{"title":"一种基于行为的市场情绪指标,用于分析和预测波动性和泡沫","authors":"C. Ciaschini, M. C. Recchioni","doi":"10.1108/rbf-07-2021-0128","DOIUrl":null,"url":null,"abstract":"PurposeThis work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.Design/methodology/approachData evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).FindingsThe empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.Originality/valueThe authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A market sentiment indicator, behaviourally grounded, for the analysis and forecast of volatility and bubbles\",\"authors\":\"C. Ciaschini, M. C. Recchioni\",\"doi\":\"10.1108/rbf-07-2021-0128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.Design/methodology/approachData evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).FindingsThe empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.Originality/valueThe authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. 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引用次数: 0
摘要
本研究旨在设计一个指标,用于检测和预测欧洲洲际交易所期货市场(IFEU)、美国洲际交易所期货市场(IFUS)和芝加哥期货交易所(CBOT)三个涉及农产品和软商品的市场的价格波动和投机泡沫。该指标被设计为需求/供应优势比,旨在克服美银美林(Bank of America Merrill Lynch)的牛熊比等情绪指数中存在的主观性限制。设计/方法/方法数据证据允许对雅可比扩散过程进行参数估计,该过程对需求份额进行建模,并导致对投机泡沫和实现波动性的预测。采用自回归综合移动平均误差(ARIMA)动态回归对结果进行验证。并与传统的广义自回归条件异方差(GARCH)模型的结果进行了比较。该数据库是从汤森路透数据流(近期期货每日频率)中检索的。实证分析表明,该指标成功地捕捉到了中长期波动率在未来的趋势。将模拟结果与通常用于预测波动率趋势的传统GARCH模型的模拟结果进行比较,证实该指标能够复制趋势,并提供转折点,即GARCH分析完全忽略的附加信息。原创性/价值作者的商品需求作为离散时间过程,能够在连续时间框架中复制观察到的趋势,以及转折点。这个过程适合于估计代理的行为参数,即长期均值、均值回归速度和羊群行为。这些参数用于预测投机泡沫和实现波动率。
A market sentiment indicator, behaviourally grounded, for the analysis and forecast of volatility and bubbles
PurposeThis work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.Design/methodology/approachData evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).FindingsThe empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.Originality/valueThe authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.
期刊介绍:
Review of Behavioral Finance publishes high quality original peer-reviewed articles in the area of behavioural finance. The RBF focus is on Behavioural Finance but with a very broad lens looking at how the behavioural attributes of the decision makers influence the financial structure of a company, investors’ portfolios, and the functioning of financial markets. High quality empirical, experimental and/or theoretical research articles as well as well executed literature review articles are considered for publication in the journal.