投资咨询业务资格：证监许可【2012】669号 中信期货研究|⾦融衍⽣品策略⽇报 2025-08-27 股市⾏情未完，债市情绪回暖 股指期货：⾼位震荡，资⾦缩量。 股指期权：期权交易持续活跃，偏度暗⽰⾏情未完。 国债期货：债市多头情绪延续。 股指期货方面，高位震荡，资金缩量。周二沪指低开震荡，量能收缩 近5000亿元，资金出现对冲情绪，预计将进入高位震荡。一是高量能的持 续性有限，历史上A股成交额超过3万亿元的日期只有两个，其一是2024年 10月8日，对应去年沪指高点，其二就是周一，因此未见新增量资金的情 况下，量能继续放大的难度加大。二是进入中报密集披露期，资金规避前 期高估值板块，寻找低位补涨和业绩改善的行业机会，催化农林牧渔、 美容护理、基础化工领涨。三是阅兵临近，风险偏好随时可能收敛，资金 有所衰减，午后出现高位股回撤，导致出现集中平多，股指期货四品种均 减仓超万手。但此类回撤我们定义为牛市中的震荡，亏损股补涨等牛市尾 声信号尚未出现，居民存款搬家仍在进程中，因此回调是加仓良机，而非 担忧行情反转，建议继续持有IM多单，等待下方加仓契机。 股指期权方面，期权交易持续活跃，偏度暗示行情未完。前一日我们 ...

Group 1 - The overall A-share market has risen since the beginning of 2024, with growth style performing relatively strong while value style has been weaker [3][4] - From early 2024 to August 21, 2025, the CSI All Share Index saw a maximum increase of 56.98%, while the CSI 300 Value Index had a maximum increase of 45.13%, and the ChiNext Index reached a maximum increase of 82.16% [4] - The current market uptrend is similar to the period from 2013 to 2017 [6] Group 2 - Between 2012 and 2014, A-shares experienced a bear market with a maximum drawdown of 39.24% due to poor fundamentals and declining corporate profits [7] - In the second half of 2014, financial stocks such as securities and insurance surged significantly, with the Securities Industry Total Return Index rising by 206.91% from July 1 to the end of 2014 [9] - In the first half of 2015, small-cap and growth styles saw substantial gains, with the CSI All Share Index rising from over 2000 points to over 8000 points [10][11] Group 3 - The second half of 2015 saw a significant market decline, with valuations quickly dropping to around 4 stars [16] - From 2015 to the end of 2018, the A-share market experienced a maximum drawdown of 55.78%, with small-cap stocks and growth stocks facing even larger declines [20] - The period from 2016 to 2017 saw a recovery in value and consumer stocks, leading to a slow bull market [21] Group 4 - The current market phase resembles the 2015-2016 period, with stimulus policies beginning to take effect and corporate fundamentals starting to recover [35] - If corporate fundamentals continue to improve, there is potential for further market growth, similar to past economic recovery phases [36] - The current market is rated at around 4 stars, indicating it is relatively inexpensive and still offers opportunities for stock asset allocation [37][40] Group 5 - The current bull market differs from the 2014-2015 bull market in that there is stricter control over leveraged investments and the real estate sector remains in a bear market [32][33] - The main drivers of the recent market uptrend have been financial stocks, with the Securities Industry Total Return Index achieving a maximum increase of 80.43% from June 3, 2024, to the end of 2024 [28] - By 2025, small-cap and technology stocks are expected to take over as the main growth drivers, while value and consumer stocks may remain relatively subdued [29]

Group 1: Introduction to Volatility Data - The financial option implied volatility index reflects the 30 - day implied volatility (IV) trend as of the previous trading day, and the commodity option implied volatility index is obtained by weighting the IV of the two - strike options above and below the at - the - money option of the main contract, reflecting the IV change trend of the main contract [2] - The difference between the IV index and historical volatility (HV) indicates the relative level of IV to HV. A larger difference means higher IV relative to HV, and a smaller difference means lower IV relative to HV [2] Group 2: Volatility Index Charts - Charts show the IV, HV, and IV - HV differences for various financial and commodity options, including 300 - stock index, 50ETF, 1000 - stock index, 500ETF, and many commodity options like silver, soybean meal, corn, etc [3] Group 3: Implied Volatility Quantile and Volatility Spread Quantile - Implied volatility quantiles represent the current level of a variety's IV in history. A high quantile means high current IV, and a low quantile means low current IV. Volatility spread refers to the difference between the IV index and historical volatility [4] - Quantile rankings are provided for different options such as PVC, PTA, 50ETF, 300 - stock index, etc [5]

Core Viewpoint - The article discusses the significant discount in the futures market compared to previous years and the higher implied volatility of put options compared to call options, suggesting a potential pessimistic outlook among investors. It proposes a quantitative timing strategy based on the synthetic underlying price of ETF options to address these issues [1]. Group 1: Concepts of Premium and Discount - The premium and discount of stock index futures is defined as the difference between futures prices and spot prices, with a positive value indicating a premium and a negative value indicating a discount. The annualized premium rate is often used for better comparison [2]. - The seasonal discount phenomenon in stock index futures is attributed to dividend payouts from constituent stocks, which can lead to a natural decline in the index and is particularly evident from May to September [2]. Group 2: Synthetic Underlying of ETF Options - The price of the synthetic underlying for ETF options can be expressed using the call option price, strike price, and put option price. The premium or discount rate is calculated as the difference between the synthetic price and the underlying ETF price [3]. - There is a strong positive correlation (over 0.97) between the annualized premium rate of the synthetic underlying of ETF options and the annualized premium rate of stock index futures after excluding dividends, indicating that the synthetic underlying may provide a more accurate reflection of market expectations [3]. Group 3: Quantitative Timing Strategy Backtest Results - The strategy suggests that when the valuation of put options is significantly higher than that of call options, it does not necessarily indicate a market downturn. Instead, it may present a buying opportunity [4]. - The strategy is based on the premise that when the ETF synthetic underlying futures premium is at a historical low, it indicates excessive pessimism, and a potential rebound may occur, prompting a buy signal for the next trading day [4]. Group 4: Historical Backtest Performance - The strategy has shown significant outperformance compared to the underlying ETFs since 2018, with an annualized return of 19.05% and a maximum drawdown of -17.83% when trading the Huatai-PineBridge 300 ETF [6]. - The cumulative return of the timing strategy reached 142.9%, significantly higher than the 51.8% return of the IC monthly contract and 2.52% of the 500 ETF [6]. Group 5: Summary - The article highlights the relationship between the synthetic underlying of ETF options and stock index futures, emphasizing the effectiveness of a quantitative timing strategy based on the synthetic premium. The results indicate that significant discounts in the futures market do not necessarily signal a sell-off but rather present opportunities for long positions [12].

Core Viewpoint - The Chinese ETF market is undergoing a significant transformation from 2024 to April 2025, with the total scale of non-monetary ETFs increasing from 1.85 trillion yuan at the end of 2023 to 3.89 trillion yuan, marking a 110% growth [1]. ETF Market Scale Changes - The ETF market is experiencing a shift in dominance from individual investors to institutional investors, with institutional holdings in stock ETFs reaching 62.14% and in bond ETFs reaching 84.90% [4]. - State-owned institutions and insurance companies are the main contributors to this growth, with state-owned holdings increasing by 922.4 billion yuan to 1.05 trillion yuan in the second half of 2024, and insurance funds increasing by 113.2 billion yuan to 260.7 billion yuan [4]. Institutional Preferences - Institutions are actively investing in core broad-based ETFs, with a total increase of 866.8 billion yuan in 300 ETFs and 500 ETFs, accounting for 59.3% of total inflows into stock ETFs [5]. - There is a strong preference for high-dividend assets among institutions, driven by the challenges of low interest rates, with the total market size of dividend-themed index funds reaching 173.55 billion yuan, an increase of 20.09 billion yuan from the end of 2024 [6]. Insurance Capital Activity - Insurance capital has been particularly active in acquiring dividend assets, with 16 instances of stake increases in listed companies, focusing on sectors like banking, utilities, energy, and logistics [9]. - Ping An Life has been notably active, making six acquisitions in Hong Kong-listed bank stocks, becoming a key player in this market [9]. Dividend ETF Characteristics - The main dividend index sectors are characterized by essential or monopolistic attributes, such as energy, resources, telecommunications, and utilities, benefiting from national policy incentives [10]. - Despite the growth in dividend ETFs, there are concerns regarding the sustainability of returns, as over 50% of the 56.32% return from the dividend low-volatility index in 2023-2024 came from the banking and coal sectors [11]. Market Outlook - The resilience of dividend assets has been highlighted, with both A-shares and Hong Kong stocks showing a favorable trend in dividend style since March [11]. - Future expectations suggest that while growth styles may dominate, dividend styles will exhibit a higher probability of success due to their high dividend yields and low volatility [11].

Group 1: Definitions - Financial option implied volatility index reflects the 30 - day implied volatility (IV) trend as of the previous trading day. Commodity option implied volatility index is obtained by weighting the IVs of the two strike prices above and below the at - the - money option of the main contract month, reflecting the IV change trend of the main contract [3] - The difference between the IV index and historical volatility (HV) indicates the relative level of IV to HV. A larger difference means IV is relatively higher than HV, and a smaller difference means IV is relatively lower [3] Group 2: Implied Volatility and Historical Volatility Charts - Charts show the IV, HV, and IV - HV differences of various products including 300 index, 50ETF, 1000 index, 500ETF, cotton, sugar, rubber, PTA, crude oil, methanol, iron ore, copper, PVC, rebar, urea, gasoline, aluminum, zinc, etc. from different time periods [4][6][7][8] Group 3: Implied Volatility and Historical Volatility Quantiles - Implied volatility quantiles represent the current IV level of a product in history. High quantiles mean current IV is high, and low quantiles mean current IV is low. Volatility spread is the difference between IV and HV [19] - The implied volatility quantiles of different products are provided, such as PTA (0.85), PVC (0.72), etc. [21]

Group 1 - The report indicates that the domestic stock indices experienced mixed performance, with the CSI 300, SSE 50, and SSE Composite Index showing declines of -1.08%, -1.22%, and -0.03% respectively, while the CSI 500 and CSI 1000 saw increases of 0.32% and 0.62% respectively [2][9] - The trading volume of comprehensive ETFs increased to 50.728 billion yuan, up by 10.269 billion yuan from the previous week, with large-cap style ETFs accounting for 27.055 billion yuan and small-cap style ETFs for 23.914 billion yuan [2][29] - The average weekly performance of 32 thematic ETFs was -0.32%, with large-cap style ETFs averaging -0.45% and small-cap style ETFs averaging -0.22% [3][30] Group 2 - The report highlights that the top three performing comprehensive ETFs were the 1000ETF, 500ETF, and 800ETF, with returns of 0.87%, 0.57%, and -0.50% respectively, while the bottom three were the ChiNext 50, Deep 100ETF, and ChiNext, with returns of -1.89%, -1.56%, and -1.10% [4][35] - In the thematic ETF category, the leading performers were in the biopharmaceutical and military sectors, with returns of 2.11%, 1.96%, and 1.85%, while the new energy vehicle and non-ferrous ETFs lagged with returns of -4.70%, -4.68%, and -2.34% [4][35] - The report notes that there was a net outflow of funds from major index ETFs in the comprehensive category, while the ChiNext-related ETFs saw inflows, indicating a shift in investor sentiment [4][35] Group 3 - The report provides insights into the trading activity of domestic stock ETFs, indicating that the trading hotspots were concentrated in the ChiNext 50, 1000ETF, and various sector ETFs such as banking and military [27][28] - The report also tracks the changes in market capitalization and trading volumes of comprehensive and thematic ETFs, noting that the total trading volume for thematic ETFs was 31.48 billion yuan, down by 4.192 billion yuan from the previous week [30][29] - The report emphasizes the importance of monitoring the trading activity and fund flows in ETFs as indicators of market sentiment and potential investment opportunities [26][29] Group 4 - The report indicates that the bond market showed mixed performance, with the Shanghai Stock Exchange convertible bonds experiencing a slight increase of 0.26%, while the main stock index futures had varied results [16][19] - In the commodity market, the report notes that the CRB poultry and edible oil indices saw slight increases, while the overall commodity market experienced mixed results [20][24] - The report also highlights the performance of overseas ETFs, with the NASDAQ ETF showing a gain of 1.74%, while the H-share and Hang Seng ETFs experienced declines [41][41]

Group 1: Report Information - Report name: Volatility Data Daily Report [1] - Update time: May 15, 2025 [1] Group 2: Index Explanation - The implied volatility index of financial options reflects the 30 - day implied volatility trend as of the previous trading day. The implied volatility index of commodity options is obtained by weighting the implied volatilities of the two - strike options above and below the at - the - money option of the main contract, reflecting the implied volatility change trend of the main contract [2] - The difference between the implied volatility index and historical volatility: A larger difference indicates that the implied volatility is relatively higher than the historical volatility, while a smaller difference means the opposite [2] Group 3: Implied Volatility and Historical Volatility Charts - Charts show the trends of implied volatility (IV), historical volatility (HV), and their differences (IV - HV) for various products including 300 - stock index, 50ETF, 1000 - stock index, 500ETF, IFR, corn, cotton, rubber, PTA, crude oil, Chinese jujube, iron ore, aluminum, PVC, rebar, zinc, urea, palm oil, etc [3][5][6][7][11] Group 4: Quantile Ranking - Implied volatility and historical volatility quantile rankings are provided for products such as PTA, 50ETF, methanol, etc. For example, the implied volatility quantile of PTA is 0.93, and its historical volatility quantile is 0.79 [13]