finace forecasting paper

8/12/2019 Finace Forecasting Paper

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CHAPTER I

INTRODUCTION

Brief Background

Trade is believed to have taken place throughout much of recorded human history.

Goods were traded straight by means of exchanging one good for another. Due to the demand

to have a medium of exchange and unit of measurement, money was then created to facilitate

trading. Money allows people to trade goods and services indirectly, understand the price of

goods. Before, gold or silver was used as a common form of money before by trading partners

to conduct their business. Money has reflected variations in government and politics, in power

an economic life, in religious and other cultural beliefs, in science and technology and in other

aspects of how we live. Eventually benefits of paper currency became obvious, still the currency

could not leave a mark in international trading. To enable countries to issue their own currency

and to have their own monetary policy, foreign exchange was established as a series of

solution. Furthermore, international trade was conducted by providing means of exchanging one

currency for another according to the valued exchange rate, which was either agreed-upon or

set by the market.

Towards the end of World War II, International Monetary Fund (IMF) was set up by the

major countries of the world. The IMF is an international organization that monitors balance of

payments and exchange rate activities. In July 1944, at Bretton Woods, New Hampshire, 44

countries signed the Articles of Agreement of the IMF. At the focus of those agreements was the

establishment of a worldwide system of fixed exchange rates between countries. It was gold

that anchor for the fixed exchange rates system. One-ounce of gold was defined to be worth 35

U.S. dollars. All other currencies were appraised to the U.S dollar at a fixed exchange rate. For


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Example the Japaneses Yen was set at 360 yen to a dollar, the British Pound was set at $4.80.

(About Us: History of Foreign Exchange Market, 2014)

Although the fixed exchange system served well, there was then the occurrence of dollar

devaluation and also it cannot be anymore convertible to gold. Free Exchange Rate Regime

and Jamaica agreement took place. Since March 1973, exchange rates have become much

more volatile and far less predictable. The major currencies such as US dollar move

independently of the other currencies. The currency may be traded by anyone so inclined. Its

value is a function of the current supply and demand forces in the market, and there are no

specific intervention points that have to be observed. (About Us: History of Foreign Exchange

Market, 2014)

In the Philippines the Bangko Sentral ng Pilipinas (BSP) maintains the exchange rate

system. The Bangko Sentral ng Pilipinas (BSP) sustains a floating exchange rate system. The

peso-dollar trading among Bankers Association of the Philippines (BAP) member-banks and

between these banks and the BSP are done through the Philippine Dealing System (PDS).

Exchange rates are determined on the basis of supply and demand in the foreign exchange

market. The role of the BSP in the foreign exchange market is principally to ensure orderly

conditions in the market. The market-determination of the exchange rate is consistent with the

Governments commitment to market-oriented reforms and outward-looking strategies of

achieving competitiveness through price stability and efficiency. (Foreign Exchange Market in

Philippines, 2008)

Rationale of the Study

The exchange rate is the price of a unit of foreign currency in terms of the domestic

currency. In the Philippines, for instance, the exchange rate is conventionally expressed as the

value of one US dollar in peso equivalent (Bangko Sentral ng Pilipinas, 2008). Exchange rate


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alters everyday and it is in an extreme manner in the Philippines due to the imposition of floating

exchange rates. With this, floating exchange rates means that the exchange rate is set by

market forces and changes in response to changing economic conditions (Mankiw, 2009).

The basic link between the local and the overseas market for different goods, services

and financial assets is the exchange rate system. Using the exchange rate, we are able to

evaluate prices of goods, services, and assets quoted in different currencies. Exchange rate

movements can affect actual inflation as well as expectations about future price movements and

directly affect domestic prices of imported goods and services. It can also affect the countrys

external sector through its impact on foreign trade. An appreciation of the peso, for instance,

could lower the price competitiveness of our exports versus the products of those competitor

countries whose currencies have not changed in value. The exchange rate also influences the

cost of servicing (principal and interest payments) on the countrys foreign debt. A peso

appreciation reduces the amount of pesos needed to buy foreign exchange to pay interest and

maturing obligations. (Bangko Sentral ng Pilipinas, 2008).

As discussed in this paper, the foreign exchange rate has an important role on the

decision making process of the countrys financial and economic activities. To have an

approximate of future rates is very important in the planning stage. Past experiences, news,

other media or even personal experience can be a basis to established forecasted values of the

exchange rates. However, exchange rates are made up of numbers which can be more properly

forecasted with the appropriate forecasting tools. Therefore, a forecast must be made with the

use of several forecasting tools in order to estimate the best values of the exchange rate for the

future. This will be done so that a better economic decision can be made for the Filipino citizens,

investors and the Philippine economy.


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Statement of the Problem

The Philippines emerged as the worlds third biggest recipient of remittances in 2013,

behind India and China, with total remittances of $26 billion according to a report released

recently (The World Bank: News, 2014). Also, the inflow of remittances from Filipinos abroad

expected to remain strong this year on the back of continuing global and local economic growth

according to Standard Chartered Bank (PhilStar: News, 2014). Filipino overseas workers

comprise a large number in the workforce around the world. Filipinos are now the second

largest Asian group in the US totalling 3.2 million based on the 2010 census of major racial and

ethnic groups in America (Inquirer, 2012). With these figures, it is one of the most watched

remittances or exchange rate against the PHP.

Determining monthly average exchange rates is essential because it helps business

owners or executives to see or predict trends in the market that can be disadvantageous to the

company and find solution before these can result to a problem. For firms trading with other

countries, movements in the exchange rate can be very important. A sharp appreciation in

sterling could reduce demand for exports. Firms may need to plan ahead by hedging exchange

rate movements or seeking to sell to domestic markets.

Therefore, the main focus of the study is the determination of future monthly average

PHP-USD exchange rates using different forecasting techniques that will be useful in solving

various financial problems and meeting the study objectives.


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CHAPTER II

OBJECTIVES OF THE STUDY

Objectives

Generally, the study aims to determine future monthly average Philippine Peso (PHP) -

US Dollar (USD) exchange rates using different forecasting models.

Specifically, this study aim to provide forecasts that would direct several sectors of the

society as to time to have transactions overseas. The forecasts that can be achieved can:

Give useful information for better understanding between the local and the overseas

market for various goods, services and financial assets and able to evaluate prices of

goods, services, and assets quoted in different currencies

Help policymakers in determining future contingency measures in relations with

economic stability

Endow basis for cost of servicing which includes principal and interest payments on

the countrys foreign debt

Serve as a quantitative basis for expectations of future prices

Provide OFWs and their families with information on when to send their remittances

in the Philippines

Aid Philippines and local entities as to when to export and import goods

Guide entrepreneurs in establishing a PHP-USD exchange firm


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CHAPTER III

METHODOLOGY

Collection of Data

The data points used in the study were obtained from online research. These were

gathered from the official internet site of the National Statistical Coordination Board (NSCB), a

policy-making and coordinating agency on statistical matters in the Philippines that provides

timely, accurate, relevant and useful data for the government and the public for planning and

decision making. Monthly average PHP-USD exchange rates from 2009 to 2013 in tabular form

were collected.

Test for Seasonality

Exchange rates are affected by numerous factors such as differentials in inflation and

interest rates, current-account deficits, public debts, terms of trade and political stability and

economic performance (Bergen, 2010). These things may cause the said rates to exhibit

seasonality which needs to be subjected to decomposition to separate the time series into its

component parts.

Seasonal time series contain a pattern which repeats itself, at least approximately, each

year (Levin, 2001). Thus, the trends of the monthly average PHP-USD exchange rates per year

for five years are illustrated to determine the highest and the lowest data points of each year. If

these points are not consistent or do not occur in about the same periods, the data points are

not seasonal. Hence, there is no need to subject them to decomposition.
http://www.investopedia.com/terms/e/exchangerate.asphttp://www.investopedia.com/terms/e/exchangerate.asp


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Figure 1: The Monthly Average Exchange Rate of a US Dollar in Terms of Philippine

Peso from 2009 to 2013

Table 1: The Peaks and Troughs of the Monthly Average Exchange Rate of a US

Dollar in Terms of Philippine Peso from 2009 to 2013

Year Limits

Peaks (High) Troughs (Low)

2009 March December

2010 July October

2011 January August

2012 January December

2013 December February

Table 1 is derived from the trends depicted in Figure 1.Analyzing this based on the

preceding paragraph, the high and low points of each year do not occur in about the same

months or the peaks and the troughs are not consistent. Therefore, the monthly average PHP-

USD exchange rates from 2009 to 2013 are not seasonal. Hence, the data points will not be

deseasonalized or be subjected to decomposition.

40

42

44

46

48

50

MonthlyAvera

geinPhilippine

Pe

so

Time (Month)

2009

2010

2011

2012

2013


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Analysis of Data

The forecasting tool used for the study was the Microsoft Office Excel 2013. To measure

forecasting accuracy, Mean Absolute Deviation (MAD), Mean Absolute Percentage Error

(MAPE) and Mean Square Error (MSE) were employed.

Five forecasting models or techniques were used in forecasting the monthly average

PHP-USD exchange rates. All the techniques utilized belonged to time-series models that

attempt to predict the future based on the past. The forecasting models used are the following:

Naive Forecasting

This is considered as the simplest forecasting technique and a benchmark model. This

uses the actual monthly average PHP-USD exchange rates for the past period as the

forecasted monthly average PHP-USD exchange rates for the next period. The naive

forecasting equation is given below.

Where:

= forecast for the next month

= observed value this month

Unweighted Moving Average (UWMA)

The study utilized the two-month, three-month and five-month moving averages

(Appendix). To obtain the best forecast among the three, the span of time that results to the

lowest MAD was chosen. For the two-month moving average, below is the formula used:


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Where:

= forecast for time period t+1

= actual value in time period t

= actual value in time period t-1

2 = number of months to average

For the three-month moving average, below is the formula used:

Where:






For the five-month moving average, below is the formula used:

Where:

5

...41

1

ttt

t

YYYF


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Weighted Moving Average (WMA)

The study utilized the two-month, three-month and five-month weighted movingaverages (Appendix). To obtain the best forecast among the three, the span of time that results

to the lowest MAD was chosen. For the two-month weighted moving average, the last months

average PHP-USD exchange rate is given a higher weight of two and the other monthly average

is given a weight of one. For the three-month weighted moving average, the last months

average PHP-USD exchange rate is given a higher weight of three and the other monthly

averages are given weights of two and one, respectively. For the five-month weighted moving

average, the last months average PHP-USD exchange rate is given a higher weight of five and

the other monthly averages are given weights of four, three, two and one, respectively. For thetwo-month moving average, below is the formula used:

Where:


= weight of two multiplied by the actual value in time period t

= actual value (having a weight of one) in time period t-1

3 = sum of weights

For the three-month moving average, below is the formula used:


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Where:


= weight of three multiplied by the actual value in time period t

= actual value (having a weight of two) in time period t-1


6 = sum of weights

Below is the formula used for the five-month weighted moving average:

Where:


5 = weight of 5 times actual value in time period t


15 = sum of weights

Simple Exponential Smoothing (SES)

The formula used to get the forecasted monthly average PHP-USD exchange rates

using this model is shown below. To obtain a good forecast the appropriate value for alpha was

15

1...4541

1

ttt

t

YYYF


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selected at 0.9. This was derived using the general approach by developing trial forecast with

different values of alpha and selecting the alpha that results in the lowest MAD. (See Appendix

29 and 30)

Where:

Ft+1 = new forecast (for time period t + 1)

Ft = previous forecast (for time period t)

= smoothing constant (0 1)

Yt = pervious periods actual demand

Trend Projection (TP)

The formula used to get the forecasted monthly average PHP-USD exchange rates

using this model is shown below. The independent variable is the time period (monthly) and the

dependent variable is the actual observed value in the time series (monthly average PHP-USD

exchange rates).

= b0+ b1X

Where:

= predicted value

b0 = intercept

b1 = slope of the line

)(1 tttt FYFF


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X = time period (i.e., X = 1, 2, 3, , n)

b1 = xy n x b0= b1x

x2n x2

Where:

= summation sign for n data points

x = values of independent variables

y = values of dependent variables

n = number of data points or observations

x = average of the values of the xs

= average of the values of the ys


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Table 2.Monthly Average PHP-USD Exchange Rates from 2009 to 2013

CHAPTER IV

PRESENTATION OF DATA

The PHPUSD exchange rate specifies how much one currency, the USD, is currently

worth in terms of the other, the PHP. Because exchange rates play such an important role in a

country's competiveness level, currency exchange rates are among the most analysed and

forecasted indicators in the world. The exchange rate is determined by the level of supply and

demand on the international markets.

The study focuses on forecasting monthly average PHP-USD exchange rates given the

data provided by the Bangko Sentral ng Pilipinas. The rates provided were weighted average

interbank rates.

The scope of the study will cover average monthly exchange rates for 5 years (from2009-2013).

MonthYear

2009 2010 2011 2012 2013

January 47.21 46.03 44.17 43.62 40.73

February 47.59 46.31 43.70 42.66 40.67

March 48.46 45.74 43.52 42.86 40.71April 48.22 44.63 43.24 42.70 41.14

May 47.52 45.60 43.13 42.85 41.30

June 47.91 46.30 43.37 42.78 42.91

July 48.15 46.32 42.81 41.91 43.35

August 48.16 45.18 42.42 42.04 43.86

September 48.14 44.31 43.02 41.75 43.83

October 46.85 43.44 43.45 41.45 43.18

November 47.03 43.49 43.27 41.12 43.55

December 46.42 43.95 43.64 41.01 44.10

From the table above, the monthly data values range from P40 to P49. The minimum

monthly data point is recorded during February 2013 (P40.67) while the maximum monthly data


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CHAPTER V

FORECAST AND ACCURACY TESTING

Time (Month) Monthly Average PHP-USD Exchange RatesUsing Different Forecasting Techniques

Year MonthActualData

Naive UWMA WMA SES TP

2009 Jan 47.21 - - - 44.15 47.33

Feb 47.59 47.21 - - 46.90 47.22

Mar 48.46 47.59 47.40 47.46 47.52 47.11

Apr 48.22 48.46 48.03 48.17 48.37 47.01

May 47.52 48.22 48.34 48.30 48.23 46.90

Jun 47.91 47.52 47.87 47.75 47.59 46.79

Jul 48.15 47.91 47.72 47.78 47.88 46.68Aug 48.16 48.15 48.03 48.07 48.12 46.57

Sept 48.14 48.16 48.16 48.16 48.16 46.47

Oct 46.85 48.14 48.15 48.15 48.14 46.36

Nov 47.03 46.85 47.50 47.28 46.98 46.25

Dec 46.42 47.03 46.94 46.97 47.02 46.14

2010 Jan 46.03 46.42 46.73 46.62 46.48 46.03

Feb 46.31 46.03 46.23 46.16 46.08 45.93

Mar 45.74 46.31 46.17 46.22 46.29 45.82

Apr 44.63 45.74 46.03 45.93 45.79 45.71May 45.60 44.63 45.19 45.00 44.75 45.60

Jun 46.30 45.60 45.12 45.28 45.51 45.49

Jul 46.32 46.30 45.95 46.07 46.22 45.39

Aug 45.18 46.32 46.31 46.31 46.31 45.28

Sept 44.31 45.18 45.75 45.56 45.29 45.17

Oct 43.44 44.31 44.75 44.60 44.41 45.06

Nov 43.49 43.44 43.88 43.73 43.54 44.95

Dec 43.95 43.49 43.47 43.47 43.49 44.85

2011 Jan 44.17 43.95 43.72 43.80 43.90 44.74Feb 43.70 44.17 44.06 44.10 44.14 44.63

Mar 43.52 43.70 43.94 43.86 43.74 44.52

Apr 43.24 43.52 43.61 43.58 43.54 44.41

May 43.13 43.24 43.38 43.33 43.27 44.31

Jun 43.37 43.13 43.19 43.17 43.14 44.20


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Table 3: Forecasted Monthly Average Exchange Rate of a US Dollar in Terms of

Philippine Peso from 2009 to 2013 Using Different Forecasting Models

Jul 42.81 43.37 43.25 43.29 43.35 44.09

Aug 42.42 42.81 43.09 43.00 42.86 43.98

Sept 43.02 42.42 42.62 42.55 42.46 43.87

Oct 43.45 43.02 42.72 42.82 42.96 43.77

Nov 43.27 43.45 43.24 43.31 43.40 43.66Dec 43.64 43.27 43.36 43.33 43.28 43.55

2012 Jan 43.62 43.64 43.46 43.52 43.60 43.44

Feb 42.66 43.62 43.63 43.63 43.62 43.33

Mar 42.86 42.66 43.14 42.98 42.76 43.23

Apr 42.70 42.86 42.76 42.79 42.85 43.12

May 42.85 42.70 42.78 42.75 42.71 43.01

Jun 42.78 42.85 42.78 42.80 42.84 42.90

Jul 41.91 42.78 42.82 42.80 42.79 42.79

Aug 42.04 41.91 42.35 42.20 42.00 42.69Sept 41.75 42.04 41.98 42.00 42.04 42.58

Oct 41.45 41.75 41.90 41.85 41.78 42.47

Nov 41.12 41.45 41.60 41.55 41.48 42.36

Dec 41.01 41.12 41.29 41.23 41.16 42.25

2013 Jan 40.73 41.01 41.07 41.05 41.02 42.15

Feb 40.67 40.73 40.87 40.82 40.76 42.04

Mar 40.71 40.67 40.70 40.69 40.68 41.93

Apr 41.14 40.71 40.69 40.70 40.71 41.82

May 41.3 41.1440.93 41.00 41.10 41.71

Jun 42.91 41.3 41.22 41.25 41.28 41.61

Jul 43.35 42.91 42.11 42.37 42.75 41.50

Aug 43.86 43.35 43.13 43.20 43.29 41.39

Sept 43.83 43.86 43.61 43.69 43.80 41.28

Oct 43.18 43.83 43.85 43.84 43.83 41.17

Nov 43.55 43.18 43.51 43.40 43.24 41.07

Dec 44.1 43.55 43.37 43.43 43.52 40.96


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Table 4: Test for Accuracy using MAD, MAPE and MSE for the Forecasted Monthly

Average Exchange Rate (PHP-USD) from 2009 to 2013

Tests for Accuracy

Naive UWMA WMA SES TP

MAD - 0.46 0.51 0.47 0.48 0.97

MAPE - 1.03% 1.16% 1.07% 1.08% 2.21%

MSE - 0.33 0.43 0.37 0.47 1.40

Interpreting the foregoing monthly and quarterly forecasts, it can be said that trends of

the forecasts using all the forecasting techniques except for the time-series regression are with

the actual data. Looking at the forecast accuracy measures on the other hand, all the models

has produced MAD and MSE lower than one for both the monthly and quarterly forecasts with

the exception of the time-series series regression. Hence, it can be said that the forecasting

techniques are useful in predicting future average PHP-USD exchange rates. As well in

analyzing the figures, PHP will lower its value for the next 24 months or eight quarters but will

surge up on the succeeding periods.

With regards to the forecasts of the time-series regression, they are heavily away from

the actual data with forecasts accuracy measures showing relatively huge quantities compared

to the others. This might be caused by the fact that only the warm-up samples are used in

determining the slope and the intercept of its linear trend line. Thus, the second half of the data

points are not considered causing high risks of deviations from the trend of the actual data.

Among the models, it is the smoothing nonlinear trends that has the lowest MAD, MAPE

and MSE and which means that it is the best technique to be used in forecasting the exchange

rates. This is caused by a trend-modification parameter phiand two smoothing constants that

minimize the MSE and eventually resulting to relatively small absolute errors (See Appendix 13

and Appendix 14).

Smoothing linear trends and simple exponential smoothing are desirable in forecasting

the said monthly and quarterly exchange rates because they make use of smoothing constants


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that minimize absolute errors. This is supported by the computed forecast accuracy measures

and as depicted in the figures as well.

Relating the forecasts to the study objectives, it can somehow be stated that it is

favorable to establish a PHP-USD exchange firm with the emerging peso value in the long run.

Entrepreneurs will be paying lesser peso compared to if the peso value increases that they will

pay greater amount as well.

Moreover given the forecasts, they can really serve in the understanding of the future

relations of the Philippine with the US. Aside from these two countries are allies of each other,

their economies can abound fairly because exchange rates are fluctuating in a fast pace as

shown in the figures. In this case, exchange rates may favor the US during the first few periods

but it will do the same for the Philippine in succeeding periods.

On imports and exports, the country may import large quantities of goods from the US

when the peso value is stronger the USD and this is good during the last periods of the forecast.

In this case, fewer peso is needed to purchase the goods. On the other hand, businesses may

tend to import less when peso value is high because it means as well that they need to pay

greater amounts for the products. This is true for the first periods in the forecasts.

Generally, the forecasts determined above using forecasting techniques can be utilized

in many other ways. In a way or another, direct or indirect, they will be able to serve as tools in

decision making and problem solving when it comes to financial matters domestically and more

especially internationally.


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CHAPTER VI

SUMMARY/ IMPLICATIONS

Based in the data given, it implies that the relationship of the Philippines and US would

be stronger considering the fluctuations in exchange rates that favor both countries at different

periods. Thus, the economic relations between these countries show a favorable scenario.

Holding things as constant, the forecasts show that average PHP-USD exchange rates would

continuously grow. Foreign currency exchange serves as a basis of an economy's well being. If

the PHP is strong compared to other countries, then it implies that the economy is in a good

situation. The data shows also that there are various fluctuations in the trend of foreign

exchange that are caused by economic factors such as interest rates and inflation. Fluctuating

exchange rates create uncertainty. Producers will be reluctant to buy international stock for fear

that its value will depreciate in the following months which is not good for a country's well being.

Uncertainty therefore acts as a disincentive to trade. Despite these fluctuations, the Philippine

economy is seen as stable and in the long run it would flourish. Strong currency exchange

attracts investors making the Philippine economy stable.

Foreign exchange rate affects the trade industry of the country. When USD is stronger

than the Philippine peso, then exporters are better off. Examples are the factors that determine

the relationship between PHP and USD. The Philippines require dollars to pay for their imports

of goods and services from US and to fund any investment they may wish to undertake in this

country. Assume that they obtain these dollars on the foreign exchange market by supplying


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peso in return. Then, the Philippine demand for dollars (mirrored by the supply of peso) is

determined by the exports to Philippine and our capital inflow from that country.

On the other side of the market, the US demand for peso is determined by our need to

pay for imports from Philippines, and for any capital investment that we undertake there. We

buy those yen by supplying US dollars in return. Thus, the supply of dollars (mirrored by the

demand for peso) is determined by our imports from Philippine and our capital outflow to that

country. In summary therefore, the demand for USD reflects the behavior of our exports and

capital inflow, while the supply of dollars reflects the behavior of our imports and capital outflow.

In other words, transactions on the foreign exchange market echo the international trade and

financial transactions that are summarized in the balance of payments. An increase in the

export industry is expected since the dollar is stronger than the peso. Moreover, remittances

from workers abroad will increase leading to higher revenue for the country. On the negative

side, it would discourage investors. When peso is weak, foreign businessmen would be hesitant

to invest. In addition, Filipinos would prefer to work abroad which would deplete skilled and

competent workers here in the country.

On the other hand and in conclusion, forecasts show different and contradicting results.

Given the forecasting results, the smoothing nonlinear trend implies a favorable outcome

compared to others as supported by its relatively low forecast accuracy measures. Therefore,

forecasting the monthly and quarterly average PHP-USD exchange rate using different

techniques is a good measure in solving financial problems.


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APPENDICES

Appendix 1. Naive Forecasting

Forecasted Monthly Average Exchange Rates of a US Dollar in Terms of Philippine Peso from 2009 to 2013

Year Month Actual Data (PHP) Forecast (PHP) Error

2009 January 47.21

February 47.59 47.21 0.38

March 48.46 47.59 0.87

April 48.22 48.46 -0.24

May 47.52 48.22 -0.7

June 47.91 47.52 0.39

July 48.15 47.91 0.24

August 48.16 48.15 0.01

September 48.14 48.16 -0.02

October 46.85 48.14 -1.29

November 47.03 46.85 0.18

December 46.42 47.03 -0.61

2010 January 46.03 46.42 -0.39

February 46.31 46.03 0.28

March 45.74 46.31 -0.57

April 44.63 45.74 -1.11

May 45.60 44.63 0.97

June 46.30 45.60 0.7

July 46.32 46.30 0.02

August 45.18 46.32 -1.14

September 44.31 45.18 -0.87

October 43.44 44.31 -0.87

November 43.49 43.44 0.05

December 43.95 43.49 0.46

2011 January 44.17 43.95 0.22

February 43.70 44.17 -0.47

March 43.52 43.70 -0.18

April 43.24 43.52 -0.28

May 43.13 43.24 -0.11

June 43.37 43.13 0.24

July 42.81 43.37 -0.56

August 42.42 42.81 -0.39

September 43.02 42.42 0.6

October 43.45 43.02 0.43

November 43.27 43.45 -0.18


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36.00

38.0040.0042.0044.0046.0048.0050.00

Jan-2009

Apr-2009

Jul-2009

Oct-2009

Jan-2010

Apr-2010

Jul-2010

Oct-2010

Jan-2011

Apr-2011

Jul-2011

Oct-2011

Jan-2012

Apr-2012

Jul-2012

Oct-2012

Jan-2013

Apr-2013

Jul-2013

Oct-2013

Exc

hangeRateinPeso

Time Period (Month-Year)

Actual Data Forecast

December 43.64 43.27 0.37

2012 January 43.62 43.64 -0.02

February 42.66 43.62 -0.96

March 42.86 42.66 0.2

April 42.70 42.86 -0.16

May 42.85 42.70 0.15

June 42.78 42.85 -0.07

July 41.91 42.78 -0.87

August 42.04 41.91 0.13

September 41.75 42.04 -0.29

October 41.45 41.75 -0.3

November 41.12 41.45 -0.33

December 41.01 41.12 -0.11

2013 January 40.73 41.01 -0.28

February 40.67 40.73 -0.06

March 40.71 40.67 0.04

April 41.14 40.71 0.43

May 41.3 41.14 0.16

June 42.91 41.3 1.61

July 43.35 42.91 0.44

August 43.86 43.35 0.51

September 43.83 43.86 -0.03

October 43.18 43.83 -0.65

November 43.55 43.18 0.37

December 44.1 43.55 0.55

MAD 0.46

MAPE 1.03%

MSE 0.33


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Appendix 2. Simple Moving Average (Two-Month)


Year Month Actual Data(PHP) Forecast(PHP) Error

2009 January 47.21

February 47.59

March 48.46 47.40 1.06

April 48.22 48.03 0.19May 47.52 48.34 -0.82June 47.91 47.87 0.04July 48.15 47.72 0.43

August 48.16 48.03 0.13September 48.14 48.16 -0.02

October 46.85 48.15 -1.30November 47.03 47.50 -0.47December 46.42 46.94 -0.52

2010 January 46.03 46.73 -0.70February 46.31 46.23 0.09

March 45.74 46.17 -0.43April 44.63 46.03 -1.40May 45.60 45.19 0.41June 46.30 45.12 1.19July 46.32 45.95 0.37

August 45.18 46.31 -1.13September 44.31 45.75 -1.44

October 43.44 44.75 -1.31November 43.49 43.88 -0.38December 43.95 43.47 0.48

2011 January 44.17 43.72 0.45February 43.70 44.06 -0.36

March 43.52 43.94 -0.41April 43.24 43.61 -0.37May 43.13 43.38 -0.25June 43.37 43.19 0.18July 42.81 43.25 -0.44

August 42.42 43.09 -0.67September 43.02 42.62 0.41


26/30

October 43.45 42.72 0.73November 43.27 43.24 0.04December 43.64 43.36 0.28


March 42.86 43.14 -0.28April 42.70 42.76 -0.06May 42.85 42.78 0.07June 42.78 42.78 0.00July 41.91 42.82 -0.91



2013 January 40.73 41.07 -0.34February 40.67 40.87 -0.20

March 40.71 40.70 0.01April 41.14 40.69 0.45May 41.3 40.93 0.38June 42.91 41.22 1.69July 43.35 42.11 1.25

August 43.86 43.13 0.73September 43.83 43.61 0.22

October 43.18 43.85 -0.66November 43.55 43.51 0.05December 44.1 43.37 0.74

MAD 0.51

MAPE 1.16%

MSE 0.43


27/30

Appendix 3. Weighted Moving Averages (Two-Month)

Forecasted Monthly Average Exchange Rate of a US Dollar in Terms of Philippine Peso from 2009 to 2013


2009 January 47.21

February 47.59

March 48.46 47.46 1.00

April 48.22 48.17 0.05May 47.52 48.30 -0.78June 47.91 47.75 0.16July 48.15 47.78 0.37








March 43.52 43.86 -0.34April 43.24 43.58 -0.34May 43.13 43.33 -0.20June 43.37 43.17 0.20July 42.81 43.29 -0.48

August 42.42 43.00 -0.58September 43.02 42.55 0.47


28/30

October 43.45 42.82 0.63November 43.27 43.31 -0.04December 43.64 43.33 0.31


March 42.86 42.98 -0.12April 42.70 42.79 -0.09May 42.85 42.75 0.10June 42.78 42.80 -0.02July 41.91 42.80 -0.89







MAD 0.47

MAPE 1.07%

MSE 0.37

Appendix 4. Simple Exponential Smoothing (= 0.90)


29/30



2009 January 47.21 44.15 3.06

February 47.59 46.90 0.69March 48.46 47.52 0.94April 48.22 48.37 -0.15May 47.52 48.23 -0.71June 47.91 47.59 0.32July 48.15 47.88 0.27


October 46.85 48.14 -1.29November 47.03 46.98 0.05December 46.42

47.02 -0.60






March 43.52 43.74 -0.22April 43.24 43.54 -0.30May 43.13 43.27 -0.14June 43.37 43.14 0.23July

42.81

43.35 -0.54August 42.42 42.86 -0.44

September 43.02 42.46 0.56October 43.45 42.96 0.49

November 43.27 43.40 -0.13December 43.64 43.28 0.36

2012 January 43.62 43.60 0.02


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February 42.66 43.62 -0.96March 42.86 42.76 0.10April 42.70 42.85 -0.15May 42.85 42.71 0.14June 42.78 42.84 -0.06July 41.91 42.79 -0.88







MAD 0.481

MAPE 1.08%

MSE 0.473

finace forecasting paper

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