The role of investor sentiment in the contemporaneous dynamics in energy futures prices: A Discrete Wavelet Transformation 36th USAEE CONFERENCE / Washington DC Sana BEN KEBAIER 1 1 PhD Student Paris Dauphine University-PSL September 24, 2018 Sana BEN KEBAIER Paris Dauphine-PSL USAEE CONFERENCE / Washington DC September 24, 2018 1 / 22
OUTLINE 1 Introduction and motivation 2 Literature review 3 Data 4 MODWT Non Linear Granger Causality 5 Empirical results 6 Conclusion Sana BEN KEBAIER Paris Dauphine-PSL USAEE CONFERENCE / Washington DC September 24, 2018 2 / 22
Introduction and motivation Crude oil and natural gas are major sources of energy. Over the last decade, the interest of different investors on energy markets has dramatically increased for many reasons. Strong co-movement between equity markets, make commodity markets very attractive to investors seeking diversification benefits by using commodities to hedge against risk (e.g. Gorton and Rowenhorst (2006),Buyuksahin and Robe (2014)). Therefore, significant changes appeared in commodity markets. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 3 / 22
Introduction and motivation Financialization of energy markets : Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 4 / 22
Introduction and motivation Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 5 / 22
Introduction and motivation However, starting from the financial crises in 2008, the correlation between energy prices and stock market has significantly increased and become positive : This endorsed a contagion effect between economic news and energy markets (Reboredo and Uddin, 2016). While, financial investors continue to strongly invest in commodity markets when diversification benefits disappeared. These growing interest in commodity markets is synchronized with significant booms and busts in energy markets. The results of the relationship between investor sentiment and equity markets are of huge interest and very challenging to classical asset pricing theory, Baker and Wurgler (2006), Yang and Lee (2013). Do investors behavior plays a role in driving energy prices? or do they just respond to price movements and make decision build on prices volatilities and forecasts? I explore the time frequency linear and non-linear causalities between investor sentiment and energy commodity futures prices. I believe that the results are significant to investors, traders and policy makers. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 6 / 22
Literature review "From efficient theory to behavioral finance", Shiller (2003) Crude oil price is determined by macroeconomic global conditions,economic shocks,monetary shocks, stock market volatilities, exchange rate movements (e.g.barsky and Kilian (2004),,Arouri et al(2011), Creti et al (2013)). Natural gas price mainly determined by its fundamentals : Macroeconomic variables,weather, oil price, coal price,storage, supply shortfall and natural gas imports and exports, Nick and Thoenes (2014), Ji et al (2018). Investor sentiment Generally, a significant relationship is found between crude oil and investor sentiment index, Li et al (2015), Han et al (2017). None of previous studies treated this issue in the case of natural gas. None of previous literature treated the time frequency relationship between crude oil and investor sentiment index. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 7 / 22
Data To investigate the relationship between energy futures prices and investor sentiment index I use : Daily data from January 2004 to December 2017. Prices are extracted from DataStream Software. For investor sentiment, I use the AAII global sentiment index extracted from DataStream. For econometric needs, the data must be stationary => The first difference of energy futures prices + The investor sentiment raw data (stationary). Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 8 / 22
Data : INVESTOR SENTIMENT Survey among 1600 financial analysts and institutional investors. Participants are asked about their opinion regarding the current economic situation and their expectations for the next six months. The sentiment indicator consists of 36 different indicators. For both investor groups, private and institutional, is a component for current situations as well as expected situations indicated (24 sub-components). The answers of the private and the institutional investors equally weighted are summarized to a headline-indicator for the current situations and the indicator of economic sentiment (expected situation). Each indicator is calculated as a qualitative diffusion-indicator and range between -100 (very bad, strongly deteriorating) and +100 (very good, strongly improving). The neutral point is therefore 0. A positive number means that the share of optimists outweighs the share of pessimists and vice versa. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 9 / 22
1) Testing co-integration and causality for the raw data 2) Decompose data into discrete wavelet scales using MODWT 3) Testing co-integration between energy scales and investor sentiment scales 4) Choosing between VAR and VECM to model the different scales 5) Testing linear and non-linear granger causality between energy prices and investor sentiment scales Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 10 / 22
How MODWT works? MODWT decomposes time series into time dependent components that transform the series from time domain into scale domain. Enables us to understand the frequency at which the activity is repeated without losing time domain. MODWT is the best time scaling filtering approach and have multiple advantages compared to the orthogonal Discrete Wavelet Transform (DWT). MODWT enables the alignment of all the decomposed wavelet and scaling coefficients in every level with the original time series. MODWT coefficients increases the effective degrees of freedom on each scale. MODWT have the power to maintain the down-sampled values at each level of decomposition. MODWT is redundant and are not affected by circular shifting of the original time series. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 11 / 22
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MODWT Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 12 / 22
Introduction and motivation Literature review Data Empirical results Conclusion Non Linear Granger Causality 5 Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 13 / 22
Results Significant co-integration between energy prices and investor sentiment. No granger causality between energy prices and sentiment index. Results for the time frequency analysis : Linear causality test : Both cointegration and no cointegration are found between energy prices and sentiment index MODWT time scales. Linear Granger causality based on VAR and VECM shows unidirectional Granger causality up to level 3 : crude oil prices have the power to influence investors behavior. Starting from level 4, the causality is bidirectional. Results for natural gas show no causality up to level 3, a unidirectional causality for level 4 and bidirectional causality from level 5 to 8. Non linear causality test : Results are similar for crude oil. Non-linear approach captures a unidirectional causality up to level 3 for natural gas ;Investors behavior impact natural gas prices. Similar bidirectional causality are detected from level 4 to 8. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 14 / 22
Introduction and motivation Literature review Data Empirical results Conclusion Empirical results 1 Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 15 / 22
Introduction and motivation Literature review Data Empirical results Conclusion Empirical results 2 Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 16 / 22
Empirical results 3 Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 17 / 22
Introduction and motivation Literature review Data Empirical results Conclusion Empirical results 4 Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 18 / 22
Introduction and motivation Literature review Data Empirical results Conclusion Empirical results 5 Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 19 / 22
Introduction and motivation Literature review Data Empirical results Conclusion Empirical results 6 Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 20 / 22
Conclusion Classical economic theory leaves no place to behavioral implications in energy commodity pricing process. A large literature in finance studied the evidence related to investor sentiment linkage with financial market and asset prices. Results are very challenging to classical pricing theories. According to linear and non-linear approaches results are similar for crude oil : on the short run, the causality is unidirectional : crude oil has the power to drive investors behavior. in average and long run, the causality is bidirectional between investor sentiment and crude oil. Results were of a great interest in the case of natural gas. the non-linear approach gives more insights in the short run=investors sentiment has a significant influence on natural gas futures prices. in the middle and long term, the causality is bidirectional and significant between natural gas and investor sentiment. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 21 / 22
What is next? I will compare oil market results with natural gas market results. Results are still descriptive, I am improving the economic interpretation. Sana BEN KEBAIER Paris Dauphine-PSL USAEE September 24, 2018 22 / 22