scholarly journals Optimal hedging under departures from the cost-of-carry valuation: Evidence from the Spanish stock index futures market

2003 ◽  
Vol 27 (6) ◽  
pp. 1053-1078 ◽  
Author(s):  
Juan A Lafuente ◽  
Alfonso Novales
Keyword(s):  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Optimal Hedging ◽  
Cost Of Carry ◽  
The Cost

Price Expectation and the Pricing of Stock Index Futures: Evidence from Developed and Emerging Markets

2006 ◽  
Vol 09 (04) ◽  
pp. 639-660 ◽  
Author(s):  
Janchung Wang ◽  
Hsinan Hsu
Keyword(s):  
Emerging Markets ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Price Expectation ◽  
Index Futures ◽  
Cost Of Carry ◽  
The Cost ◽  
Better Than

This study examines how well the pricing model of Hsu and Wang (2004) explains the behavior of stock index futures prices for the developed markets (such as the S&P 500 index futures market) and the emerging markets (such as the Taiwan Futures Exchange (TAIFEX) Taiwan stock index futures market). It also compares the pricing performance of three alternative pricing models of stock index futures: the cost of carry model, the Hemler and Longstaff (1991) model, and the Hsu–Wang model. Overall, the Hsu–Wang model provides significantly better pricing performance than that of the cost of carry model in emerging markets with high degrees of imperfection. Moreover, this study also observes that the Hemler and Longstaff (1991) model performs better than the cost of carry model in estimating prices of the TAIFEX futures, suggesting that the incorporation of stochastic market volatility is beneficial to predict the TAIFEX futures prices.

The emotional cost-of-carry: Chinese investor sentiment and equity index futures basis

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Song Cao ◽  
Ziran Li ◽  
Kees G. Koedijk ◽  
Xiang Gao
Keyword(s):  
Investor Sentiment ◽  
Stock Index ◽  
Stock Index Futures ◽  
Chinese Stock Market ◽  
Content Type ◽  
Index Futures ◽  
Market Participants ◽  
Cost Of Carry ◽  
The Cost ◽  
Equity Index

PurposeWhile the classic futures pricing tool works well for capital markets that are less affected by sentiment, it needs further modification in China's case as retail investors constitute a large portion of the Chinese stock market participants. Their expectations of the rate of return are prone to emotional swings. This paper, therefore, explores the role of investor sentiment in explaining futures basis changes via the channel of implied discount rates.Design/methodology/approachUsing Chinese equity market data from 2010 to 2019, the authors augment the cost-of-carry model for pricing stock index futures by incorporating the investor sentiment factor. This design allows us to estimate the basis in a better way that reflects the relationship between the underlying index price and its futures price.FindingsThe authors find strong evidence that the measure of Chinese investor sentiment drives the abnormal fluctuations in the basis of China's stock index futures. Moreover, this driving force turns out to be much less prominent for large-cap stocks, liquid contracting frequencies, regulatory loosening periods and mature markets, further verifying the sentiment argument for basis mispricing.Originality/valueThis study contributes to the literature by relying on investor sentiment measures to explain the persistent discount anomaly of index futures basis in China. This finding is of great importance for Chinese investors with the intention to implement arbitrage, hedging and speculation strategies.

scholarly journals The Impact of Underlying Market Closure on Futures Market Liquidity: Evidence from China

2012 ◽  
Author(s):  
Wang Chun Wei ◽  
Alex Frino
Keyword(s):  
Futures Market ◽  
Market Liquidity ◽  
Stock Index ◽  
Stock Index Futures ◽  
Contagion Effect ◽  
Index Futures ◽  
Empirical Results ◽  
Open Session ◽  
The Impact ◽  
Closure Theory

This study investigates the trading activity of Chinese stock index futures, recently introduced at the open and close of the underlying trading. We document the impact of the underlying spot on the futures market liquidity as well as volatility as discussed in earlier works on market closure theory. Our empirical results support previous literature on the impact of the underlying, particularly during the open session, as a contagion effect, which is clearly at play. We find significant U-shaped patterns in liquidity factors and intraday volatility during open and close trades in the morning.  

The nonlinear characteristics of Chinese stock index futures yield volatility

2019 ◽  
Vol 10 (2) ◽  
pp. 175-196 ◽  
Author(s):  
Xuebiao Wang ◽  
Xi Wang ◽  
Bo Li ◽  
Zhiqi Bai
Keyword(s):  
High Frequency ◽  
Futures Market ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Short Term ◽  
Content Type ◽  
Index Futures ◽  
Basic Characteristics

Purpose The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory. Design/methodology/approach This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry. Findings This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased. Research limitations/implications This paper has research limitations in variable measurement and data selection. Practical implications This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration. Originality/value The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Conghua Wen ◽  
Fei Jia ◽  
Jianli Hao
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Trading Behavior ◽  
Volatility Forecasting ◽  
Content Type ◽  
Index Futures ◽  
Intraday Data

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

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