Stock market margin requirements and volatility: Implications for regulation of stock index futures

This article reexamines the evidence on the relationship between stock market margin buying and volatility, and discusses the implications for the regulation of futures markets margin requirements. Post-war data provide no evidence of a link between the initial margin requirements set by the Federal Reserve and stock market volatility. Over the entire period in which the Federal Reserve has set margin requirements (1934-present), there is a correlation between margin requirements and margin debt on the one hand and volatility on the other. However, margin debt is not primarily associated with downside volatility and margin requirements are not primarily associated with upside volatility, as would be expected if margin buying were the cause of the volatility. Thus, the experience with stock market margin requirements provides no support for regulating futures markets margins in order to curb volatility. While this evidence does not rule out the possibility that margin buying contributed to the speculative boom of the 1920s and the 1929 crash, margin debt represented a much greater fraction of the 1929 stock market than have stock market futures in the 1980s. Even taking the experience of the 1920s into account, therefore, there is still no justification for regulating futures margins in order to curb volatility.This article was prepared for the Columbia Center for the Study of Futures Markets Conference on Regulatory and Structural Reform of Stock and Futures Markets.     

The Differential Impact of Federal Reserve Margin Requirements on Stock Return Volatility

This study examines the effect of changes in margin requirements on stock price volatility. We examine the possibility that the impact of margin requirements varies with a stock's degree of speculative interest. Using four alternative measures of speculative interest, we divide our sample into ten portfolios. We find no consistent evidence of a relationship between margin requirements and changes in volatility for any portfolio. The inconsistent and often contradictory results produced by these changes question its usefulness by Federal Reserve decision makers.     

Margin Requirements, Volatility, and Market Integrity: What Have We Learned Since the Crash?

This study assesses the state of the policy debate that surrounds the federal regulation of margin requirements. A review of the literature finds no undisputed evidence that supports the hypothesis that margin requirements can be used to control stock return volatility and correspondingly little evidence that suggests that margin-related leverage is an important underlying source of excess volatility. The evidence does not support the hypothesis that there is a stable inverse relationship between the level of Regulation T margin requirements and stock returns volatility nor does it support the hypothesis that the leverage advantage in equity derivative products is a source of additional returns volatility in the stock market.     

Margin requirements and stock volatility

Since 1934 the Federal Reserve Board has had the power to set separate limits on the amount of credit that can be extended to purchasers of common stock. There has been much recent debate about the efficacy of these margin regulations. This article argues that the Fed has responded to increases in stock prices by raising margin requirements. The increase in prices has been associated with a decrease in volatility. There is no evidence that changes in margin requirements reduce subsequent stock return volatility. Also, trading halts have not had much effect on volatility in the past. Trading halts that were associated with banking panics were associated with high stock return volatility, but halts without bank panics were not associated with high levels of volatility.This article summarizes discussion that was presented at the Columbia Center for the Study of Futures Markets Conference on Regulatory Reform of Stock and Futures Markets, May 12, 1989.     

Volatility components, leverage effects, and the return-volatility relations

This paper investigates the risk-return trade-off by taking into account the model specification problem. Market volatility is modeled to have two components, one due to the diffusion risk and the other due to the jump risk. The model implies Merton’s ICAPM in the absence of leverage effects, whereas the return-volatility relations are determined by interactions between risk premia and leverage effects in the presence of leverage effects. Empirically, I find a robust negative relationship between the expected excess return and the jump volatility and a robust negative relationship between the expected excess return and the unexpected diffusion volatility. The latter provides an indirect evidence of the positive relationship between the expected excess return and the diffusion volatility.     

Margin Regulation and Stock Market Volatility

Using daily and monthly stock returns we find no convincing evidence that Federal Reserve margin requirements have served to dampen stock market volatility. The contrary conclusion, expressed in recent papers by Hardouvelis (1988a , b ), is traced to flaws in his test design. We do detect the expected negative relation between margin requirements and the amount of margin credit outstanding. We also confirm the recent finding by Schwert (1988) that changes in margin requirements by the Fed have tended to follow rather than lead changes in market volatility.     

Asset Pricing with Dynamic Margin Constraints

This paper provides a novel theoretical analysis of how endogenous time‐varying margin requirements affect capital market equilibrium. I find that margin requirements, when there are no other market frictions, reduce the volatility and correlation of returns as well as the risk‐free rate, but increase the market price of risk, the risk premium, and the price of risky assets. Furthermore, margin requirements generate a strong cross‐sectional dispersion of stock return volatilities. The results emphasize that a general equilibrium analysis may reverse the conclusions of a partial equilibrium analysis often employed in the literature.     

Idiosyncratic risk does not matter: A re-examination of the relationship between average returns and average volatilities

A recent study by Goyal and Santa-Clara [J. Finance 58 (2003) 975] finds a significantly positive relationship between average stock returns and pre-determined average return volatility measures, while finding no relationship between the average return and its own volatility. The result is interpreted as evidence that idiosyncratic risk matters in asset pricing. We re-examine the issue in extended sample periods and find the proclaimed positive relationship is not substantiated. Our analysis indicates that the above-mentioned positive relationship is mainly driven by the data in the 1990s. The trading strategy suggested by Goyal and Santa-Clara to exploit the return predictability by pre-determined volatility does not yield sustained economic gains.     

On the risk return relationship

While the risk return trade-off theory suggests a positive relationship between the expected return and the conditional volatility, the volatility feedback theory implies a channel that allows the conditional volatility to negatively affect the expected return. We examine the effects of the risk return trade-off and the volatility feedback in a model where both the return and its volatility are influenced by news arrivals. Our empirical analysis shows that the two effects have approximately the same size with opposite signs for the daily excess returns of seven major developed markets. For the same data set, we also find that a linear relationship between the expected return and the conditional standard deviation is preferable to polynomial-type nonlinear specifications. Our results have a potential to explain some of the mixed findings documented by previous studies.     

An inverted U‐shaped crude oil price return‐implied volatility relationship

We examined the return–volatility relationship for USO ETF oil price return and CBOE Crude Oil ETF Volatility Index, OVX. The data for the USO and OVX covers the period covering May 11, 2007 to February 28, 2013. Our OLS regression results suggest evidence of regular feedback and leverage effects. When we employ linear quantile regression techniques, we find evidence of regular and inverse feedback effects. The inverse feedback effects being noticeable in the upper quantile region of the oil return distribution. There is also support for a regular leverage effect in USO prices. We also examined the return–volatility relationship using quantile regression copula methods for measuring the degree of asymmetry in the relationships between the oil price return and implied volatility. The results of the analysis indicate, first, that there exists a negative relationship between contemporaneous oil VIX and USO ETF oil returns. Second, that the relationship between oil returns and implied volatilities depends on the quartile at which the relationship is being investigated. Third, there exists an inverted U-shaped dependency relationship between returns and implied volatilities across quantiles. Fourth, though an inverted U-shape exists, the shape is different from those observed in stock markets.     

The relationship between stock returns and volatility in international stock markets

This study examines the relationship between expected stock returns and volatility in the 12 largest international stock markets during January 1980 to December 2001. Consistent with most previous studies, we find a positive but insignificant relationship during the sample period for the majority of the markets based on parametric EGARCH-M models. However, using a flexible semiparametric specification of conditional variance, we find evidence of a significant negative relationship between expected returns and volatility in 6 out of the 12 markets. The results lend some support to the recent claim [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42; Whitelaw, R., 2000. Stock market risk and return: an empirical equilibrium approach. Review of Financial Studies 13, 521–547] that stock market returns are negatively correlated with stock market volatility.     

Are idiosyncratic volatility and MAX priced in the Canadian market?

The negative relationship between realized idiosyncratic volatility (RIvol) and future returns uncovered by Ang et al. (2006) for the U.S. market has been attributed to return reversals. For the Canadian market where return reversals are considerably less important, we find that RIvol is positively related to future returns, even after controlling for risk loadings, illiquidity and reversals. Unlike the findings of Bali et al. (2001) for the U.S. market, we find that the relationship between extreme positive returns (MAX) and future returns for the Canadian market is positive and that idiosyncratic volatility continues to be consistently positively related to future returns after controlling for MAX. We find evidence that suggests that reversals for stocks with extreme daily returns are confined to (typically small) stocks with low institutional holdings.     

Initial margin policy and stochastic volatility in the crude oil futures market

This article examines the relationship between the volatilityof the crude oil futures market and changes in initial marginrequirements. To closely match changes in futures market volatilitywith the corresponding changes in margin requirements, we inferthe volatility of the futures market from the prices of crudeoil futures options contracts. Using a mean-reverting diffusionprocess for volatility, we show that changes in margin policydo not affect subsequent market volatility.     

Portfolio Returns,Market Volatility,and Seasonality

This paper examines three important issues related to the relationship between stock returns and volatility. First, are Duffee's (1995) findings of the relationship between individual stock returns and volatility valid at the portfolio level? Second, is there a seasonality of the market return volatility? Lastly, do size portfolio returns react symmetrically to the market volatility during business cycles? We find that the market volatility exhibits strong autocorrelation and small size portfolio returns exhibit seasonality. However, this phenomenon is not present in large size portfolios. For the entire sample period of 1962–1995, the highest average monthly volatility occurred in October, followed by November, and then January. Examining the two sub-sample periods, we find that the average market volatility increases by 15.4% in the second sample period of 1980–1995 compared to the first sample period of 1962–1979. During the contraction period, the average market volatility is 60.9% higher than that during the expansion period. Using a binary regression model, we find that size portfolio returns react asymmetrically with the market volatility during business cycles. This paper documents a strongly negative contemporaneous relationship between the size portfolio returns and the market volatility that is consistent with the previous findings at the aggregate level, but is inconsistent with the findings at the individual firm level. In contrast with the previous findings, however, we find an ambiguous relationship between the percentage change in the market volatility and the contemporaneous stock portfolio returns. This ambiguity is attributed to strongly negative contemporaneous and one-month ahead relationships between the market volatility and portfolio returns.

     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.     

The Determinants of Asymmetric Volatility

Volatility in equity markets is asymmetric: contemporaneousreturn and conditional return volatility are negatively correlated.In this article I develop an asymmetric volatility model wheredividend growth and dividend volatility are the two state variablesof the economy. The model allows both the leverage effect andthe volatility feedback effect, the two popular explanationsof asymmetry. The model is estimated by the simulated methodof moments. I find that both the leverage effect and volatilityfeedback are important determinants of asymmetric volatility,and volatility feedback is significant both statistically andeconomically.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

Fundamentals or Managerial Discretion? The Relationship between Accrual Variability and Future Stock Return Volatility

This study extends the theoretical framework of Callen and Segal (2004) and Vuolteenaho (2002) to investigate the association between accrual variability and firm‐level stock return volatility. The empirical evidence supports our prediction that increased uncertainty in current‐period accounting accruals is associated with significantly higher volatility of future stock returns, and the results are valid for measures of both systematic and idiosyncratic volatility. When accrual variability is decomposed into fundamental and discretionary portions, we find that the positive relationship between accrual variability and future stock return volatility is dominated by the fundamental component of accrual variability. Overall, our results suggest that uncertainty reflected in accrual information is subsequently reflected in the fluctuation of future stock returns, and that the predictive content in accruals primarily reflects firms' fundamental uncertainty, rather than any effects of managerial choices and interventions in the accounting process.     

Modelling return and conditional volatility exposures in global stock markets

This article empirically investigates the exposure of country-level conditional stock return volatilities to conditional global stock return volatility. It provides evidence that conditional stock market return volatilities have a contemporaneous association with global return volatilities. While all the countries included in the study exhibited a significant and positive relationship to global volatility, emerging market volatility exposures were considerably higher than developed market exposures. JEL Classification G12