Doing factor analysis in stata forex

Published в Crypto making money off volume rates | Октябрь 2, 2012

doing factor analysis in stata forex

Findings suggest that investors do not rely on their own Analysis is run in STATA using Newey–West standard errors and other time-series. The Little () test was used to determine whether data for individual items were missing completely at random (MCAR) using Stata's 'mcartest'. by Liu Wei; S CONFA: Stata module to perform confirmatory factor analysis modeling by Stanislav Kolenikov; S SHUFFLEVAR: Stata module to shuffle. FOREX SIGNAL SERVICE AUSTRALIA TIME

Then the reserves is optimal again. The foreign exchange reserves are excess a lot in But it is in the optimal scale in Then the writer will explore the factors affecting the forex reserves scale. The Factors Considered The scale of forex reserves can be affected by many factors, such as the economic environment factors, international trade factors, the global investment environment factors and so on.

In this thesis, the writer chooses some important factors and does quantitative analysis about these factors. Generally speaking, trade surplus or trade deficit can directly affect the scale of forex reserves. The amount of the exports and imports can have a large effect on the scale of forex reserves.

According to the international experience, the scale of foreign exchange has a positive correlation with the exports and a negative correlation with the imports. When the export is larger, the scale of forex reserves should be larger with other conditions unchanged. And when the import is larger, the scale of forex reserves should be smaller with other conditions unchanged.

The foreign direct investment also has a high correlation with the forex reserves. On one hand, a foreign direct investment FDI is an investment in the form of a controlling ownership in a business in one country by an entity based in another country. If the foreign direct investment increases in one country, it can enlarge the scale of the forex reserves.

On the other hand, if the foreign direct investment is larger, the foreign investors will also export some profit which is earned on the investment to their own countries. The country should hold a part of reserves to avoid the fluctuations caused by these activities. Theoretically speaking, the amount of the external debt also can affect the scale of forex reserves. If one country has a large debt, it will need to hold enough forex reserves to pay off the debt or the interest on the debt on time.

The scale of the forex reserves should be adjusted according to the amount of debt. The gross domestic product of one country can influence the foreign exchange reserves as well. The increase in GDP shows that the country has a good economic environment, which attracts the investment of other countries. And the increase in GDP also shows that national income is increasing.

It means that the disposable income of the resident is increasing. And the residents in that country will be much eager for holding foreign currencies, such as for travelling abroad, studying abroad and so on. So the GDP indicators can be an important factors affecting the scale of forex reserves. The last factor the writer thinks can affect the scale of forex reserves is the foreign exchange rate system.

In the development of RMB exchange rate system, the writer thinks the most important turning point is the event happened in Before , there are two foreign exchange rates in China. One is called the official exchange rate, another is called market exchange rate.

The official exchange rate is settled exchange rate between banks. And the market exchange rate is the trading exchange rate between banks and enterprises retained foreign exchange. And on 1st January , there is a new foreign exchange system which is based on the supply and demand in the market established in China.

In the new system, the foreign exchange rate is single and floating. After , there is only one exchange rate in China. As for how to test the effect of foreign exchange system on the scale of forex reserves, the writer will introduce the dummy variable. Before , the dummy variable is 0, and after , the dummy variable is 1. Although there are many other factors affecting the scale of forex reserves, the writer only considers these factors above in the thesis.

And the writer thinks the factors above can well explain the scale of forex reserves. And to avoid heteroscedasticity and the large fluctuation of the variables, the writer takes the logarithm of these economic variables to run some tests. The writer will use these five indicators to analyze the factor affection.

The sample interval the writer chooses is from to There are totally 30 statistics in the thesis. Unit Root Test The data selected is time series variables. And for time series variables, it is necessary to exam the stationarity of the variables. In this thesis, the writer will use Augmented Dicky Fuller ADF unit root test to test whether the variables are stationary or non-stationary.

In statistics, a unit root test exams whether a time series variables is non-stationary and possesses a unit root. The null hypothesis is generally defined as the presence of s unit root and the alternative hypothesis is either stationary, trend stationary or explosive root depending on the test used. The results are shown in Table 3. It can be illustrated that these variables are integrated of order 1. So the writer can do the co-integration test with these variables to exam whether there exists a long term relationship between them.

Co-Integration Test In this thesis, there are five independent variables. The writer uses the Johansen test to do the co-integration test. The Johansen test is a procedure for testing co-integration of several I 1 time series. This test permits more than one co-integrating relationship so is more generally applicable than the Engle-Granger test which is based on the Dickey-Fuller or the augmented test for unit roots in the residuals from a single estimated co-integrating relationship.

It is essential to determine the right lag order. According to the conclusion that the optimal lag order of Johansen co-integration test is equal to the optimal lag order of unconstrained VAR model minus 1, the writer firstly determines the lag order of unconstrained VAR. In Pakistan, exchange rate is highly affected by increased imports, political situations and the labour population residing in other countries.

This motivates home country investors to invest in foreign currencies. In different times, market displays unexpected returns due to behaviours of investors. Considering this issue, the aim of this study is to expand literature on herding, by exploring herding behaviour in Pakistani Forex market.

Researchers have found no other study specifically contributing towards herding literature in currencies in Pakistan. Currencies under study are listed for trading in top platforms. Herding behaviour of home country investors is analysed in current study.

Thus, our empirical analysis uses prices of six currencies, over a period of 5 years and examines whether these currencies exhibit herding behaviour or not. This research paper is organized in five sections. Introduction of the article talks about behavioural finance and the need of studying herding behaviour in Pakistan forex market.

Literature review builds up theoretical background and describes empirical literature on herding. Methodology section contains description of models to be analysed, statistical and estimation techniques. Section 4 discusses results of estimations. At the end, conclusion is presented in last section. Literature review Theoretical literature review The roots of efficient market hypothesis EMH are traced in the work of Fama and Samuelson Any decision taken by investor fully reflects the true condition of market Hamid et al.

The weak form of efficiency suggests that prices display random walk as they reflect all historical information. Semi-strong form of efficiency suggests that prices of securities show historical information as well as any new public information. So there is no way that an investor can earn more profit by any technical or fundamental analysis. Strong form of EMH incorporates all historical information weak form of EMH , public information semi-strong form of EMH and any kind of private information regarding assets.

In this way, all kinds of information are readily available to investors and they can make decisions rationally. In finance literature, there has been much debate on the validity of EMH and this debate dates back to origin of this concept Delcey There is one stream of literature that validates its authenticity while other poses questions on the efficiency of markets.

Investors do not pay attention to market information which causes under-pricing of securities. Moreover, the models which reject EMH itself are biased and provide erroneous results. Another proponent of random walk Malkiel also argues that economic data must be significant to provide substantial results for EMH.

While the work on EMH in finance went on, there emerged another field of behavioural finance. This presented the fact that human mind controls the decision-making of investors. Behavioural finance answers the questions that how and why markets become inefficient despite the availability of information Hong This field has gained much attention in recent years and it grabs concepts from sociology and psychology Ahmed and Karira Research in this area has led to the concepts of heuristics, informational cascades and herding.

People tend to follow the choices of others in markets and thus base their decisions on their beliefs. Avery et al. Considering these two concepts related to investment decision, authors of this research article have tried to explore the fact whether traded currencies in Pakistan show herding behaviour imitating trends among investors or they follow principles of EMH.

Empirical literature review Herding behaviour has received wider attention of researchers in economics and finance Ballis and Drakos There are different opinions of researchers regarding the presence of herd behaviour in varying times and markets. For example, Chang et al.

But existence of herd behaviour in different developed countries was supported by Chiang and Zheng and Khan et al. Chang et al. Study of Chiang and Zheng is in contrast to previous studies of Chang et al. There is a variety of literature on herding regarding stocks in markets. Rompotis examined exchange-traded funds ETFs for herding behaviour. They found no evidence in support of biasness among investors regarding investment in ETFs.

Christie and Huang explained that when individual returns of equities follow principal returns of portfolios, they show herding behaviour. Investors depend upon informational cascades and sentiments for investment in crypto currencies. Asad et al.

In another study by Mahmood et al. In this regard, they found that heuristics and herding are positively related to investment decisions. If prices of stocks adjust all the available information, then herding behaviour tends to disappear Romano It implies that herding behaviour occurs due to asymmetry of information in market.

There is another stream of literature which finds out herding effect during some specific time period. For example, Gavriilidis et al. Some research articles also use primary data from investors to test herding behaviour. For example, Shaikh found that professional investors are more prone to overconfidence and herding while investing in securities.

He also reported that older investors show more herd behaviour than younger ones. Javaira and Hassan analysed stock returns of Karachi Stock Exchange in period of — They found no support for the presence of herding behaviour among investors. Only support was found in year , when there was liquidity crisis and information asymmetry. Zafar and Hassan tested herding in Karachi Stock Exchange for the period of to and found evidence in favour of herding in both up and down markets.

In a similar nature of study by Ahmed and Karira , there is no evidence of herd behaviour in Pakistan Stock Exchange. This study used daily market returns of stocks from period of to Their results showed herding behaviour only in four sectors of Pakistan Stock Exchange. Another study by Yousaf et al. Shah et al. There are a few studies available which test the presence of herding among currencies.

Sherman studied herd behaviour in currencies.

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