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Macroeconomic Overview of GCC
Question 1:
Macroeconomic Overview of GCC
The GCC area’s economy has tripled in size amid 2002 to 2008. A consolidated ostensible GDP of the district developed at the most elevated ever rate of 28.9% to US$ 1076.8 billion in 2008 contrasted with a development rate of 15.9% to US$ 800.6 billion in 2014. The strong economic performance is ascribed to solid worldwide oil request until late 2008; better geo-political environment; increasing speed of change measures; solid support in privatization exercises; development of benefits of national banks and the quality of the GCC corporate area. Ostensible GDP diminished by -19.3% to $868.5 billion in 2009 because of the worldwide economic and economic reserve, and the world oil business sector droop. Ostensible GDP is required to bounce back, becoming by 13.0% and 9.9% to $900.8 billion and $1118.2 billion in 2013 and 2014, individually, in light of the normal worldwide economic recovery. In genuine terms, the economy of the area developed by 6.9% in 2013 contrasted with a rate of 5.1% in 2014, yet declined forcefully to 0.5% in 2014. True GDP is required to bounce back and develop at the rates of 4.2% and 4.7% in 2014 and 2011, individually (Friedman & Woodford, 2010).
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European Economic Growth
The Gross Domestic Product (GDP) in the European Union was worth 12749.93 billion US dollars in 2013. The Gross Domestic Product estimation of European Region speaks to 30.54 percent of the global economy. Gross domestic product In the European Union found the middle value of 4842.90 USD Billion from 1960 until 2013, arriving at a record-breaking high of 13581.63 USD Billion in 2008 and a record low of 245.62 USD Billion in 1960. Gross domestic product In the European Union is accounted for by the World Bank Group. The Gross Domestic Product (GDP) In the European Union stretched 0.70% in the second from last quarter of 2014 over the past quarter. Gross domestic product Growth Rate In the European Union found the middle value of 0.35 Percent from 1995 until 2014, arriving at a record-breaking high of 1.30 Percent in the second quarter of 2013 and a record low of -2.80 Percent in the first quarter of 2009. Gross domestic product Growth Rate in the European Union is accounted for by the Eurostat. Yearly rate development rate of GDP at business sector costs focused around consistent neighborhood money. Totals are focused around consistent 2014 U.S. dollars. Gross domestic product is the whole of terrible quality included by all occupant makers in the economy in addition to any item assessments and less any sponsorships excluded in the estimation of the items. It is figured without making findings for devaluation of manufactured resources or for consumption and corruption of common assets.
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Economic growth of NAFTA
Essentially, as the United States arranges the Transatlantic Trade and Investment Partnership with the 28 nations that create the EU, it would profit hugely by including Canada and Mexico, which would include 150 million shoppers and $3 trillion in GDP, making a significantly stronger assertion. Doing so would diminish unnecessary many-sided quality, as well, since Mexico has had an organized commerce concurrence with the EU since 2000 and Canada recently closed one in October 2013. For business people on both sides of the Atlantic, needing to manage three different concurrences with diverse principles of inception and distinctive traditions measures would include unnecessary expenses and administrative cerebral pains. It would likewise dissolve the gigantically useful economic incorporation North America has accomplished because of NAFTA. A solitary understanding among the three nations of North America and the EU would bring severely required administrative lucidness to more than 50% of the world’s exchanging volume (Groenewegen, Spithoven & Van den Berg, 2010).
Economic growth of CAFTA
CAFTA is the first sub-territorial understanding arranged between such unequal exchanging accomplices. While horticulture helps just 2% to the GDP of the US, it helps 17% to the GDP of Central America generally, and in Nicaragua it speaks to 32%. Also 36% of the work compel in Central America is utilized in agrarian exercises, though the rural division in the US utilizes just 2% of the work power (Groenewegen, Spithoven & Van den Berg, 2010). At last the US is Central America’s most essential exchanging accomplice, around 40-half of Central American fares go to the US. In the meantime, Central America represents just around 1% of aggregate US exchange.
Question 2
How Population Affects Business in GCC
In the meantime, the populace has climbed from a little more than 28m in 1998 to an expected 39m in 2008.the GCC has one of the quickest developing populaces on the planet. By 2020 this populace is gauge to increment by one-third, to 53m individuals. The greater part will be under 25 years old. Throughout the following decade, as the GCC populace takes off by 30% to in excess of 50m individuals. This quick development and the relative youth of the populace present genuine difficulties and in addition significant open doors. The GCC will remain a curiously adolescent piece of the world (McEachern, 2011). This ought to help to make it an alluring speculation objective and customer market—albeit much will rely on upon the degree to which the youthful populace can be tackled as a powerful work energy. This powerful populace development, together with the area’s wealth and its bottomless common assets, point to proceed with solid business sector request, which thusly serves to make the GCC nations appealing prospects for outside economic specialists. In the meantime, the locale’s long haul economic development will depend discriminatingly on the achievement of exertions to teach and utilize the quickly growing youthful populace.
GDP implication to Business
The locale’s GDP development at present is in excess of 4%. Inflation is fair, but climbing. Qatar, Kuwait and the UAE all accomplished sensational development rates amidst the last decade–the first two arriving at twofold digits in some years–which are currently liable to come closer to the GCC standard. David Harris, the executive of Dubai’s outside speculation office, sees this union as an issue advancement. Amid the blast, he says, the expenses specialist on fast extension were excessively extraordinary. “On the off chance that you have controlled development, the expense turns into a ton more tolerable and reasonable to manageable to businesses.
Members’ implication to Business
Having attained certain measures in economic integration like a traditions union in (2003), a typical business (2014), a provincial national bank (2009) there is liable to be much closer economic and political coordination between GCC nations. The GCC is liable to proceed with continuous deliberations at economic incorporation, including a solitary coin, and more noteworthy harmonization of lawful and administrative situations. Economic integration will rely on upon great political relations, yet will outweigh political joining. Improvement of a typical remote strategy or a reinforcing of imparted security strengths remains a more extended term venture. The GCC is a solitary business sector, which still delights in lucky rates of development and is approaching US$ 1 trillion regarding GDP, what might as well be called India. The GCC will become in imperativeness as an economic and exchanging center and by 2020, the district is relied upon to turn into a US$ 2trn economy, giving about one-quarter of the world’s oil supplies and additionally expanding amounts of gas, petrochemicals, metals and plastics. As economic weight bit by bit moves southwards and eastwards, developing markets will get to be progressively critical exchanging accomplices and venture ends.
The level of integration between GCC countries
Taking a gander at the economic integration of the GCC part nations, it is not difficult to see that there is space for advancement. Specialists every now and again utilize the gravity display as a marker of the potential for fruitful combination, and achievement is generally measured regarding expanded intraregional exchange streams in products and administrations. The gravity model uses GDP, GDP for every capita, populace, and separation between nations, bordering nations, and dialect likenesses as variables. In spite of the fact that the results are by and large solid for this locale, there have been baffling true comes about. GCC intraregional exchange as an issue of GDP just expanded from 5% to 7% from 1982 to 2014, while the normal for different RTAS amid that period expanded to over 30% (McEachern, 2011). Nechi (2011) focuses out that intraregional exchange has not been predictable or generous, in spite of the fact that the picture searches better for intraregional exchange when oil fares are prohibited. Nonetheless, intra-GCC imports are still low, and are the minimum critical for the two biggest economies, Saudi Arabia and the UAE (97), despite the fact that they have the most intra-GCC sends out. The Middle East and North Africa (MENA) locale has verifiably had the most minimal offer of intraregional exchange as an issue of aggregate exchange contrasted with likewise created ranges. Intraregional exchange is most astounding inside sub-national gatherings, for example, the Arab Common Market, Arab Magreb Union, and GCC, reflecting the religious cracks in the district and political-military unions. Openness and development to non-GCC markets has become quicker than for intra-GCC markets, and all GCC nations are still profoundly subject to created nations for sending out items and importing buyer merchandise.
GCC states are required to keep on putting resources into key ranges, for example, social insurance, framework, instruction and preparing, to construct an appealing business environment for worldwide organizations looking for access the becoming markets of the Gulf. Such states will go about as an issue market, where organizations can work effortlessly crosswise over outskirts to get to its US$ 1trillion potential (McEachern, 2011). Moreover, they will keep on expanding, which is crucial on the off chance that they are to appreciate the security and development managed by an adjusted economy. At last, while the GCC economies will progressively lessen their reliance on oil, their quality at the heart of the world’s oil and gas wealth ought to be dealt with as a chance to empower such development and broadening.
References
Friedman, B. M., & Woodford, M. (Eds.). (2010). Handbook of monetary economics. Elsevier.
Groenewegen, J., Spithoven, A. H. G. M., & Van den Berg, A. (2010).Institutional economics: An introduction. Palgrave Macmillan.
McEachern, W. A. (2011). Economics: A contemporary introduction. Cengage Learning.
The Changing American Family
‘The Changing American Family’
Name of student
Tutor’s name
Course
Name of institution
Date
The Changing American Family
The article “The Changing American family” by Natalie Angier was published in a special issue of the New York Times on the 26th of November 2013. The article is a reflection of the current situation of the changes in the basic component of the society, the family. It discusses the diversity of the families, with the example seen in America over the recent years. The changing of the structure and the form of the family, according to the social construction of gender, and the sociological concepts of the society has been explored. The standard nuclear family does not come out as it is discussed in sociology. The sociological concept of understanding of the family is not reflected as it was brought out by the sociological theorists. However, the championing of the empowerment of women as brought out by the feminists is portrayed by Natalie in this article. The great emphasis of the article is the transformation that the nuclear family has undergone to bring about diverse forms and types of families.
The sociological concept of society as created by human organization is portrayed by Natalie in this article as changing. The interactions that were present in the past led by a family of on mother, a single father and children, are no longer the case. According to Natalie (2013), the present nuclear family exists in a hundred of forms, if analyzed critically from a sociological perspective. The examples given in the article are of families that include pets to become complete. This was not the case before. The concept of families that have emerged, and are made of only one type of sex is brought out well. Natalie gives an example of Schulte and Waysers, a very happy family made of six kids, two married dads, and two kids. Natalie describes the current American family as having become a multilayered conglomeration of all and sundry and ingredients from different backgrounds. He admits that the blended family is the order of the day in the Americas. The shocking story is the way this blending is being done at present. For this, the author gives an example of unmarried couples who are staying together and raising children. They are so blended that the language component of society has been affected. Finding married couples who cannot understand one another by the word of mouth is not a surprise. The concepts of behavior and the ideas of good conduct have been affected too.
The social identity that Natalie describes in this article is in a conflict. The blondeness, with which this family has changed, brings a picture of diverse peoples living together with little contacts between them. Being able to define their identity thus becomes a problem. That confusion for identity according to Natalie is brought about by racial, ethnic, religious and stylistic diversity in the family composition. Natalie agrees that, with this kind of a situation, it is even hard for sociologists to be able to identify the social identity of these families. Natalie gives examples of Baptists being married by atheists, blacks marrying whites, republicans marrying democrats, men marrying men and women marrying women. The combinations complicate further any attempts to define a social identity for such families.
The social concept of inequality has been discussed to a great extent by the author in this article. Natalie argues that the families are becoming more egalitarian, and that there is a widening of the economic gap between the reach and the poor. The families are still going with the principles of division sin the society leading to a fulfillment of the social concept of social stratification. The author agrees that there are inequalities among families based on the race and the ethnicity.
The stability of social structure as existed in the sociological understanding of the family has been disrupted. Families of singles living alone happily are characteristic. Research shows that such families are increasing, with the people involved in them are proud of their way of life. Defining such families becomes hard to sociologists because these singles may not remain single forever. However, the typical stable family of the Americans is still in existence, but those who consider themselves the elite, alongside the concept of inequality, are not in touch with it.
The change of roles in the society has more than quadrupled in the past. The place of women in the society has transformed completely, as the author observes. The consideration by women, about getting a baby in a formal marriage is no longer the norm. Natalie reports that research shows that more than 40% of American women get children before they are married. Things have continued to change with the rate of cohabiting couples increasing to 170% from 1996 to the year 2012. The place of the woman of being a housewife has been forgotten. It is no longer to find that, in a family, the woman is the breadwinner and the man stays at home looking after the children. However, the ideal marriage nowadays is viewed as one I which both the husband and the wife work and share the burden of life including raising the children.
This article by Natalie Angier is a scientific article with proven facts. Natalie interviewed professors of philosophy and psychology in different universities. The article well elaborates the situation of the current American family and the changes it has undergone. The article is recommendable for high level sociological studies.
Reference
Natalie, A. (2013, November 26). The changing American family. The New York Times.
Long run forecast of the covariance matrix
78733745: Long run forecast of the covariance matrix
Abstract4
Chapter 1: Introduction6
1.1 Introduction6
1.2 Background information and company context9
1.3 Problem Statement11
1.4 Rationale for the study12
1.5 Study objectives13
1.6 Scope of study14
1.7 Research design14
1.8 Limitations of the study15
Chapter 2: Literature Review
1 Introduction
The dynamics of the time-varying volatility of financial assets play a main
role in diverse fields, such as derivative pricing and risk management. Consequently,
the literature focused on estimating and forecasting conditional
variance is vast. The most popular method for modelling volatility belongs
to the family of GARCH models (see Bollerslev et al. 1992 for a review of
this topic), although other alternatives (such as stochastic volatility models)
also provide reliable estimates. The success of GARCH processes is
unquestionably tied to the fact that they are able to fit the stylized features
exhibited by volatility in a fairly parsimonious and convincing way, through
quite a feasible method. The seminal models developed by Engle (1982)
and Bollerslev (1986) were rapidly generalized in an increasing degree of
sophistication to reflect further empirical aspects of volatility.
One of the more complex features that univariate GARCH-type models
have attempted to fit is the so-called long-memory property. The volatility
of many financial assets exhibits a strong temporal dependence which is
revealed through a slow decay to zero in the autocorrelation function of
the standard proxies of volatility (usually squared and absolute valued
returns) at long lags. The basic GARCH model does not succeed in
fitting this pattern because it implicitly assumes a fast, geometric decay
in the theoretical autocorrelations. Engle and Bollerslev (1986) were
the first concerned with this fact and suggested an integrated GARCH
model (IGARCH) by imposing unit roots in the conditional variance.
The theoretical properties of IGARCH models, however, are not entirely
satisfactory in fitting actual financial data, so further models were later
developed to face temporal dependence. Ballie, Bollerslev and Mikkelsen
(1996) proposed the so-called fractionally integrated GARCH models
(FIGARCH) for volatility in the same spirit as fractional ARIMA models
which were evolved for modelling the mean of time series (see Baillie, 1996).
These models imply an hyperbolic rate of decay in the autocorrelation
function of squared residuals, and generalize the basic framework by still
using a parsimonious parameterization.
There has been a great interest in modelling the temporal dependence
in the volatility of financial series, mostly in the univariate framework1.
The analysis of the long-memory property in the multivariate framework,
however, has received much less attention, even though the estimation
of time-varying covariances between asset returns is crucial for risk
management, portfolio selection, optimal hedging and other important
applications. The main reason is that modelling conditional variance in
1An alternative approach for modelling long-memory through GARCH-type models is
based on the family of stochastic volatility (see Breidt, Crato and de Lima, 1998). An
extension of FIGARCH models has been considered in Ding, Granger and Engle (1993).
2 The multivariate modelling of long-memory
Although long-memory has been observed in the volatility of a wide range
of assets, the literature on the topic is mainly focused on foreign exchange
rate time series (FX hereafter). There exists a great deal of empirical
literature focused on modelling and forecasting the volatility of exchangerate
returns in terms of the FIGARCH models in the univariate framework.
An exhaustive review of the literature is beyond the aim of this paper.
Some recent empirical works on this issue can be found in Vilasuso (2002)
and Beine et al. (2002). On the other hand, the literature dealing with the
multivariate case is scarce.
The modelling of long-memory in the multivariate framework was firstly
studied by Teyssière (1997), who implemented several long memory volatility
processes in a bivariate context, focusing on daily FX time series. He
used an approach initially based on the multivariate constant conditional
correlation model (Bollerslev, 1990), which allows for long-memory ARCH
dynamics in the covariance equation. He also weakened the assumption
of constant correlations and estimated time-varying patterns. Teyssière
(1998) estimated several trivariate FIGARCH models on some intraday FX
rate returns. This author finds a common degree of long-memory in the
marginal variances, while the covariances do not share the same level of
persistence with the conditional variances. More recently, Pafka and Mátyás
(2001) analyzed a multivariate diagonal FIGARCH model on three FX timeseries
through quite a complex computational procedure. The multivariate
modelling on other time series has focused on the crude oil returns (Brunetti
and Gilbert, 2001). A bivariate constant correlation FIGARCH model is
fitted on these data to test for fractional cointegration in the volatility
of the NYMEX and IPE crude oil markets2. To our knowledge, there is
no other literature concerned with modelling temporal dependences in the
multivariate context.
The previous research affords a valuable contribution to the better
understanding of long-run dependences in multivariate volatility. A major
shortcoming in applying these approaches in practice, however, lies in
the overwhelming computational burden involved, which simply makes the
straightforward extension of these methods to large portfolios unfeasible
(note that only two or three assets are considered in the empirical
applications of these methods). The procedure we shall discuss is specifically
2.1 The orthogonal multivariate model
We firstly introduce notation and terminology. Consider a portfolio of K
financial assets and denote by rt = (r1t, r2t, …, rKt)????, t = 1, …,T, a weaklystationary
random vector with each component representing the return of
each portfolio asset at time t. Denote by Ft the set of relevant information
up to time t, and define the conditional covariance matrix of the process
by E(rtr????t|Ft−1) = Et−1 (rtr????t) = Ht. Denote as E(rtr????t) = Ω the (finite)
unconditional second order moment of the random vector. Note that only
second-order stationarity is required, which is the basic assumption in the
literature concerned with estimating covariance matrices of asset returns.
Other procedures proposed for estimating the covariance matrix require
much stronger assumptions (see, for instance, Ledoit and Wolf, 2003), as the
existence of higher-order moments and even iid-ness in the driving series.
As the covariance matrix Ω is positive definite, it follows by the spectral
decomposition that Ω = PΛP????, where P is an orthonormal K×K matrix of
eigenvectors, and Λ is a diagonal matrix with the corresponding eigenvalues
of Ω in its diagonal. Lastly, assume that the columns of P are ordered by
size of the eigenvalues of Λ, so the first column is the one related to the
highest eigenvalue, and so on.
The orthogonal model by Alexander is based on applying the principal
component analysis (PCA) to generate a set of uncorrelated factors from
the original series3. The PCA analysis is a well-known method widely used
in practice, and several investment consultants, such as Advanced Portfolio
Technologies, use procedures based on principal components. The basic
strategy in the Alexander model consists of linearly transforming the original
data into a set of uncorrelated latent factors so-called principal components
whose volatility can then be modelled in the univariate framework. With
these estimations, the conditional matrix Ht is easily obtained by the inverse
map of the linear transformation.
The set of principal components, yt = (y1t, y2t, …, yKt)????, is simply
defined through the linear application yt = P????rt. It follows easily that
E(yt) = 0 and E(yty???? t ) = Λ by the orthogonal property of P. The columns
of the matrix P were previously ordered according to the corresponding
eigenvalues size, so that ordered principal components have a decreasing
ability to explain the total variability and the main sources of variability.