Forecasting the Volatility of Palm Oil Market

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1 The International Symposium on Forecasting (ISF) 2013 KAIST College of Business, Seoul Forecasting the Volatility of Palm Oil Market by: Chee-Pung Ng Putra Business School Universiti Putra Malaysia Wei-Chong Choo and Cui-Jing Soh Faculty Economics and Management Universiti Putra Malaysia Presentation Date: 25 th June 2013

Universiti Putra Malaysia cpng101@hotmail.

2 Outline Introduction Literature Review Data and Methodology Data collection Graphs Forecasting methods Results and Discussion In-Sample Results Out of Sample Results Conclusion Recommendations for Further Study

methods Results and Discussion In-Sample Results Out

3 Introduction A great deal of effort has gone into modeling the volatility of financial time series (Smith and Bracker, 2003). Most of the forecasting effort has concentrated on forecasting volatility in exchange rates (West and Cho, 1994; or Bollerslev, 1990) option prices (Christensen and Prabhala, 1998; or Noh Engle and Kane, 1994) stock prices (Gallo and Pacini, 1998). Not much effort has been made in attempting to forecast the volatility in commodity (or futures) series. This research attempts to fill this void in the literature.

option prices (Christensen and Prabhala, 1998; or Noh Engle and Kane, 1994) stock prices (Gallo and Pacini, 1998).

4 Problem Statement Investors and portfolio managers very crucial need a superior forecast of the volatility because this is a good starting point for assessing investment risk.

5 Research Objectives Main objective: Determine the best forecasting model in the crude palm oil volatility. Specific objective: To compare performance of STES with GARCH models and Ad Hoc methods by using measurements like Mean Absolute Error (MAE) and Root Mean Square (RMSE) in out of sample.

Specific objective: To compare performance of STES with GARCH models and

6 Research Questions 1. Which model is the best method in forecasting the crude palm oil volatility? 2. How are the performances of STES with GARCH models and Ad Hoc methods by using measurements like Mean Absolute Error (MAE) and Root Mean Square (RMSE) in out of sample?

7 Why Commodities? The commodities literature is expanding and acquiring importance as a result of the increasingly significant role that commodities play in international financial markets and economies (Hammoudeh, Malik and McAleer, 2011).

8 Why Crude Palm oil? 1. There are quite a numbers of studies in gold and silver. However, there are lack of research for crude palm oil. For example, modelling and forecasting volatility in the gold market (Trück and Liang, 2012), A power GARCH examination of the gold market (Tully and Lucey, 2007), and Structure in Gold and Silver Spread Fluctuations (Batten, Ciner, and Lucey, 2007). 2. Demand of crude palm oil increase tremendously and been used in many sectors. Palm oil in normally been used as a common cooking oil. Malaysia develops biodiesel-palm oil (bright prospect). More and more product used palm oil as it is ingredient for soap, cosmetics and skincare products, household candles, lubricants and detergents.

Gold and Silver Spread Fluctuations (Batten, Ciner, and Lucey, 2007). 2. Demand of crude palm oil increase tremendously and been used in many sectors.

9 Significance of study The result of this study will benefit or advantage for investors who are planning or involving in investment of crude palm oil. Investors can have a further understanding of the fluctuation or risk associated with the crude palm oil price volatility. Volatility is a great concern for policy makers and regulators who are interested in the effect of volatility on the stability of financial markets in the particular and the whole economy in general.

Investors can have a further understanding of the fluctuation or risk associated with the crude palm oil price

10 Definition of Variables Forecasting Volatility Volatility refers to the degree to which prices fluctuate. financial In finance, volatility is often used to quantify the risk of a financial instrument by computing the risk into standard deviation or variance (Brooks, 1998). Forecasting is a method of analysing and investigating the past or available information to estimate the result of future (Dwaikat, 2009).

financial In finance, volatility is often used to quantify the risk of a financial instrument by

11 Literature Review The ARCH model proposed by Engle (1982) allowed the data to determine the best weights to use in forecasting the variance. Bollerslev (1986), GARCH, has been used to model time-varying conditional volatility. Both models explain time series behaviour by allowing the conditional variance to evolve over time and to respond to volatility. Glosten et al., (1992) proposed the asymmetric GARCH, (GJR) model. Nelson (1991) proposed the Exponential GARCH (EGARCH) model.

Both models explain time series behaviour by allowing the conditional variance to evolve over time and to respond to

12 Literature Review smooth transition exponential smoothing (STES) allow a parameter to vary over time as a continuous function of a transition variable. Exponential smoothing is a popular approach, which has been found to perform well in empirical studies (e.g. Boudoukh et al.,1997). Researchers developed adaptive exponential smoothing methods, which allow smoothing parameters to change over time, in order to adapt to changes in the characteristics of the series (e.g. Trigg and Leach, 1967). Ung, 2013 state that STES model shown to be the most effective portfolio risk forecasting method on a monthly basis.

Researchers developed adaptive exponential smoothing methods, which allow smoothing parameters to change over time, in order to adapt to changes in the

13 Literature Review Although various studies or researches had been conducted to forecast the commodity volatility, our interest here is to evaluate the performances of different methods in forecasting the commodity specifically crude palm oil volatility.

interest here is to evaluate the performances of different

Data and Methodology Secondary data Daily data (crude palm oil) Cover 2000 observations Data period Data source: (2001 to 2010) Malaysia Derivatives Exchange (MDEX)

14 Data and Methodology Secondary data Daily data (crude palm oil) Cover 2000 observations Data period Data source: (2001 to 2010) Malaysia Derivatives Exchange (MDEX) Measurements The daily closing price for crude palm oil will be used for analysis. General statistical and econometric analyses, used for time series estimation and forecasting.

(MDEX) Measurements The daily closing price for crude palm oil will be used for

15 Graphs MDEX CPO PRICE GRAPH MDEX CPO ln(ret) GRAPH MDEX CPO RES GRAPH

16 Graphs MPOB CPO PRICE GRAPH MPOB CPO ln(ret) GRAPH MPOB CPO RES GRAPH

17 Graphs MPOB CKPO PRICE GRAPH MPOB CKPO ln(ret) GRAPH MPOB CKPO RES GRAPH

18 Forecasting Methods Ad-hoc Methods Random Walk x t = x t-1 + ε t Naïve Variance Forecasting Moving Average 30 X 1 t = W t X t + W t-1 X t W t-k-1 X t-k-1 EWMA σ 2 t = αԑ 2 t-1 + (1-α)σ 2 t-1 GJR GARCH Models GARCH ơ t2 = ω + α ε 2 t-1 + β ơ 2 t-1 IGARCH σ² t =ᴡ+ (1-I [Ɛ t -₁> 0]) α₁ɛ² t-₁+(1-i [Ɛ t-₁> 0] )γ Ɛ² t-₁+β₁σ² t-₁ EGARCH STES STES Models

t = αԑ 2 t-1 + (1-α)σ 2 t-1 GJR GARCH Models GARCH ơ t2 = ω + α ε 2 t-1 + β ơ 2 t-1

19 Results and Discussions

20 In-Sample Results Summary of MAE for 2000 in-sample volatility forecast generated by all methods.

21 In-Sample Results Summary of RMSE for 2000 in-sample volatility forecast generated by all methods.

22 In-Sample Results Theil-U and mean ranking of MAE for 2000 in-sample daily volatility forecast using realised variance as actual. The highlighted in bold are the three lowest average Theil-U values.

23 In-Sample Results Theil-U and mean ranking of RMSE for 2000 in-sample daily volatility forecast using realized variance as actual.

24 Out of Sample Results Summary of MAE for 500 out of sample volatility forecast generated by all methods.

25 Out of Sample Results Summary of RMSE for 500 out of sample volatility forecast generated by all methods.

26 Out of Sample Results Theil-U and mean ranking of MAE for 500 out sample daily volatility forecast using realized variance as actual.

27 Out of Sample Results Theil-U and mean ranking of RMSE for 500 out sample daily volatility forecast using realized variance as actual.

28 Conclusion As volatility is unobservable, it must be estimated. Seeking for a model which produces best forecasts has always been a main concern for researchers and investors. There is growing evidence claims that sophisticated time-series models tends to produce more precise forecasts compared to conventional one.

29 Conclusion the result of the comparison between 3 forecasting models show that the STES method is ranked at first. Overall result show that STES-E+AbsE rank is the best performance following by STES-AbsE and STES-ESE across the MAE criterion. The ranking of the four lowest average Theil value across the RMSE criterion are as follow: STES- E+AbsE, the lowest at in sample and MA30 are perform best in out sample. STES-E-AbsE outperformed other methods in Crude palm oil series.

30 Recommendations for Further Study 1. Using intraday, weekly, monthly and yearly prices. 2. Data such as volume can be used in forecasting to generate a more informative result. 3. Study on the effect of the economic down turn and crash on 1987 and More forecast methods should be extended in future study to produce a better insight of which forecast method is the best. 5. This research can also be extended in terms of number of series. This can help to prove the accuracy of the results.

31 Thank you!

Intraday Volatility Analysis on S&P 500 Stock Index Future

Intraday Volatility Analysis on S&P 500 Stock Index Future Hong Xie Centre for the Analysis of Risk and Optimisation Modelling Applications Brunel University, Uxbridge, UB8 3PH, London, UK Tel: 44-189-526-6387