Index Futures Research Articles

Index volatility and the put-call ratio: a tale of three markets

This paper investigates the influence of the put-call-ratio (PCR) implied by the Shanghai Stock Exchange (SSE) 50 ETF option on the price discovery process of the SSE50 index, on both the spot and the futures markets. By constructing an asymmetric VARX-MGARCH model, this paper examines the relationship between the PCRs and SSE50 index (futures). Empirical results indicate an asymmetric V-shaped relationship between the PCRs and the conditional volatility of the stock index returns and the index futures returns. The conditional volatility increases as the PCRs deviate widely from the mean. This study suggests that the PCRs implemented in many trading practices may be misused, because there is no evidence that the PCRs and index returns are correlated. Instead, this research implies a different way of using them: to trade volatility.

Cost-benefit analysis of trading strategies in the stock index futures market

With the introduction of many derivatives into the capital market, including stock index futures, the trading strategies in financial markets have been gradually enriched. However, there is still no theoretical model that can determine whether these strategies are effective, what the risks are, and how costly the strategies are. We built an agent-based cross-market platform that includes five stocks and one stock index future, and constructed an evaluation system for stock index futures trading strategies. The evaluation system includes four dimensions: effectiveness, risk, occupation of capital, and impact cost. The results show that the informed strategy performs well in all aspects. The risk of the technical strategy is relatively higher than that of the other strategies. Moreover, occupation of capital and impact cost are both higher for the arbitrage strategy. Finally, the wealth of noise traders is almost lost.

Investors’ Preference and Regulatory Aspects in the Indian Derivatives Market

The Indian Stock market undoubtedly experienced tremendous growth in the trading activity in the last twenty-five years. The Derivatives segment of the market began its operations with the launch of Index Futures by the National Stock exchange of India (NSE) in the year 2000. The Derivatives, as a financial product, derives its value from an underlying asset and is primarily designed to reduce the risk which businesses and individuals face due to the fluctuations in the asset prices. It helps individuals and companies to undertake risky projects with high returns and use derivatives as a tool to hedge risk, which can potentially lead to wealth creation. The popular instruments of equity derivatives market are- Forward, Future, Option, and swaps. This paper presents the legal framework and regulatory aspects by the market regulator Securities and Exchange Board of India (SEBI) involving derivatives that is based on the recommendations of the L.C. Gupta Committee. SEBI laid its objectives in regulating derivative markets to ensure transparent trading environment, safety, and integrity.

Effects of Futures Trading Constraints on Volatility of Stock Market

Financial regulations and policies affect the financial market efficiency. Due to China's stock market experienced a serious slump from June to August in 2015, China Financial Futures Exchange (CFFEX) subsequently implements constraints on trading futures aiming to limit the speculation in futures market. To explore the impact of trading constraints on index futures on the volatility of stock market and focuses on the change in CSI300 volatility before and after restricting futures from 2015 to 2016. The trading constraints on futures help decrease the stock market’s price fluctuation and effectively calm down the stock market. The results suggest that the proper trading constraints may be necessary while the market is irrational and the findings provide insights for the investors and regulators.

The distribution of index futures realised volatility under seasonality and microstructure noise

Previous research documents that the distribution of realised volatility appears approximately log-normal. However, formal tests reject normality fairly convincingly, which may indicate intrinsic features in the intraday data series, namely, the presence of seasonal intraday patterns and microstructure noise. Because many models are based on a normality assumption, this must be verified in order to validate the results. We find departures from normality due to the seasonal and noise components of intraday data, such that, after controlling for both features, the volatility estimates follow a log-normal distribution. Our results reveal that failing to account for these market imperfections can have important implications for analyses of volatility transmission and for investment and hedging decisions.

High-Frequency Trading and Its Impact on Exogenous Liquidity Risk of China’s Stock Index Futures Market before and after Trading Restrictions

Since China’s first stock index futures, China Securities Index 300 (CSI300) stock index futures were published in 2010, and China’s stock index futures market is now in a period of rapid development and play a key role in price discovery. During 2014 to 2015, China’s stock index futures market fluctuated abnormally, and the overuse of high-frequency trading (HFT) strategies in the stock index futures market was blamed as the main reason of the abnormal volatility. To lower down market fluctuation, the regulatory institute then announced a series of trade restriction policy to prevent the overuse of HFT behaviour. However, until now, the impact of such trade restriction policy for HFT remains uncertain. To tackle this issue, based on minute-level HFT data from the CSI 300 index futures market, this paper aims to investigate the relationship between HFT and the exogenous liquidity risk and how HFT affects China’s stock index futures market on its liquidity using the liquidity-adjusted value at risk (LVaR) model. The findings indicate that HFT improves the return of the liquidity provider and reduces the exogenous liquidity risk significantly.

A cooperative inter-provincial model for energy conservation based on futures trading

With rapid development of global society, economic growth is increasingly depending on energy consumption. It will be urgent to explore an effective model to conserve energy while maximizing the socioeconomic benefits. To improve on China’s current territorial management model, we applied market-based means to cooperative energy conservation by constructing an energy conservation model based on futures trading. The model has three components: a function that defines the annual socioeconomic benefits for energy consumption; a method for dividing provinces into buyers and sellers of energy-saving index futures; and a cooperative optimal energy consumption model for the buyer or seller provinces. To demonstrate this approach, we performed an empirical analysis of electricity conservation for four Chinese provinces (Shanxi, Jiangsu, Sichuan, and Gansu). Compared with the current territorial management model, the cooperative model increased the overall socioeconomic benefits for electricity consumption by 18.7% and decreased the overall electricity consumption per unit of socioeconomic benefits by 15.7%. In addition, each province’s socioeconomic benefits from energy consumption improved and the electricity consumption per unit of socioeconomic benefits decreased. Our sensitivity analysis revealed that fluctuation of the futures price (from −50% to +50%) created only small differences in the socioeconomic benefits for all provinces.

Evolutionarily stable strategy analysis of Quantitative investment strategy

Based on Chinese Stock Index Future, this study focuses on two types of market price changes, trends and shake. How investors’ strategies evolve to influence the market from trend to shake or from shake to trend to obtain the “evolutionary stable strategy” (ESS) of the market is conducted. The results show that trend strategies are more favourable in most market environments, and find a single strategy cannot survive in the market, and trading frequency should be appropriately reduced to reduce transaction costs, so as to survive in the market for a long time.

Intertemporal price discovery between stock index futures and spot markets: New evidence from high‐frequency data

AbstractThis article utilizes high‐frequency 15‐s intraday data from September 2017 through to August 2018 to investigate price leadership dynamics between Kuala Lumpur index futures (FKLI) and its underlying spot market: FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBM KLCI) in Bursa Malaysia. Harnessing the explanatory powers of Wavelet analysis, we employ Maximal Overlap Discrete Wavelet Transform to evaluate interdependence between contemporaneous futures and spot returns. We observe that price discovery between futures and spot markets at granular level is a scale‐dependent phenomenon. Moreover, we record a counter‐intuitive but not unprecedented evidence of futures market lagging the spot market in price formation. This discrepancy approaches convergence in 1–8 min. Our findings constitute evidence against the efficient market hypothesis and hint at opportunities for statistical arbitrage by high‐frequency trading. The results from time‐frequency domain receive strong support from vector error correction robustness checks, though corroboration is less conclusive from DCC‐GARCH and Baba, Engle, Kraft, and Kroner‐GARCH results.

Stock index futures trading impact on spot price volatility. The CSI 300 studied with a TGARCH model

A TGARCH modeling is argued to be the optimal basis for investigating the impact of index futures trading on spot price variability. We discuss the CSI-300 index (China-Shanghai-Shenzhen-300-Stock Index) as a test case. The results prove that the introduction of CSI-300 index futures (CSI-300-IF) trading significantly reduces the volatility in the corresponding spot market. It is also found that there is a stationary equilibrium relationship between the CSI-300 spot and CSI-300-IF markets. A bidirectional Granger causality is also detected. “Finally”, it is deduced that spot prices are predicted with greater accuracy over a 3 or 4 lag day time span.

Impact of the Adjustment of Maximum Order Volume on Pricing Efficiency of Stock Index Futures in China

In April 2017, China Financial Futures Exchange adjusted the maximum order volume of single trading in stock index futures, and this paper conducts research on this event. Firstly, it analyzes the influence of the adjustment of maximum order volume on the characteristics of the limit order book with high-frequency data and the impact of ordering situation on the trading depth and volatility of each contract with panel data. Secondly, it takes high-frequency tick-by-tick data to explore the causal relationship between the ordering situation and the probability of informed trading and analyzes the impact of the event on the probability of informed trading. Finally, the dynamic factor analysis method is used to quantify the pricing efficiency based on the probability of informed trading and the characteristics of limit order book, and the influence of the event on the pricing efficiency of stock index futures market is discussed. The results show that the reduction of maximum order volume has different effects on dominant contracts and nondominant contracts of stock index futures. After the event, the overall trading volume of the market increased, where the trading volume of dominant contracts decreased and that of nondominant contracts increased. For dominant contracts, the depth, slope, and liquidity decrease, the spread increases, and the probability of informed trading decreases so that the pricing efficiency becomes worse, while the results of nondominant contracts are the opposite. For Chinese stock index futures market, the pricing efficiency is greatly reduced and the resource allocation capacity is weakened under the influence of the event. Therefore, the adjustment of maximum order volume is not conducive to the healthy development of the stock index futures market. It is suggested that the reduction of the maximum order volume is only implemented for nondominant contracts.

Analysis on block chain financial transaction under artificial neural network of deep learning

In order to conduct an in-depth study on financial transactions of block chain, the classical back propagation (BP) neural network based on the artificial neural network (ANN) model is selected, and its propagation mode, weight change, and learning process are analyzed. For the problem of slow convergence speed and local minimum value of BP neural network, based on the idea of deep learning, the initial value and training step are changed by auto-encoder and restricted Boltzmann machine, and the theory is analyzed. Taking the stock index futures trading in the block chain financial trading as an example, the stock price trading of stock index futures is studied using the two deep learning neural network models to predict the price changes. The results show that the auto-encoder, as an unsupervised learning system, performs better than the restricted Boltzmann machine in setting the initial weights and thresholds, with fewer iterations, faster convergence rate, and smaller convergence error. The results obtained by the auto-encoder can be used as initialization settings and data analysis. The prediction accuracy of the whole model is around 59%. When the transaction cost is not considered, the transaction can be conducted based on the prediction signal of the deep learning model. Therefore, deep learning neural network model can be applied to block chain financial transactions as a reference for financial transactions, which has a good practical significance for the development of this field.

Forecasting Stock Index Futures Intraday Returns: Functional Time Series Model

It is of great significance to forecast the intraday returns of stock index futures. As the data sampling frequency increases, the functional characteristics of data become more obvious. Based on the functional principal component analysis, the functional principal component score was predicted by BM, OLS, RR, PLS, and other methods, and the dynamic forecasting curve was reconstructed by the predicted value. The traditional forecasting methods mainly focus on “point” prediction, while the functional time series forecasting method can avoid the point forecasting limitation, and realize “line” prediction and dynamic forecasting, which is superior to the traditional analysis method. In this paper, the empirical analysis uses the 5-minute closing price data of the stock index futures contract (IF1812). The results show that the BM prediction method performed the best. In this paper, data are considered as a functional time series analysis object, and the interference caused by overnight information is removed so that it can better explore the intraday volatility law, which is conducive to further understanding of market microstructure.

Noise trading and stock market bubbles: what the derivatives market is telling us

PurposeThe authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that contemporaneous price deviations in the derivatives market are statistically significant in explaining movements in index futures prices and option-market volatility measures.Design/methodology/approachTo understand the impact noise may have in the S&P 500 derivatives market, the authors first measure and evaluate the influence noise exerts on futures prices and then investigate its influence on option volatility.FindingsIn the period from 1996 to 2003, this study finds significant changes in the volatility and mean reversion in the noise level and a significant increase in its relation to implied volatility in option prices. The results are consistent with a bubble in technology stocks that occurred with significant increases in noise trading.Research limitations/implicationsThis study provides estimates for this model during the periods preceding and during the technology bubble. The study analysis shows that the volatility and mean reversion in the noise level are much stronger during the bubble period. Furthermore, the relation between noise trading and implied volatility in the futures market was of a significantly larger magnitude during this period. The study results support the importance of noise trading in market bubbles.Practical implicationsBloomfield, O'Hara and Saar (2009) find that noise traders lower bid–ask spreads and improve liquidity through increases in trading volume and market depth. Such improved market conditions could have positive effects on market quality, and this impact could be evidenced by lower implied volatility when noise traders are more active. Indeed, the results in this study indicate that the level and characteristics of noise trading are fundamentally different during the technology bubble, and this noise trading activity has a larger impact during this period on implied volatility in the options market.Originality/valueThis paper uniquely analyzes derivatives on the S&P 500 Index in order to detect the presence and influence of noise traders. The authors derive and implement a two-factor jump diffusion noise model. In their model, noise rectifies the difference of analysts' opinions, market information and beliefs among traders. By incorporating a reduced-form temporal expression of heterogeneities among traders, the model is rich enough to capture salient time-series characteristics of equity prices (i.e. stochastic volatility and jumps). A singular feature of the authors’ model is that stochastic volatility represents the random movements in asset prices that are attributed to nonmarket fundamentals.

Is Overnight Volatility Overlooked?

The equity index futures, traded almost 24 hours, reveal the overnight price process. We use both daytime and overnight high-frequency futures price data to estimate the realized volatility (RV) of the S&P 500 and NASDAQ 100 indexes. We find that more than 40% of daily volatility is overnight volatility, which is omitted by many previous works. We demonstrate strong predictive power of overnight RV on daytime RV and vice versa. By incorporating overnight RV, our best-proposed model reduces the forecasting mean squared error of the S&P 500 daytime RV by 27.8% compared to the benchmark model. Based on the inter-correlation between daytime and overnight volatility, we propose a novel Day-Night Realized Stochastic Volatility (DN-SV-RV) model, where the daytime and overnight returns are jointly modeled with their RVs, and their latent volatilities are correlated. The proposed DN-SV-RV model has the best out-of-sample return density forecasting among benchmark models. Under this innovative framework, the model estimation shows that daytime and overnight volatility complement each other, and volatility clustering is persistent during and after regular trading hours. Finally, by jointly estimating daily close-to-close return and realized volatility measures, we show that the daily RV, estimated from the almost 24-hour traded futures, is an accurate measure of daily return volatility by effectively measuring the overnight volatility, compared to daytime RV and squared overnight returns.

Deep learning and time series-to-image encoding for financial forecasting

In the last decade, market financial forecasting has attracted high interests amongst the researchers in pattern recognition. Usually, the data used for analysing the market, and then gamble on its future trend, are provided as time series; this aspect, along with the high fluctuation of this kind of data, cuts out the use of very efficient classification tools, very popular in the state of the art, like the well known convolutional neural networks (CNNs) models such as Inception, ResNet, AlexNet, and so on. This forces the researchers to train new tools from scratch. Such operations could be very time consuming. This paper exploits an ensemble of CNNs, trained over Gramian angular fields (GAF) images, generated from time series related to the Standard & Poor's 500 index future; the aim is the prediction of the future trend of the U.S. market. A multi-resolution imaging approach is used to feed each CNN, enabling the analysis of different time intervals for a single observation. A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach. Our method outperforms the buyand-hold (B&H) strategy in a time frame where the latter provides excellent returns. Both quantitative and qualitative results are provided.

A Study of Efficiency of Index Futures, Lead-Lag Relationship, and Speed of Adjustments in India using High-Frequency Data

We conducted a rigorous analysis to find out the speed of adjustments in futures and spot indices on NSE NIFTY 50 in short-run as a part of examining the efficiency of the financial futures market in India. Towards that, we undertook the analysis of long-run and short-run efficiencies separately and used Engle-Granger’s error correction mechanism (ECM) so that a clear picture of short-run efficiency in terms of speed of adjustments could emerge. The rigour manifested in the analysis of up to 412538 data points that bred from the choice of five different time intervals spanning from 1-minute to 120-minutes. Most of the price discovery took place in the futures market, and the spot market followed it mostly with a lag of 9 minutes. However, it took 35 minutes to completely return to the desired relationship once a drift had taken place. The increase in the speed of adjustments, as compared to the speed documented in previous studies, could be attributed to the large-scale adoption of the high-frequency (i.e. algorithmic) trading in recent times. Our findings suggested that traders can effectively use the near month contract of Nifty 50 Futures to hedge their open positions in the index or any other stock. At the same time, the market also offers an opportunity to arbitrageurs as the integration did not take place before 9 minutes.

Do futures lead the index under stress? Evidence from the 2015 Chinese market turmoil and its aftermath

Do index futures still lead the spot market over government intervention or under trading restrictions? This intriguing question can be explored in the context of the 2015 Chinese market turmoil with frequent government interventions and its aftermath when unprecedentedly severe restrictions were imposed on the trading of the CSI 300 index futures. The intraday price discovery, studied via VECM, became weakly bidirectional over the boom phase from October 2014 to June 2015, in contract to the index futures’ strong leading before the boom. The futures’ leading status weakened drastically over the crash phase between June and September 2015, probably due to the government’s interference with the stock market. Surprisingly, the futures under severe trading restrictions after the crash from September 2015 to June 2016 still led the spot weakly, and the volatility spillover to the index was somewhat significant. Policywise, the empirical findings imply that government interventions or trading restrictions weaken the functioning of markets.

Intraday sentiment and market returns

We investigate intraday sentiment factor and its relation to near-term returns in both the stock market and stock index futures market. For study near-term sentiment effect, we construct stock index futures sentiment and stock sentiment at 5-minutes frequency. In each market system, we show sentiment variables are a strong positive predictor of subsequent stock market returns and stock index futures returns in 5-minutes horizons. In the two market system, the evidence suggests that 1) sentiment variables have a spillover effect that they affect relevant other market’s future returns significantly and 2) sentiment variables have contagion effect that they affect relevant other market’s future sentiment significantly. Finally, we show an out-of-sample forecasting test and confirm the sentiment variables have powerful predictability to returns in short-term.

Investor sentiment‐styled index in index futures market

AbstractIn this paper, we define and analyze the sentiment‐styled index for the CSI 300 index futures in the Chinese futures market. Our sentiment‐styled index for the CSI 300 index futures from April 16, 2010 to April 30, 2019 is constructed by the first and second principal component analyses, rather than only by the first principal component analysis used in the Baker and Wurgler (Journal of Finance 61(4): 1645–1680, 2006) method. The sentiment‐styled index explains 78.38% of the sample variance. The vector error correction model is adapted to study the dynamics of cointegration of the sentiment‐styled index and the logarithmic futures price. We use the GARCH‐DCC model to illustrate the spillover effect between the sentiment‐styled index and the Chinese futures market. We show that this investor sentiment‐styled index does have the price discovery from the Granger causality and common factor weights and the hedging function from the Baba–Engle–Kraft–Kroner model empirically; furthermore, we use the curvature term of the sentiment‐styled index to determine the multiple unit roots. More empirical results for the sentiment‐styled index of the Chinese stock market, the sentiment‐styled index of the CSI 300 index futures, and the return of the CSI 300 index futures market are studied in this paper.