents.
The rational market adjustment theory clearly suffers several conceptual weaknesses. Ross (1989, 7) conceded that “attempts to formalize the EMH as a consistent, analytical economic theory have met with less success than the empirical tests of the hypothesis.” Fama (1988, 76) conceded that there is no scientific way to determine whether prices are rational, i.e., unbiased estimates of fundamental values. Despite these weaknesses, the rational markets theory has a number of advocates who would seemingly have little to say about stock market crashes. Under the dictates of the pure form of the theory, analyses of stock market crashes would be limited to statements that the market moves swiftly from one rational set of prices to another rational set.
Since rational markets proponents view the stock market as an efficient mechanism, they view stock crashes as a rational market working with impressive speed were presented as arguments against the need for any reforms (Arbel et al. 1988, 124).
The Speculative Market Theory
A speculative market exists when stocks are demanded simply because the prices are expected to continue rising, with little or no regard paid to the relationship between the price of stock and future earnings potential. The extent to which speculation drives the market is not easily determined and to some degree is related to the development of the securities markets. Writing in the early 1900s, when the markets were easily manipulated by large investment banks and other financial interests, Veblen observed that “the mere buying and selling of stocks by outsiders for a rise or decline is of course a speculative business; it is a typical form of speculative business” [1915, 164-165]. But Veblen further noted that for the large financial interests, e.g., investment banks and managers of the corporations, who could manipulate the markets, stock transactions were not more speculative than ordinary business management of enterprises producing vendible products [1915,165-166].
In The General, Theory of Employment, Interest, and Money, Keynes defined speculation as “the activity of forecasting the psychology of the market,” as opposed to enterprise, “the activity of forecasting the prospective yield of assets over their whole life” (1935, 158). Given the manner in which market participants tend to make decisions to buy and sell stocks, the risk of the predominance of speculation over enterprise grows as securities markets become better organized to provide “liquidity.” Investors are always hampered in their decisions by a great lack of knowledge about future events. But with the growth of ownership of stocks by those not involved in management, there has been a serious decline in “the element of real knowledge in the valuation of investments by those who own them or contemplate purchasing them” [1935, 153]. Under these circumstances, investors turn to the convention that the present is a reliable guide to the future. The existing state is not expected to continue indefinitely, but as long as they can rely upon the maintenance Of that convention, investors can legitimately convince themselves that their only risk “is of a genuine change in the news in the near future” (1935, 153). Each investor attempts to form his own assessment as to the likelihood of such a change under the limiting belief that is usually will not be very large (1935, 152-153).
In general, the results of previous research evidence that the market of developed economies are generally weak form efficient. That means the successive returns are independent and follow random walk (Fama, 1965,1970). On the other hand, the research findings on the market of developing and less developed countries are controversial. Some of the researcher find evidence of weak form efficiency and cannot reject the random-walk hypothesis in emerging markets (Branes, 1986; Dickinson and Muragu, 1994; Urrutia, 1995). Whereas the others find the evidence of non-randomness stock price behavior and reject the weak-form efficiency in the developing and emerging markets (Poshakwale. S,1996 and Nourredine Khaba, 1998).
JUMP Models
Over the past several decades, some stylized facts have emerged about the statistical behavior of speculative market returns. The most important of these empirical findings are that asset returns are approximately a martingale difference sequence, the conditional variance is time-varying, and the unconditional distribution displays leptokurtosis (Chan and Maheu, 2002). Conventional wisdom on volatility dynamics is that generalized autoregressive conditional heteroscedasticity (GARCH) and stochastic volatility (SV) models provide a good first approximation to these stylized facts by modeling the autoregressive structure in the conditional variance (Chan and Maheu, 2002). Both the GARCH and SV models are designed to capture smooth persistent changes in volatility. But these models are not suited to explaining the large discrete changes found in asset returns. In most speculative markets, discrete jumps in returns are necessary to better match statistical features observed in the data (Andersen, Benzoni, and Lund 1999; Gallant, Hsieh, and Tauchen 1997), as well as reconcile mispricing in options markets (Bakshi, Cao, and Chen 1997; Bates 1996; Das and Sundaram 1999). A large literature has investigated the importance of jumps from statistical and asset pricing perspectives.
The basic Poisson jump model of stock returns used in finance was introduced by Press (1967), who called his approach a compound events model, because it can be motivated as the aggregation of a random number of price changes within a fixed time interval. The Poisson distribution is assumed to govern the number of events that result in price movements, and the average number of events in a time interval is called the intensity. The model is capable of producing skewness and excess kurtosis in returns. All volatility dynamics are assumed to be the result of discrete jumps in stock returns, and the size of a jump is stochastic and normally distributed. Several early empirical applications have demonstrated the usefulness of the Press model. Akgiray and Booth (1988), Tucker and Pond (1988), and Hsieh (1989) found that a normal-Poisson jump model provides a good statistical characterization of daily exchange rates. Similar results were found by Ball and Torous (1983) using stock returns.
The basic jump model has been extended in a number of directions. Estimation of continuous-time SV jump diffusion models requires simulation methods and only recently has been investigated by Anderson et al. (1999), Craine, Lochstoer, and Syrtveit (2000), Eraker, Johannes, and Poison (1999) and Chernov, Gallant, Ghysels, and Tauchen (1999). A tractable alternative is to combine jumps with an ARCH/GARCH model in discrete time. In this case the GARCH parameterization explains the smooth changes in volatility, whereas the jumps explain infrequent large discrete movements in asset returns. Applications of a GARCH-jump mixture model have been given by Vlaar and Palm (1993), and Nieuwland, Vershchoor, and Wolff (1994).
A common thread in these GARCH-jump mixture models is the assumption that a constant Poisson distribution directs the jump probability through time. However, it seems likely that the jump probability will change over time. Would we expect the probability of a jump in stock market returns before the 1987 stock market crash to be the same as other periods? The results of Bates (1996) would suggest the answer to this is no.
Recent research has extended the theoretical framework to permit a time-varying jump distribution. For example, Das (1998) and Fortune (1999) used dummy variables to allow the jump intensity to change over the week. Chernov et al. (1999) estimated specifications that allow the jump intensity to depend on the size of previous jumps, and a stochastic volatility factor. Eraker et al. (1999) modeled jumps in both returns and volatility.
Weak From Efficiency Model
The early studies on testing weak form efficiency started on the developed market, generally agree with the support of weak-form efficiency of the market considering a low degree of serial correlation and transaction cost (Cootner, 1962; Osborne, 1962; Fama, 1965). All of the studies support the proposition that price changes are random and past changes were not useful in forecasting future price changes particularly after transaction costs were taken into account. However, there are some studies, which found the predictability of share price changes (Fama and French, 1988; Poterba and Summers, 1988) in developed markets but they did not reached to a conclusion about profitable trading rules.
Poterba and Summers (1988) suggest that noise trading, trading by investors, whose demand for shares is determined by factors other than their expected returns provides a plausible explanation for the transitory component in stock prices. And they suggest constructing and testing theories of noise trading as well as theories of changing risk factors could account for the characteristics of stock returns auto-correlogram they found. Fama and French (1988) conclude that auto-correlation’s may reflect market inefficiency or time-varying equilibrium expected returns generated by rational investor behavior and neither view suggests, however, the patterns of auto-correlation should be stable for a long sample period.
Hudson, Dempsey and Keasey (1994) found that the technical trading rules have predictive power but not sufficient to enable excess return in U.K market. Similarly, Nicolaas, (1997) also conclude that past returns have predictive power in Australian market but the degree of predictability of return is not so high. Overall, the empirical studies on developed market shows no profitability from using past records of price series supports the weak-form efficiency of the EMH in general. On the other hand, the research findings of weak-form efficiency on the market of developing and less developed markets are controversial. Most of the less developed market suffers with the problem of thin trading. In addition, in smaller markets, it is easier for large traders to manipulate the market. Though it is generally believe that the emerging markets are less efficient, the empirical evidence does not always support the thought. There are two groups of findings; the first group find weak-form efficiency in developing and less developed markets are Branes (1986), (on the Kuala Lumpur Stock Exchange); Chan, Gup and Pan, 1992, (in major Asian markets); Dickinson and Muragu, 1994 (on the Nairobi Stock Exchange) and Ojah and Karemera 1999, (on the four Latin American countries market) despite the problems of thin trading.
On the other hand, the latter group, who evidence that the market of developing and less developed markets are not efficient in weak-sense are Cheung, Wong and Ho, (1993), on the stock market of Korea and Taiwan; in a world bank study by Claessens, Dasgupta and Glen (1995), report significant serial correlation in equity returns from 19 emerging markets and suggest that stock prices in emerging markets violates weak form EMH; similar findings are reported by Harvey (1994) for most emerging markets. Nourrrendine Kababa (1998) has examined the behavior of stock price in the Saudi Financial market seeking evidence that for weak-form efficiency and find that the market is not weak-form efficient. He explained that the inefficiency might be due to delay in operations and high transaction cost, thinness of trading and illuiquidity in the market. Roux and Gilberson (1978) and Poshakwale S. (1996) find the evidence of non-randomness stock price behavior and the market inefficiency (not weak-form efficient) on the Johannesburg stock Exchange and on the Indian market.
EMPIRICAL DATA
Tsibouris and Zeidenberg (1996) tested the weak form of Efficient Market Hypothesis by using daily returns of stocks from U.S. stock market (from 1988 until 1990) and they did manage to find evidence against it. White (1993) did not manage to find enough evidence to reject the EMH when he tried to predict the IBM stock returns on daily basis using data from 1972 to 1980. The conclusion from the results of these studies is that there is no clear evidence whether the market is predictable or not.
Kendall (1953) states that if stock prices are weak form efficient, then past prices contain no information about future changes and price changes are random. Kendall (1953) found that stock and commodity prices follow a random walk. A random walk implies zero correlation between price change at t and price change at t+1, which is what we observe. If price cycles were predictable competition between investors would eliminate them: Arbitrage/Speculation will force prices to their efficient values. If prices are predictable then a simple trading rule would be: BUY undervalued assets and SELL overvalued assets. Prices will only change on the basis of new information which by definition is random, hence price changes are random.
Fama (1965) notes empirical evidence in support of the Gaussian hypothesis has been offered by Kendall and highly contests such deductions, arguing that the empirical distributions of price changes are usually too peaked and that there are typically so many outliers that the data is unlikely to be represented by a Gaussian distribution.
Barone (1990) said that the idea of weak form efficiency requires that there are no consistent patterns in the stock prices, and consequently returns. While early tests of random walk did not detect any strong evidence of the existence of any return pattern. The tests were conducted using daily CAC 1 index from January 1978 to December 1987. They found no satisfactory explanation for the negative Tuesday returns on the Paris Bourse. Barone (1990) analyzed the MIB 2 stock index between January 1975 and August 1989. He found an average January return of 0.33 percent that is significantly different from zero.
Stock Prices In attempt to provide a glimpse of the significance of this study on the existing related literature, particularly in the field of trading, this part of the chapter shall be discussing studies on the unpredictability of stock prices. Much of the literature on program trading considers its effect on stock price volatility. Stoll and Whaley (1990) examine the consequences of program trading occurring on “triple-witching days,” that is, dates when multiple derivative contracts on stocks simultaneously expire. As heavy program trading frequently occurs on these expiration dates, Stoll and Whaley’s evidence of higher volatility suggests that program trading can be linked to increased volatility.
Edwards (1988) studies the impact of stock-index futures and finds that volatility does not increase after the introduction of multiple derivative contracts. Since these contracts are frequently involved in program trading strategies, an increase in stock price volatility would be consistent with a program trading effect. Maberly, Allen, and Gilbert (1989) note that this result depends on the sample period. Harris (1989) finds only a slight increase in volatility during the 1980s, suggesting that the increased program trading activity that was facilitated by futures trading had, at most, a very modest effect on volatility. Martin and Senchack (1989, 1991) find that the volatility of stocks included in the Major Market Index (MMI) rose after the MMI futures contract was introduced. Their risk decomposition indicates that the systematic risk of these stocks rose. Since the MMI futures contract is frequently involved in program trading, this finding suggests that program trading led to higher volatility.
Moreover, Froot, Perold, and Stein (1991) investigate returns on the Standard and Poor 500 since the 1930s. They find that changes in volatility are conditional on the length of the holding period. There is strong evidence of an increase in return volatility during the 1980s for 15-minute holding periods. When longer holding periods are examined, it is much less evident that volatility has changed. Miller (1990) suggests a conceptual distinction between the volatility of price changes and price-change velocity. While statistical tests frequently demonstrate no change in volatility levels, the speed of price adjustments does appear to have increased during the 1980s.
Froot and Perold (1990) decompose price changes into bid-ask bounce, nontrading effects, and noncontemporaneous cross-stock correlations. They demonstrate that price adjustments occurred more rapidly during the 1980s. Concurrently, direct investigation of the effects of program trading finds temporary increases in volatility that are most prominent in index arbitrage activities. Duffee, Kupiec, and White (1990) review much of this evidence. Grossman (1988) regresses various measures of daily price volatility on program trading intensity, finding no significant effect.
Furthermore, a Securities and Exchange Commission study (1989) finds a positive association between daily volatility of changes in the Dow Jones Index and levels of program trading activity. Furbush (1989) finds a significant relationship between price volatility and program trading activity in the three days prior to the October 19, 1987, market break. Harris, Sofianos, and Shapiro (1990) and Neal (1991) investigate intraday program trading, finding that responses to program trades are similar to those found for block trades. Using “GARCH” estimation procedures, Moser (1994) finds a modest increase in the volatility of returns for one-day holding periods associated with sell program activity. Thus, the evidence is inconclusive.
Fama’s Model: Empirical Results
Research into volatility itself has stimulated research into momentum and financial herding. Grinblatt et al. (1995), and Wermers (1999) came to the conclusion that a large part of herding behavior occurs when investors “momentum-follow,” and Nofsinger and Sias (1999) found evidence that implicates the use of momentum strategies by growth-oriented funds as an important source of herding. What is worthy of note is that momentum and herding have a notable impact on market price that is not related to economic or financial fundamentals. Market price seldom corresponds to intrinsic value and this disequilibrium can continue for extensive periods of time. Moreover, whereas economic and financial fundamentals will affect value, they are not the main movers of stock prices.
In this regard, Fama (1981) found that a substantial fraction of return variation couldn’t be explained by macroeconomic news. Roll (1984) found that news about weather conditions, the principal source of variation in the price of orange juice, explains only about 10% of the movement in orange juice futures prices. Roll (1988) further found that it is difficult to account for more than one-third of the monthly variation in individual stock returns on the basis of systematic economic influences. When investigating which factors moved share price, Cutler et al. (1989) found that macroeconomic news explains only about one-fifth of the movement in stock prices, and they state: “The view that movement in stock prices reflect something other than news about fundamental values is consistent with evidence on the correlates of ex post returns (1989: 9). Haugen et al. (1991) established that the main driver of stock returns was changes in volatility, and that fundamental economic and financial factors were not the main drivers of changes in volatility. In fact, they found that as few as one-quarter of the volatility shifts are associated with the release of significant (financial and economic) information.
It is in the uncertainty of market behavior brought about the emergence of forecasting techniques such as the efficient market hypothesis (EMH). Nevertheless, debate continues over the validity of the efficient market hypothesis (EMH), which holds that security prices fully reflect all available information at any given time. Fama (1970) has categorized market efficiency into three levels in which the definition of information varies into the weak form, which deals with the information contained in previous prices or price trends; the semi-strong form, which broadens the definition to include all publicly available information; and the strong form, which broadens the definition to include even privately held information. Exhaustive tests have been conducted to determine the level of efficiency of large financial markets. This paper shifts the focus of the debate to a smaller speculative market – sports gambling – where new, more definitive evidence is available that suggests that this more thinly traded market achieves a high degree of weak form efficiency. If the EMH is true, no systematic trading strategy will result in significantly abnormal returns. At the weak level this refers to strategies based on previous prices or price trends.
Basically, the efficient market hypothesis (EMH) states simply that it is impossible to consistently outperform the market on a risk-adjusted basis after transaction costs and taxes. It forms the basic benchmark of analysis in financial economics and can be described, less formally. The EMH has been extensively tested since the late 1960s, so much so that Michael Jensen (1978) has said that it may be the most extensively tested proposition in all the social sciences. In short, the EMH suggests that stock markets are ‘rationally’ priced. Fama and French (1988), among others, find that stock returns are somewhat predictable when analyzed over longer terms than the daily or weekly models often used to test the propositions of the EMH.
A related problem is the sensitivity of results to the choice of variables included in the empirical model. To illustrate, Davidson and Froyen (1982) report that their result supporting EMH based on the monthly New York Stock Exchange stock price index and M1 is overturned when their estimating equation includes the federal funds rate. Unfortunately, similar misspecification problems are found in many past studies. One pertinent issue examined in many previous studies is whether all available information is incorporated into current stock prices. In other words, do lags of economic variables have an important influence on current and future stock price movements? The proponents of EMH argue that stock prices respond only to unanticipated changes in macroeconomic variables (Davidson and Froyen, 1982; Pearce and Roley, 1983). Fama (1981) finds a significant expected inflation-stock return relationship, however, when the previous year’s growth rate in the monetary base is included in the regression.
Similarly, there are also controversies with regard to the effects of inflation on stock prices. Contrary to traditional belief, Fama and Schwert (1977) and Gultekin (1983) find that unexpected inflation and stock prices are negatively related. Geske and Roll (1983) claim that such a relationship is spurious (Coate and Vanderhoff, 1986) and provide evidence in favor of an expected inflation and stock price relationship.
It is in Fama’s model that the modern EMH, as illustrated in this portion of the chapter, has been among the primary indicators of market behavior. The study made by Fama basically associates between daily price changes for stocks in the Dow Jones Industrial Average (DJIA) over the time period January 1958 through September 1962. He examines interdependency by conducting runs tests and tests for serial correlation using various lags on log daily price relatives.
From these results Fama stresses that (5, pp. 73, 76, 80) the absolute size of the serial correlation coefficients is always quite small” and that “the percentage differences between the actual and expected number of runs are quite small. He concludes that there is no evidence of important dependence from either an investment or a statistical point of view. A number of other studies during this same time period reached similar conclusions when examining price dependency for daily security returns in other national markets or for different time intervals or different commodities in the United States market. Fama’s article remains the authoritative source on daily price dependency for security returns.
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