A stochastic process approach in setting the appropriate margin level for the TAIFEX stock index futures

2006 ◽  
Vol 32 (11) ◽  
pp. 886-902
Author(s):  
Jian‐Hsin Chou ◽  
Hong‐Fwu Yu
Keyword(s):  
Brownian Motion ◽  
Value Theory ◽  
Stock Index ◽  
Stock Index Futures ◽  
Brownian Motion Process ◽  
Content Type ◽  
Index Futures ◽  
Natural Logarithm ◽  
Motion Process ◽  
Set Up

PurposeThe main purpose of this paper is to compute the appropriate margin level for the stock index futures traded on the Taiwan Futures Exchange (TAIFEX) and, then, to examine the appropriateness of the real margin requirement set by the TAIFEX.Design/methodology/approachThis paper develops a new approach assuming the future's prices follow a geometric Brownian motion process. Compared with the extreme value theory that has been intensively used to determine the appropriate futures margin levels, one of the advantages of the present model is no need to specify the frequency at which extremes are taken.FindingsThe evidences indicate that the theoretical margins obtained by the proposed model can provide a more accurate and flexible margin level in accordance with the market volatility.Research limitations/implicationsThe main limitation of this approach is that the natural logarithm of the futures prices is assumed to follow a Brownian motion process. However, such an assumption might not be practical for financial returns.Practical implicationsThe research is helpful for the clearinghouse to set up its margins policy, especially under various conditions of volatility risks.Originality/valueThis paper proposes a theoretical procedure to set an appropriate futures margin for the TAIFEX. This paper also provides a better understanding of Taiwan's futures market that is newly launched and is useful for investors to hedge and speculate.

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.

Are Chinese market-neutral strategy hedge funds really market neutral?

2018 ◽  
Vol 8 (1) ◽  
pp. 21-42 ◽  
Author(s):  
Xucheng Huang ◽  
Jie Sun
Keyword(s):  
Hedge Funds ◽  
Factor Model ◽  
Reference Value ◽  
Stock Index ◽  
Stock Index Futures ◽  
Chinese Market ◽  
Content Type ◽  
Index Futures ◽  
Market Risks ◽  
Risk Characteristics

Purpose The purpose of this paper is to empirically analyze the “market-neutral” characteristics of the market-neutral strategy hedge funds in Chinese A-share market. Design/methodology/approach The analyses in the paper are conducted to study the market-neutral characteristics by means of index analysis, correlation analysis, β-neutral analysis and the three-factor model analysis. Findings The results show that the performance advantage of the market-neutral strategy hedge funds is obvious. Most market-neutral strategy funds are exposed to market risks and the α strategy funds also have obvious style factor exposure; strictly speaking, all of the market-neutral strategies have not reached the “market-neutral” requirements. This paper also finds that Chinese trading restrictions on stock index futures in September 2015 have a significant impact on Chinese market-neutral strategy hedge funds. Originality/value The conclusion of this paper has a certain reference value for understanding the risk characteristics and possible problems of hedge funds in emerging markets, and also has important reference value for investors.

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.

Volatility behaviour of stock index futures in China: a bivariate GARCH approach

2015 ◽  
Vol 32 (1) ◽  
pp. 128-154 ◽  
Author(s):  
Yang Hou ◽  
Steven Li
Keyword(s):  
Trading Volume ◽  
Futures Market ◽  
Stock Index ◽  
Garch Models ◽  
Stock Index Futures ◽  
Informational Efficiency ◽  
Conditional Correlation ◽  
Content Type ◽  
Index Futures ◽  
Volatility Transmission

Purpose – This paper aims to investigate the volatility transmission and dynamics in China Securities Index (CSI) 300 index futures market. Design/methodology/approach – This paper applies the bivariate Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC) Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models using high frequency data. Estimates for the bivariate GARCH models are obtained by maximising the log-likelihood of the probability density function of a conditional Student’s t distribution. Findings – This empirical analysis yields a few interesting results: there is a one-way feedback of volatility transmission from the CSI 300 index futures to spot returns, suggesting index futures market leads the spot market; volatility response to past bad news is asymmetric for both markets; volatility can be intensified by the disequilibrium between spot and futures prices; and trading volume has significant impact on volatility for both markets. These results reveal new evidence on the informational efficiency of the CSI 300 index futures market compared to earlier studies. Originality/value – This paper shows that the CSI 300 index futures market has improved in terms of price discovery one year after its existence compared to its early days. This is an important finding for market participants and regulators. Further, this study considers the volatility response to news, market disequilibrium and trading volume. The findings are thus useful for financial risk management.

scholarly journals Identifying the major reversals of the BIST-30 index by extreme outliers

2017 ◽  
Vol 1 (1) ◽  
pp. 74-88
Author(s):  
Ümit Erol
Keyword(s):  
Rate Of Change ◽  
Stock Index ◽  
Practical Implementation ◽  
Stock Index Futures ◽  
Content Type ◽  
Index Futures ◽  
Fund Managers ◽  
Level 3 ◽  
Extreme Outlier ◽  
Practical Implications

Purpose The purpose of this paper is to show that major reversals of an index (specifically BIST-30 index) can be detected uniquely on the date of reversal by checking the extreme outliers in the rate of change series using daily closing prices. Design/methodology/approach The extreme outliers are determined by checking if either the rate of change series or the volatility of the rate of change series displays more than two standard deviations on the date of reversal. Furthermore; wavelet analysis is also utilized for this purpose by checking the extreme outlier characteristics of the A1 (approximation level 1) and D3 (detail level 3) wavelet components. Findings Paper investigates ten major reversals of BIST-30 index during a five year period. It conclusively shows that all these major reversals are characterized by extreme outliers mentioned above. The paper also checks if these major reversals are unique in the sense of being observed only on the date of reversal but not before. The empirical results confirm the uniqueness. The paper also demonstrates empirically the fact that extreme outliers are associated only with major reversals but not minor ones. Practical implications The results are important for fund managers for whom the timely identification of the initial phase of a major bullish or bearish trend is crucial. Such timely identification of the major reversals is also important for the hedging applications since a major issue in the practical implementation of the stock index futures as a hedging instrument is the correct timing of derivatives positions. Originality/value To the best of the author’ knowledge; this is the first study dealing with the issue of major reversal identification. This is evidently so for the BIST-30 index and the use of extreme outliers for this purpose is also a novelty in the sense that neither the use of rate of change extremity nor the use of wavelet decomposition for this purpose was addressed before in the international literature.

A new approach to forecast market interest rates through the CIR model

2019 ◽  
Vol 37 (2) ◽  
pp. 267-292 ◽  
Author(s):  
Giuseppe Orlando ◽  
Rosa Maria Mininni ◽  
Michele Bufalo
Keyword(s):  
Brownian Motion ◽  
Interest Rates ◽  
Arima Model ◽  
Standard Brownian Motion ◽  
Brownian Motion Process ◽  
Cir Model ◽  
Market Data ◽  
Content Type ◽  
Standardized Residuals ◽  
Motion Process

Purpose The purpose of this study is to suggest a new framework that we call the CIR#, which allows forecasting interest rates from observed financial market data even when rates are negative. In doing so, we have the objective is to maintain the market volatility structure as well as the analytical tractability of the original CIR model. Design/methodology/approach The novelty of the proposed methodology consists in using the CIR model to forecast the evolution of interest rates by an appropriate partitioning of the data sample and calibration. The latter is performed by replacing the standard Brownian motion process in the random term of the model with normally distributed standardized residuals of the “optimal” autoregressive integrated moving average (ARIMA) model. Findings The suggested model is quite powerful for the following reasons. First, the historical market data sample is partitioned into sub-groups to capture all the statistically significant changes of variance in the interest rates. An appropriate translation of market rates to positive values was included in the procedure to overcome the issue of negative/near-to-zero values. Second, this study has introduced a new way of calibrating the CIR model parameters to each sub-group partitioning the actual historical data. The standard Brownian motion process in the random part of the model is replaced with normally distributed standardized residuals of the “optimal” ARIMA model suitably chosen for each sub-group. As a result, exact CIR fitted values to the observed market data are calculated and the computational cost of the numerical procedure is considerably reduced. Third, this work shows that the CIR model is efficient and able to follow very closely the structure of market interest rates (especially for short maturities that, notoriously, are very difficult to handle) and to predict future interest rates better than the original CIR model. As a measure of goodness of fit, this study obtained high values of the statistics R2 and small values of the root of the mean square error for each sub-group and the entire data sample. Research limitations/implications A limitation is related to the specific dataset as we are examining the period around the 2008 financial crisis for about 5 years and by using monthly data. Future research will show the predictive power of the model by extending the dataset in terms of frequency and size. Practical implications Improved ability to model/forecast interest rates. Originality/value The original value consists in turning the CIR from modeling instantaneous spot rates to forecasting any rate of the yield curve.

The spillover effect between CSI 500 index futures market and the spot market

2017 ◽  
Vol 7 (2) ◽  
pp. 249-272 ◽  
Author(s):  
Xuejun Fan ◽  
De Du
Keyword(s):  
Stock Market ◽  
Spillover Effect ◽  
Spot Market ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Chinese Stock Market ◽  
Stock Market Crash ◽  
Content Type ◽  
Index Futures

Purpose Focusing on the spillover effects between the CSI 500 stock index futures market and its underlying spot market during April to September 2015, the purpose of this paper is to explore whether Chinese stock index futures should be responsible for the 2015 stock market crash. Design/methodology/approach Using both linear and non-linear econometric models, this paper empirically examines the mean spillover and the volatility spillover between the CSI 500 stock index futures market and the underlying spot market. Findings The results showed the following: the CSI 500 stock index futures market has significant one-way mean spillover effect on its spot market. The volatility in CSI 500 stock index futures market also has a significant positive spillover effect on its spot stock market, and the mean value of dynamic correlation coefficient between the two market volatility is 0.4848. The spillover effect of the CSI 500 stock index futures market on the underlying spot market is significantly asymmetric, characterized by relatively moderate and slow during the period of the markets rising, yet violent and rapid during the period of the markets falling. The findings suggest that although the stock index futures itself was not the “culprit” of Chinese stock market crash in 2015, its existence indeed accelerated and exacerbated the stock market’s decline under the imperfect trading system. Originality/value Different from the existing literature mainly focusing on CSI 300 stock index futures, this paper empirically examines the impact of the introduction of CSI 500 stock index futures on 2015 Chinese stock market crash for the first time.

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