LONG MEMORY AND TIME VARYING HEDGING OPPORTUNITIES BETWEEN CLEAN ENERGY, CRUDE OIL AND TECHNOLOGY SECTOR

6 In this paper, long memory and time varying hedging opportunities between clean energy, West 7 Texas Intermediate (WTI) crude oil and technology share prices were analyized between 3 May 2005- 8 16 October 2019. The relationships were investigated by DECO-FIGARCH model with daily 9 frequencies. According to findings, it is understood that volatility clusters were determined in crude oil, 10 alternate source energy and technology returns. Due to this useful information shocks reach to all three 11 investment tools and being eliminated at hyperbolic speed, also the volatility spillover lasted for a long 12 time. The most important finding of the research is that long position risks arising in both clean energy 13 and technology sectors can be effectively and efficiently hedged with WTI futures contracts. On the 14 other hand, it was determined that WTI can be added to the portfolio in order to reduce the risks of 15 portfolio to be established with clean energy and technology sector.


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Supply of energy plays an important role in today's society, ranging from assuring basic human 22 needs to independence of countries. There are three basic sources where can be provided. Traditional 23 fossile sources like crude oil which has been in use for nearly more than a century, from renewable 24 energy sources and from nuclear raw materials in the form of nuclear energy. However crude oil prices 25 are determined according to demand and supply principle, local and international problems of the crude 26 oil exporting countries which are called "OPEC", sudden shocks in the market, like contraction of 27 demand or political and social restrictions taken for oil and its derivatives due to global climate change 28 will cuase high volatility in the price changes. On the other hand, boost of oil price will trigger the 29 demand on alternative sources, of course this will make a positive impact on the revenue stream of such In 2018 Reboredoa and Ugolini study evaluated the effect of cardinality of clean energy share profits in 90 price alterations of fossile fuels (oil, natural gas, coal) and power generating costs. They have found 91 that, whenever there is an up/down fluctuation in power generating costs, it has a major affect on 92 renewable energy price dynamics. Moreover, electric prices in Europe and crude oil prices in United 93 States are major determinants in renewable energy share fluctuations. 94 Ferrer et al.'s study in 2018 shows that correlation among these occure in short term, such as up 95 to 5 days, but long term effects were small in United States. Also another important result of this study 96 was, neither in long term nor short term crude oil price has major effect in the performance of alternate 97 source energy corporate shares in the stock exchange market. In 2018 Lee and Baek have used ARDL 98 model which considers asymmetrical effects and nonlinear. It was found that, alterations in crude oil 99 prices have asymmetrical and positive effect on alternate energy source company shares in short term. 100 In 2019 study of Song et al. shows that fossil fuel energy market, investors sentiment, alternate source 101 energy and dynamic data in return between renewable energy market and spread of unpredictability. 102 The results can be summarized as; spread of unpredictability is stronger than spread of returns, so the 103 risk transfer amongst markets is apparent. The fossile fuel energy markets (especially crude oil) effect 104 on alternate source energy shares in stock exchange markets are greater than investors sentiments. 105 Finally investors sentiment in alternate source energy markets can be explained up to a certain degree 106 with profits of these shares and their fluctuations. When we sum up all these with everthing that exists in the literature, the highlights are: positive 122 relation amongst crude oil price and alternate source energy price, whenever crude oil price goes up 123 there is a significant rise in the alternate source energy indexes. Also there is a causal connection 124 between technology shares, crude oil prices and alternate source energy corporate shares, on the 125 otherside the relation amongst alternate source energy corporate shares and high technology corporate 126 shares are more intense than alternate source energy corporate shares and fossil fuel prices. 127

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MGARCH models are used frequently by researchers to determine portfolio selections, 129 volatility spreads and hedge opportunities between financial markets. Financial series behavior of sharp 130 and skewed distributed character, disapperance of information in hyperbolic speed after reaching 131 financial assets, reluctancy of financial series to return to average are causing financial assets to be 132 interpreted as showing long memory behavior. In this respect Fractional GARCH models are preferred 133 instead of GARCH models to examine the volatility structures of financial assets. 134 In this study, we will examine dynamic volatilite interactions among crude oil prices, alternate off-diagonal elements in order to lessen the estimation time by simplifying the procedure. This method 143 is named, dynamic equicorrelation (DECO) model, and written as: where , is (i,j) th component of matrix of cDCC model. This scalar equicorrelation is to 146 estimate conditional correlation matrix: 147 If is matrix of 1 and is -dimensional identity matrix. This presupposition of 149 equicorrelation results as more simple equation when is given by Eq. (3): 150 Baillie et al. study in 1996 introduced a fractional integrated GARCH model (FIGARCH) to 152 specify long memory of volatility return. GARCH model is expressed as an ARMA (mp) for squared 153 error form, 154 When is long memory parameter, ∅( ) and ( ) are delimited order lag polynomials with 159 roots assumed to be outside of unit circle and (1 − ) ̅ is fractional differencing operator. FIGARCH 160 ( , ̅ , ) model is turned to standard GARCH when ̅ = 0 and IGARCH model when ̅ = 1. 161

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In this study we have used data from The WilderHill Clean Energy Index (ECO), NYSE Arca 163 Tech 100 Index (PSE) and daily closing prices of crude oil at West Texas Intermediate (WTI). They are 164 obtained from www.finance.yahoo. The WilderHill Clean Energy is the oldest index, who covers 54 165 alternate source energy companies. The abbreviation for "Clean Energy Index" in the stock market is 166 "ECO". NYSE Arca Tech 100 Index was founded in 1986 and shows share prices of computer hardware 167 & software companies, health equipment manufacturers, telecommunications and other technology 168 companies. Its abbrevation in the stock market is "PSE". 169      Notes: Qs (10) and Qs (20) refering to Ljung-Box test data performed to the squared standardized particles with 10 and 20 delays respectively. The asterisks *, ** and *** shows significance at 10 %, 5 % and 1 % levels, respectively. The p-values are shown in brackets and the standard errors are in parentheses.
In Table 5  Provisional volatilities in DECO-FIGARCH could be practiced for estimation of time-change 227 hedge ratio. Figure 4 and Table 6 showing a 1 $ long term position in crude oil, which can be hedged 228 with 53 cents in short term position at ECO. Average 1$ long term position in ECO, can be hedged with 229 39 cents with short term position in WTI. Also average 1$ long term position in WTI, can be hedged 230 with 85 cents with short term position in PSE. On the other hand, 1$ long term position in PSE, can be 231 hedged with 24 cents with short term position in WTI. Future oil contracts can be used to manage long 232 term position risks arising from alternate source energy and technology shares. 233 Note: First asset is long, second asset is short in the portfolio. 235 Calculating amount of these assets are important within the optimal portfolios, also calculating 236 short term positions to avoid any long term position risks arising from financial assets. Conditional 237 volatility obtained from DECO-FIGARCH can help to calculate amounts of portfolio by using equation between these two investments. ℎ , , representing variance in both investments. When 1 represents 243 value of asset, the remaining part will show the second investment value in the portfolio. Figure 5 shows 244 time rates of financial asset amounts amongst the prospective portfolios.   placing WTI futures in the portfolio exists, which will provide serious opportunities for managing risks.

Conclusion and Policy Recommendation
In this paper, modeling the volatility of financial assets with a more robust method with the 293 DECO-FIGARCH model will fill an important gap in this area. Although Sadorsky (2012) previously 294 listed among the short memory models Dynamic conditional correlation, Constant Conditional 295 Correlation etc. Although the subject is examined with models, it is the first study to examine these 296 relationships by using models that take into account that information shocks that affect financial assets 297 disappear at hyperbolic speed, which differentiates the study from previous studies. 298 Whereas The S&P Global Clean Energy Index, The MSCI Global Alternative Energy Index, 299 MSCI World Information Technology index and many other similar indices were used in such related 300 studies, energy and technology cathegory indices were not included, this constitutes most important 301 constraints. By including more energy and technology indices in future studies, it will also be possible 302 to develop studies to select between multiple models in terms of predictive performance. Considering 303 multivariate Fractional GARCH models, which take into account that time series are fractal (self 304 similarity) instead of short memory (CCC, BEKK, DCC GARCH etc.) models, which have been used 305 many times before, in modeling the return volatility of renewable energy and technology sectors, in 306 terms of portfolio optimization and hedging opportunities. It will offer important advantages to 307 investors.