A market sentiment indicator, behaviourally grounded, for the analysis and forecast of volatility and bubbles

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.

Agent expectations and news sentiment in the dynamics of price in a financial market

“…Those who study market dynamics are increasingly aware of behavioral bases for the contributions of both sentiment, either market-specific or general (e.g., Ciaschini and Recchioni, 2022;Zhang et al, 2011) and expectations to the direction and level of price. Measures of expectations now have an extensive history in predicting market price (e.g., Hansen and Sargent, 2014).…”

Food Price Bubbles: Food Price Indices of Turkey, the FAO, the OECD, and the IMF

“…According to Alola (2022) [5], this type of bubble can create a cycle of swiftly growing and reducing expenses that can be hard to predict and manipulate. In addition, price bubbles can results in the misallocation of resources, as sources are diverted from their most efficient uses [6,7]. During the bubble era, traders' speculative actions were a significant factor in raising the price of food [8].…”