forecasting the volatility of palm oil market...forecasting the volatility of palm oil market by:...

With Knowledge We Serve

Forecasting the Volatility of Palm Oil Market

by:

Chee-Pung Ng

Putra Business School Universiti Putra Malaysia

[email protected]

Wei-Chong Choo and

Cui-Jing Soh

Faculty Economics and Management Universiti Putra Malaysia

wcchoo@putra,upm.edu,myPresentation Date:

25th June 2013

The International Symposium on

Forecasting (ISF) 2013

KAIST College of Business, Seoul


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


Introduction

A great deal of effort has gone into modeling the volatility offinancial time series (Smith and Bracker, 2003).

Most of the forecasting effort has concentrated onforecasting 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 thevolatility in commodity (or futures) series.

This research attempts to fill this void in the literature.


Problem Statement

Investors and portfolio managers verycrucial need a superior forecast of thevolatility because this is a goodstarting point for assessing investmentrisk.


Research Objectives

Main objective:

Determine the best forecasting model in thecrude palm oil volatility.

Specific objective:

To compare performance of STES with GARCHmodels and Ad Hoc methods by usingmeasurements like Mean Absolute Error (MAE)and Root Mean Square (RMSE) in out of sample.


Research Questions

1. Which model is the best method inforecasting the crude palm oil volatility ?

2. How are the performances of STES withGARCH models and Ad Hoc methods byusing measurements like Mean AbsoluteError (MAE) and Root Mean Square (RMSE)in out of sample?


Why Commodities?

The commodities literature isexpanding and acquiring importanceas a result of the increasinglysignificant role that commodities playin international financial markets andeconomies (Hammoudeh, Malik andMcAleer, 2011).


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 goldmarket (Trück and Liang, 2012), A power GARCH examination ofthe gold market (Tully and Lucey, 2007), and Structure in Goldand 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 forsoap, cosmetics and skincare products, household candles,lubricants and detergents.


The result of this study will benefit oradvantage for investors who are planning orinvolving in investment of crude palm oil.

Investors can have a further understanding ofthe fluctuation or risk associated with the crudepalm oil price volatility.

Volatility is a great concern for policy makersand regulators who are interested in the effect ofvolatility on the stability of financial markets inthe particular and the whole economy in general.

Significance of study


Volatility refers to the degree to which financialprices fluctuate.

In finance, volatility is often used to quantify the riskof a financial instrument by computing the risk intostandard deviation or variance (Brooks, 1998).

Forecasting is a method of analysing andinvestigating the past or available information toestimate the result of future (Dwaikat, 2009).

Definition of Variables

Forecasting Volatility


Literature Review

The ARCH model proposed by Engle (1982) allowed thedata to determine the best weights to use in forecastingthe variance.

Bollerslev (1986), GARCH, has been used to modeltime-varying conditional volatility.

Both models explain time series behaviour by allowingthe conditional variance to evolve over time and torespond to volatility.

Glosten et al., (1992) proposed the asymmetric GARCH,(GJR) model.

Nelson (1991) proposed the Exponential GARCH(EGARCH) model.


Literature Review smooth transition exponential smoothing (STES) allow

a parameter to vary over time as a continuous functionof a transition variable.

Exponential smoothing is a popular approach, which hasbeen found to perform well in empirical studies (e.g.Boudoukh et al.,1997).

Researchers developed adaptive exponentialsmoothing methods, which allow smoothingparameters to change over time, in order to adapt tochanges in the characteristics of the series (e.g. Triggand Leach, 1967).

Ung, 2013 state that STES model shown to be the mosteffective portfolio risk forecasting method on a monthlybasis.


Literature Review

Although various studies or researches had beenconducted to forecast the commodity volatility, ourinterest here is to evaluate the performances ofdifferent methods in forecasting the commodityspecifically crude palm oil volatility.


MeasurementsThe daily closing price for crude palm oil will beused for analysis.

General statistical and econometric analyses, usedfor time series estimation and forecasting.

Data and Methodology

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


Graphs

MDEX CPO PRICE GRAPH

MDEX CPO ln(ret) GRAPH

MDEX CPO RES GRAPH


Graphs

MPOB CPO PRICE GRAPH

MPOB CPO ln(ret) GRAPH

MPOB CPO RES GRAPH


Graphs

MPOB CKPO PRICE GRAPH

MPOB CKPO ln(ret) GRAPH

MPOB CKPO RES GRAPH


Forecasting MethodsAd-hoc Methods

Random Walk xt = xt-1 + εt

Naïve Variance Forecasting

Moving Average 30 X1t = WtXt+ Wt-1Xt-1 + … + Wt-k-1Xt-k-1

EWMA σ2t = αԑ2

t-1 + (1-α)σ2t-1

GARCH Models

GJR

GARCH ơt2 = ω + α ε 2t-1 + β ơ2

t-1

IGARCH σ²t =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 Models

STES


Results and Discussions


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


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


In-Sample ResultsTheil-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.


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


Out of Sample ResultsSummary of MAE for 500 out of sample volatility forecast generated by all methods.


Out of Sample ResultsSummary of RMSE for 500 out of sample volatility forecast generated by all methods.


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


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


Conclusion

As volatility is unobservable, it must be estimated.

Seeking for a model which produces best forecastshas always been a main concern for researchersand investors.

There is growing evidence claims that sophisticatedtime-series models tends to produce more preciseforecasts compared to conventional one.


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 bestperformance following by STES-AbsE and STES-ESEacross the MAE criterion.

The ranking of the four lowest average Theil valueacross 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 Crudepalm oil series.


Recommendations for Further Study1. Using intraday, weekly, monthly and yearly prices.

2. Data such as volume can be used in forecasting togenerate a more informative result.

3. Study on the effect of the economic down turn andcrash on 1987 and 1997.

4. More forecast methods should be extended in futurestudy to produce a better insight of which forecastmethod is the best.

5. This research can also be extended in terms ofnumber of series. This can help to prove the accuracyof the results.


Thank you!

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