Common asset pricing factors in volatilities and returns in futures markets

Factor-based asset pricing models have been used to explain the common predictable variation in excess asset returns. This paper combines means with volatilities of returns in several futures markets to explain their common predictable variation. Using a latent variables methodology, tests do not reject a single factor model with a common time-varying factor loading. The single common factor accounts for up to 53% of the predictable variation in the volatilities and up to 14% of the predictable variation in the means. S&P500 futures volatility predicted by the factor model is highly correlated with volatility implied in S&P500 futures options. But both the factor and implied volatilities are significant in predicting future volatility. In derivatives pricing, both implied volatility from options and factors extracted from asset pricing models should be employed.     

Asymmetry in spillover effects: Evidence for international stock index futures markets

The paper investigates the asymmetry in return and volatility spillovers across futures markets with non-overlapping stock exchange trading hours. The transmission of positive and negative return and volatility shocks is analysed for 104 channels of information conveyance identified by combining 9 developed and 11 emerging markets in markets pairs with non-overlapping trading hours. The asymmetric causality test is employed to daily stock index futures returns and volatilities for the period from 03 October 2010 to 03 October 2014. The paper sheds light on the relatively little explored concept of asymmetry in return and volatility spillovers across markets, providing novel evidence on stabilizing and destabilizing spillover effects.     

Leverage Is a Double-Edged Sword

We use proprietary data on intraday transactions at a futures brokerage to analyze how implied leverage influences trading performance. Across all investors, leverage is negatively related to performance, due partly to increased trading costs and partly to forced liquidations resulting from margin calls. Defining skill out-of-sample, we find that relative performance differentials across unskilled and skilled investors persist. Unskilled investors' leverage amplifies losses from lottery preferences and the disposition effect. Leverage stimulates liquidity provision by skilled investors, and enhances returns. Although regulatory increases in required margins decrease skilled investors' returns, they enhance overall returns, and attenuate return volatility.     

An empirical investigation of large trader market manipulation in derivatives markets

Using account-level transaction data in options and futures markets, we investigate the existence of market manipulation, which is the ability of large traders to trade strategically, impacting prices and making abnormal profits. First, large trader’s option positions have a quantity impact on the underlying asset’s price. Second, large traders generate significantly positive alphas from trading options and futures. Among the different investor types, proprietary dealers generate the largest positive alphas. Third, these abnormal returns are consistent with strategic trading and cross-market manipulation. The evidence supports market manipulation across the options and futures markets, but not within the futures market itself.     

Return and volatility co-movement in commodity futures markets: the effects of liquidity risk

Commodity markets are a widely researched topic in the field of finance. In this paper, we investigate the co-movement of return and volatility measures in different commodity futures markets and how these measures are affected by liquidity risk. First, we find that commodity returns display co-movement and that liquidity risk plays a key role in shaping asset return patterns. Moreover, we show that the volatilities of commodity returns co-move, and we demonstrate the role of liquidity risk in this joint pattern. We also find that the commodity markets we investigated share a common volatility factor that determines their joint volatility co-movement. Because liquidity risk affects both commodity returns and volatility shocks, it might be interpreted as the common causal factor driving both measures simultaneously. Therefore, we affirm the view that liquidity shocks are firmly related to two residual risks originating from both market return and market volatility. Finally, we also show that liquidity spillovers can significantly drive cross-sectional correlation dynamics.     

Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices

We address the question whether the evolution of implied volatility can be forecasted by studying a number of European and US implied volatility indices. Both point and interval forecasts are formed by alternative model specifications. The statistical and economic significance of these forecasts is examined. The latter is assessed by trading strategies in the recently inaugurated CBOE volatility futures markets. Predictable patterns are detected from a statistical point of view. However, these are not economically significant since no abnormal profits can be attained. Hence, the hypothesis that the volatility futures markets are efficient cannot be rejected.     

Price dynamics in refined petroleum spot and futures markets

This article characterizes the spot and futures price dynamics of two important physical commodities, gasoline and heating oil. Using a non-linear error correction model with time-varying volatility, we demonstrate many new results. Specifically, the convergence of spot and futures prices is asymmetric, non-linear, and volatility inducing. Moreover, spreads between spot and futures prices explain virtually all spot return volatility innovations for these two commodities, and spot returns are more volatile when spot prices exceed futures prices than when the reverse is true. Furthermore, there are volatility spillovers from futures to spot markets (but not the reverse), futures volatility shocks are more persistent than spot volatility shocks, and the convergence of spot and futures prices is asymmetric and non-linear. These results have important implications. In particular, since the theory of storage implies that spreasd vary with fundamental supply and demand factors, the strong relation between spreads and volatility suggests that these fundamentals — rather than trading induced noise — are the primary determinants of spot price volatility. The volatility spillovers, differences in volatility persistence, and lead-lag relations are consistent with the view that the futures market is the primary locus of informed trading in refined petroleum product markets. Finally, our finding that error correction processes may be non-linear, asymmetric, and volatility inducing suggests that traditional approaches to the study of time series dynamics of variables that follow a common stochastic trend that ignore these complexities may be mis-specified.     

Impact of speculation and economic uncertainty on commodity markets

We examine the interactions between commodity futures returns and five driving factors (financial speculation, exchange rate, stock market dynamics, implied volatility for the US equity market, and economic policy uncertainty). Nonlinear causality tests are implemented after controlling for cointegration and conditional heteroscedasticity in the data over the period May 1990 – April 2014. Our results show strong evidence of unidirectional linear causality from commodity returns to excess speculation for the majority of the considered commodities, in particular for agriculture commodities. This evidence casts doubt on the claim that speculation is driving food prices. We also find unidirectional linear causality from energy futures markets to exchange rates and strong evidence of nonlinear causal dependence between commodity futures returns, on the one hand, and stock market returns and implied volatility, on the other hand. Overall, the new evidence found in this paper can be utilized for policy and investment decision-making.     

Intraday volatility in the stock index and stock index futures markets

We examine the intraday relationship between returns and returnsvolatility in the stock index and stock index futures markets.Our results indicate a strong intermarket dependence in thevolatility of the case and futures returns. Price innovationsthat originate in either the stock or futures markets can predictthe future volatility in the other market. We show that thisrelationship persists even during periods in which the dependencein the returns themselves appears to weaken. The findings arerobust to controlling for potential market frictions such asasynchronous trading in the stock index. Our results have implicationsfor understanding the pattern of information flows between thetwo markets.     

Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter?

Research has consistently found that implied volatility is a conditionally biased predictor of realized volatility across asset markets. This paper evaluates explanations for this bias in the market for options on foreign exchange futures. Several recently proposed solutions – including a model of priced volatility risk – fail to explain a significant portion of the conditional bias found in implied volatility. Further, while implied volatility fails to subsume econometric forecasts in encompassing regressions, these forecasts do not significantly improve delta-hedging performance. Thus this paper argues that statistical metrics are inappropriate measures of the information content of implied volatility. Implied volatility appears much more useful when measured by a more relevant, economic metric.     

The price discovery of day trading activities in futures market

Access to information is necessary for market transparency. However, contrary to trading volume and open interest, information related to day trading activities is rarely available. By incorporating unexplored day trading volume in the literature, this paper demonstrates that both the expected open interest and expected day trading volume are consistently and positively correlated with returns, but that one-lagged day trading volume is negatively correlated with futures returns. Meanwhile, both expected and unexpected day trading volume are negatively correlated with volatility, suggesting that arbitrage activities related to unexpected day trading volume may accelerate the movement of futures prices to a new equilibrium. Moreover, open interest provides liquidity but increases volatility. Finally, we strongly suggest that day trading transaction information be released by futures exchanges to achieve greater transparency.     

The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility

We examine the dynamic relation between returns, volume, and volatility of stock indexes. The data come from nine national markets and cover the period from 1973 to 2000. The results show a positive correlation between trading volume and the absolute value of the stock price change. Granger causality tests demonstrate that for some countries, returns cause volume and volume causes returns. Our results indicate that trading volume contributes some information to the returns process. The results also show persistence in volatility even after we incorporate contemporaneous and lagged volume effects. The results are robust across the nine national markets.     

Noise, equity prices, and hedging: A new approach

The existence of noise trading in equity markets has possible economic implications for arbitrage, and asset pricing. In terms of pricing, noise trading can lead to excess volatility which has been shown to influence the value of options and futures. Furthermore, option research shows that modeling volatility leads to improved hedging performance. To this end, we derive a general hedging model for equity index futures in the presence of noise trading. Our analysis shows how the level and dynamics of noise trading should influence a hedger's behavior. Finally, we empirically test our model using the NASDAQ-100 index futures and FTSE 100 index futures over the period of January 1998 to May 2003.     

Intra–day Behavior of Treasury Sector Index Option Implied Volatilities around Macroeconomic Announcements

If option implied volatility is an unbiased, efficient forecast of future return volatility in the underlying asset, then we should be able to predict its path around macroeconomic announcements from responses in cash markets. Regressions show that volatilities rise the afternoon before announcements that move cash markets, and that post–announcement volatilities return to normal as rapidly as cash prices do. Although implied volatilities are predictable, the Treasury options market is efficient since informed traders do not earn arbitrage profits once we account for trading costs.     

The calm after the storm: implied volatility and future stock index returns

This article explores the predictive power of five implied volatility indices for subsequent returns on the corresponding underlying stock indices from January 2000 through October 2013. Contrary to previous research, very low volatility levels appear to be followed by significantly positive average returns over the next 20, 40 or 60 trading days. Rolling trading simulations show that positive adjusted excess returns can be achieved when long positions in the stock indices are taken on days of very low implied volatility. This may be a hint that market inefficiencies exist in some markets, especially outside the USA. The excess returns measured against a buy and hold benchmark are significant for the German and Japanese market when tested with a bootstrap methodology. The results are robust against a broad spectrum of specifications.     

Trading behavior in S&P 500 index futures

This article examines the determinants of trading decisions and the performance of trader types, in the context of the E-Mini S&;P 500 futures and S&;P 500 futures markets. Speculators and small traders tend to follow positive feedback strategies while hedgers dynamically adjust positions in response to market returns. Such strategies apparently reverse during the 2008–09 financial crisis. Investor sentiment and market volatility play an important role in determining the net trading position of traders across the sample period. While all trader types are better at foreseeing market upturns, an out-of-sample test suggests that speculators and small traders have some predictive ability for short-term market returns.     

Time series momentum

We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for one to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities of speculators and hedgers, we find that speculators profit from time series momentum at the expense of hedgers.     

Futures-Trading Activity and Stock Price Volatility

We examine whether greater futures-trading activity (volume and open interest) is associated with greater equity volatility. We partition each trading activity series into expected and unexpected components, and document that while equity volatility covaries positively with unexpected futures-trading volume, it is negatively related to forecastable futures-trading activity. Further, though futures-trading activity is systematically related to the futures contract life cycle, we find no evidence of a relation between the futures life cycle and spot equity volatility. These findings are consistent with theories predicting that active futures markets enhance the liquidity and depth of the equity markets.     

Interday variations in volume,variance and participation of large speculators

We use data uniquely available from the Commodity Futures Trading Commission (CFTC) to document the intraweek trading patterns of large speculators in five futures markets. These markets include futures traded against the Standard and Poor's 500 stock index, Treasury Bonds, gold, corn, and soybeans. We also examine the influence of large speculator trades on the patterns of volume and volatility for the contracts in our sample. Though we detect the familiar U-shaped and inverted U-shaped patterns across weekdays for volatility and aggregate volume, the association between volume and volatility becomes stronger when we separate large speculator volume from volume associated with other traders. The coefficient on large speculator volume is much larger than the coefficient on other volume in these regressions. Compared with total volume, large speculator volume is greater on Mondays than on the other days of the week in all five markets.     

Differences in the Cost of Trade Execution Services on Floor-Based and Electronic Futures Markets

This paper provides new evidence on the impact of electronic trading on brokerage commissions by investigating a sample period that covers the period of transition from floor to electronic trading on the Sydney Futures Exchange. After controlling for liquidity, volatility and broker identity, the introduction of electronic trading remains to be associated with lower brokerage commissions relative to floor markets. The study also provides new evidence on brokerage commissions in futures markets finding that commission fees charged on futures trades average 0.002% of transaction value. This is up to 120 times smaller than the magnitude of brokerage fees charged in stock markets, and considerably lower than the magnitude of brokerage fees assumed for futures markets in previous research. Consistent with existing studies based on stock markets, commissions charged per contract decrease with order size reflecting economies of scale in the provision of brokerage services in futures markets. Commission rates are positively related to bid-ask spreads and price volatility, which proxy for the probability of execution error costs and execution difficulty, respectively. Finally, the identity of the broker is found to be a significant determinant of commissions reflecting different pricing schedules across brokers.