The AFL Canadian Labor, National Identity, and...

The AFL Canadian: Labor, National Identity, and Transnational Discourse 1936-1955 â€Å"The American Federation of Labor is an American organization,† declared William Green, president of the AFL, in his 1947 keynote speech, â€Å"It believe[d] in American, the fundamental law of the United States, the Constitution, freedom, liberty and democracy. We will have nothing to do with Communism in any shape, or form ... This sixty-sixth convention will redeclare its opposition to Communism and to Communist philosophy, and ... to [those who would] attempt to establish it among the organized labor of our country.† Though Green declared â€Å"Communism abhorrent to American labor† not all the members of the AFL were American. Indeed, Canadians and their†¦show more content†¦The AFL used anti-Communist ideology as method of furthering, their own, American economic interests. For all its supposed non-partisan domestic politics, the AFL leadership was invested in American economic supremacy. Economic and political spheres of power can not be so n eatly decomposed. The expansion of U.S. economic power in the post-war period necessarily had politics embedded within it. The AFL’s associations with the CIA and State Department in order to defend American corporations, with, naturally, associated windfalls for American labor. In Canada, as Gary Marcuse points out, â€Å"the rebellious dissidents in the unions often voiced the emergent demands for greater national autonomy, and the purge of the dissidents was intimately linked with the suppression of that nationalism.† The AFL’s choice of discourse furthered their economic ends. In order to appreciate the impact of these international developments, it is important to look at local causes and effects. It is the process of emergent nationalism and its relationship to economic action that interests me. I wish to examine, at a very local level, the relationship between economic sovereignty and cultural identity among the rank-and-file membership of the Toronto AFL locals in the W.W.II and post-war period. How do Canadians with a rising sense of nationalism understand themselves and their roles within a decidedly American institution? The relationship of the Canadian and the American is often

Roman Architecture Essay - 2647 Words

Roman Architecture Many centuries before the birth of Christ, the city of Rome grew, prospered, and developed into a thriving Republic. As in most cultures, Romes buildings became more elaborate and impressive. They developed fantastic building technologies and ideas. The feats of Roman engineers were groundbreaking, and many structures built by this culture still stand today. With knowledge borrowed from the Greeks, Rome made impressive architectural achievements, these were namely major attributes of buildings, colossal structures, and a legacy that would influence later buildings (Cornell and Matthews 11). According to legend, the city of Rome was founded in about 753 BC, by a group of shepherds. It sat at an ideal†¦show more content†¦The ancient Romans created and borrowed fundamental types of concepts that made up buildings. The ideas that the Romans borrowed were basic ideas such as the column. A column is a vertical shaped pillar with the chief design concern of supporting a building. Most columns consist of three parts, the base, the shaft, and the capital. The shaft is usually cylindrical in shape. The Greeks had three basic types of columns, Doric, Ionic, and Corinthian. All three types have narrow fillets on them. These were small vertical slits that ran the length of the column. The Romans modified the column and added two types, Truscan and Composile. The columns became widely used in homes and temples in Greece and later in Rome (Architecture). The Romans also borrowed from the Greeks other major structural designs. On the top of a column on most temples and public buildings rested an Entablature. This is a classic triangular shaped faà §ade, or front of a building. The Entablature consists of four parts. The lowest part is the Architrave, which sits on top of the capital or upper part of a column. On top of that, the frieze was typically decorated with horizontal bands. The Cornice forms the upper part of the Entablature and extols beyond the frieze on the sides. On the very top sits a Pediment, a triangular segment between the lower Entablature and the roof (Architecture). The Romans borrowed theShow MoreRelatedRoman Architecture1056 Words   |  5 PagesThe White House, The Capitol Building, The Lincoln Memorial, all these things have been affected by ancient Roman architecture. This ancient Roman architecture came to be around the time period of the Pax Romana in the Roman Empire. It was a time of great wealth and prosperity for the empire which brought it into a time of a sort of golden age for architecture. This type of architecture was influenced by the ancient Greeks, but it took their ideas and transformed them to better advantage their ownRead MoreRoman Architecture : Architecture And Structure1753 Words   |  8 Pages2010 Jun 09 Roman Architecture and Structure Roman architecture followed the heritage of earlier Greek architects. The Romans had respect for the Greek s architectural traditions, order, and design. The Romans were innovators that had the adaptability to use and improve existing techniques as well as new and existing material to create some of the most famous architectural structures like the temple, triumphal arch, and amphitheater. As time advanced so did the society needs of the Romans and withRead MoreRoman Architecture Essay1439 Words   |  6 PagesWhen one thinks of Roman architecture, many things come to mind, such as arches, columns, statues, and richly covered surfaces in marbles. One must stop to think that this empire, which gained power and influence in the first century BC, must have been influenced from the thousands of years of cultures preceding them in order to create their masterpieces of ingenuity. This phenomenon can be seen in our borrowing of ideas of ancient Greece and Rome for the construction of our capitol buildings inRead MoreRoman Architecture And Its Impact On Society1550 Words   |  7 Pages Introduction Roman architecture is a thing of the past; however, it still has a large impact upon society today. Roman buildings are what our buildings were derived from, a lot of the art we think of when we see a building was once thought of by the romans, was once built by the romans, and some of these buildings are still standing today, not many, but a few. Now, roman architecture is significant because of how it affected the growth of our architecture, how it evolved changed how weRead MoreRoman Architecture And The Renaissance1277 Words   |  6 PagesRoman Architecture and the Renaissance In the renaissance days, the designers reject the many-sided quality and vertical of the Gothic style for the straightforwardness and balanced degrees of class. Balanced bends, vaults, and the built up solicitations were revived. This reclamation was refined through direct view of Roman leftovers. The renaissance structural planning is the construction modeling of the time frame between the mid fifteenth and mid seventeenth hundreds of years in distinctiveRead MoreGreek to Roman Architecture544 Words   |  2 PagesGreek and which is Roman, However, show them pictures of the Forum of Trajan, the Pantheon, or the Agora, and they will be flipping coins trying to guess which is Roman architecture and which is Greek architecture. It is one thing being able to identify which building belongs to which civilization, it another being able to distinguish the subtle style changes within each mega structure. Very similar to a textbook and essay, Greek architecture is like a textbook, and the Romans used the textbook toRead MoreThe Roman Architecture And Engineering1798 Words   |  8 Pagestribes, the Roman Empire was done for, and had no chance of coming back to greatness; so why is it still looked upon as an amazing feat of ruling? A legacy is a reminder of something, like an empire, that was once great and inspired many things today, the way Roman architecture and engineering has. If we didn t have any of these great Roman architectural feats, then many people today wouldn t have a roof over their heads, and it would take much longer to get from place to place. The Roman legaciesRead MoreThe Influence of the Greeks and Romans on Architecture894 Words   |  4 PagesIntroduction â€Å"Architecture should speak of its time and place, but yearn for timelessness.† (Gehry, 2012). What Frank Gehry was trying to say in simple terms was our culture cannot do without proper appreciation of its classical roots and it goes without saying that the Romans and Greeks have influenced art and architecture with its classical style in a number of different ways. Allow me to give a definition for the word classical. â€Å"Classical† refers to any art or architecture modelled after ancientRead MoreThe Influence of Roman Engineering and Architecture1573 Words   |  7 PagesThe ingeniousness and beauty of Roman architecture has not been lost on us in the 2000 years since it was built. Even today, we still marvel at what incredible builders the Romans were, and at the sheer scale and integrity of many of their projects. It is hard to argue that today’s architecture will maintain the same lasting grandeur as that which the Romans built. If we can still re spect and admire the grandeur of Rome as it was in it’s day, one can only imagine how much of an influence peopleRead MoreRoman Architecture : The Temple Of Apollo1211 Words   |  5 Pagesmuch of their culture, and as a result many aspects of their architecture. Because the Roman style of architecture had developed over many years, once they adopted their ways of building, they rarely changed it. A case in which the Romans used some Etruscan elements in their building is the Capitoline Temple in Rome. However, a very important exception of this is how they adopted greek elements that they liked into their style. Once the Romans came in contact with the Greeks, they used some of their

My Goals free essay sample

In this world, Elwood, you must be oh so smart or oh so pleasant. Well, for years I was smart. I recommend pleasant. You may quote me.† I was thirteen when I first heard those words from the James Stewart movie Harvey. They stayed in my brain for about a year. After a year, I began to question them. Why only be smart or pleasant? I knew that for most of my life I had been smart. But as I grew older, I seemed to edge closer and closer to being pleasant. It crystallized about halfway through my freshman year that I wanted to be both. I wanted to be pleasant, I wanted to be amicable. I wanted to be a good person, not just simply a great one. So for the summer before my sophomore year, I went on a political road trip.It was a trip with about twenty kids from all over the country, who came together to hear presentations from people on both sides of controversial issues, such as marriage equality and minimum wage. We will write a custom essay sample on My Goals or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Hearing both sides of an issue allowed me to gain a perspective on not only my own views but of the people on the other side of the aisle. The summer road trip gave me a deeper understanding of the people around me, which led to a deeper empathy towards others. This was the start of a more pleasant me, who knew that fighting is rarely necessary and seldom effective. The other half of my goal was intelligence. I had always desperately wanted to be smart. Not just smart by comparison or for my age but undeniable intelligent, no matter the surroundings or situation. I realized that to be smarter, I would have to have to go beyond a regular education or school curriculum. To do this, I took classes outside of school in Spanish, and use computer applications to learn German. I developed the ambition to know eight languages. I read too. I read classics and famous literary oddities and tried to get my hands on anything on Time’s List of the 100 Best Novels. My ambition became to be a cosmopolitan good Samaritan.

Wonder Bars Cost of Capital or Required Return free essay sample

Study results show that on the background of a global economic climate eroded strongly by the effects of the current financial crisis, international diversification does not reduce risk. Moreover, using ARCH and GARCH models shows that the evolution of portfolio volatility is influenced by the effects of the current global financial crisis. Keywords: global financial crisis; diversification; volatility; ARCH model; GARCH model. JEL Code: G01. REL Code: 11B. Ideas in this article were presented at the Symposium „The global crisis and reconstruction of economics? †, 5-6 November 2010, Faculty of Economics, Bucharest Academy of Economic Studies. 76 Oana Madalina Predescu, Stelian Stancu 1. Introduction The sub-prime credit problem that started in the United States during 2007 affected the financial sector in other countries, especially Europe. Deterioration of this sector led to the collapse of national financial systems in different parts of the world, the result being a se vere global financial crisis. The magnitude of the recent financial crisis is considered to have no precedent since the Great Depression, the International Monetary Fund (IMF) referring to this global recession as â€Å"The Great Recession†. We will write a custom essay sample on Wonder Bars Cost of Capital or Required Return or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page A study developed by the IMF in 2009 stated that the recent financial crisis has â€Å"revealed important flaws in the current global architecture†. The IMF managing Director, Dominique Strauss-Kahn, has underlined, in May 2009, the necessity of a â€Å"new global framework† that can ensure a better coordination of national policies (Moshirian, 2010, pp. 5-6). Thus, any national policy must be strongly related with global policies in order to avoid another global financial crisis. However, not few have been those that questioned if a global financial system is desirable. In order to clarify this debate we must first understand the role of the financial system for the economy. The financial system represents the link between investors and private or public entities. Through this system the economy grows, the consumers also grow their capacity to consume, risks are shared and individuals confront with a smaller volatility on the market. All these correlations extend to the level of the global financial system, a system that works as a powerful and efficient mechanism when the economy is characterized by stability. Whereas the potential of the financial system certainly exists, words like moral hazard or adverse selection remind us that the economic theory has identified the reasons why financial markets do not always work perfectly, the failure of one or more markets generating losses for the other ones under the conditions of a global financial system. The recent financial crisis, as well as past episodes, teaches us very clearly that the capital flows guided by financial markets can represent something very different from an efficient and optimal allocation of savings towards the right investment projects. Increasing interconnections of financial institutions and markets lead to highly correlated financial risks, liquidity pressures and depletion of bank capital. Although the recent crisis originated in the United States, due to a highly integrated global economy, the financial markets around the world collapsed and the use of sophisticated financial instruments along with massive international capital flows facilitated its rapid spread across markets and borders (Claessens, Kose, Terrones, 2010). Modern portfolio theory relies on the study developed by Markowitz (1952). Rubinstein (2002) appreciated that Markowitz’s research represents the first mathematical formalization of the diversification concept of investments, Portfolio Risk Analysis using ARCH and GARCH Models in the Context of the Global Financial Crisis 77 emphasizing the fact that even though diversification reduces risk, it can not eliminate it completely. So, through diversification risk can be reduced without having any effects on the portfolio expected return. Thus, sub-perfectly correlated securities represent the â€Å"right candidates† to be included in a portfolio. Solnik (1974), among others, extended the initial CAPM (Capital Asset Pricing Model) and suggested that international diversification leads to better results than domestic diversification. However, financial integration leads to a significant correlation of security returns, the benefits of international diversification being greatly reduced (Aloui, 2010). Taking all these aspects into consideration, the present paper aims to analyze the evolution of the risk of an internationally diversified portfolio in the context of the current financial crisis. The remainder of the paper is organized as follows: Section 2 presents theoretical aspects related to the volatility measurement of financial time series using ARCH and GARCH models; in Section 3 we report the empirical results of our study and in Section 4 we provide a summary of our conclusions. 2. Measuring volatility in financial time series: the ARCH and GARCH models Philip Fransens (1988) noted that in the case of financial time series â€Å"various sources of news and other exogenous economic events may have an impact on the time series pattern of asset prices. Given that news can lead to various interpretations, and also given that specific economic events like an oil crisis can last for some time, we often observe that large positive and large negative observations in financial time series tend to appear in clusters† (Gujarati, 2004, p. 856). In practice, linear time series models are incapable to explain a number of important features common to financial data, such as: Leptokurtosis – the tendency for financial asset returns to have distributions that exhibit fat tails; Volatility clustering or volatility pooling – the tendency for volatility to appear in bunches on financial markets. Thus large returns (of either sign) are expected to follow large returns, and small returns (of either sign) to follow small returns. One of the explanations for this phenomenon, which seems to characterize financial return series, would be the fact that the information arrivals which drive price changes occur in bunches. Leverage effects – the tendency for volatility to rise more following a large price fall, rather than following a price rise of the same magnitude. 78 Oana Madalina Predescu, Stelian Stancu Campbell, Lo and MacKinlay (1997) defined a non-linear data generating process as one where the current value is related non-linearly to current and previous values of the error term: (1) where et represents an independent and identically distributed (iid) error term, and f is a non-linear function. According to the three researchers, a more specific form of the non-linear model is given by the following equation: (2) where g is a function of past error terms, and ? is the variance term. Campbell, Lo and MacKinlay characterize models with non-linear g as being non-linear in mean and those with non-linear ? as being non-linear in variance. Models can be linear in mean and variance (the classic regression model, ARMA models) or linear in mean, but non-linear in variance (GARCH models) (Brooks, 2010, pp. 380). The most commonly used financial models to measure volatility are the non-linear ARCH and GARCH models. 2 y t = f (e t , et ? 1 , et ? 2 , ) y t = g (et ? 1 , et ? 2 , ) + et ? 2 (et ? 1 , et ? 2 , ) 2. 1. The autoregressive conditional heteroscedasticity model (ARCH) One of the fundamental hypotheses of the classical regression model is the homoscedasticity or the hypothesis of constant error variance: var(et ) = ? (et ) , where et ~ N (0, ? 2 ) . The opposite case is known as heteroscedasticity. In the case of financial time series it is unlikely that the variance of the errors will be constant over time and hence it is preferred to consider a model that does not assume constant variance and which can describe how the variance of the errors evolves. As we mentioned earlier another important feature of financial series is known as volatility clustering or volatility pooling. This characteristic shows that the current level of volatility tends to be positively correlated with its level during the immediately preceding periods. Using the ARCH model (Engle, 1982) represents one of the modalities through which a phenomenon of this nature can be parameterized. In order to understand how this model works, a definition of the conditional variance of a random variable et is necessary. Thus, the conditional variance of et , denoted ? t2 has the following form: ? t2 = var(et / et ? 1 , et ? 2 , ) = E[(et ? E (et )) 2 / et ? 1 , et ? 2 , ] (3) Portfolio Risk Analysis using ARCH and GARCH Models in the Context of the Global Financial Crisis 79 Since E (et ) = 0 , equation (3) becomes: t2 = var(et / et ? 1 , et ? 2 , ) = E[et2 / et ? 1 , et ? 2 , ] (4) Equation (4) states that the conditional variance of a zero mean normally distributed random variable et is equal to the conditional expected value of the square of et . In the case of the ARCH model, the autocorrelation in volatility is modeled by: ? t2 = ? 0 + ? 1 ? et2? 1 (5) The above model is known as ARCH(1) and it shows that the conditional variance of the error term ? t2 , depends on the immediately previous value of the squared error. Equation (5) represents only a part of the model, ince nothing has been specified about the conditional mean. Under the conditions of the ARCH model, the conditional mean equation (which describes how the dependent variable y t varies over time) can take almost any form. One example of a full model would be the following one: y t = ? 1 + ? 2 ? x 2t + ? 3 ? x3t + ? 4 ? x 4t + et ? t2 = ? 0 + ? 1 ? et2? 1 where et ~ N (0, ? 2 ) . (6) (7) So the model given by equations (6)-(7) can be extended to the general case, where the error variance depends on q lags of squared errors, a model known as ARCH( q ): y t = ? 1 + ? 2 ? 2 t + ? 3 ? x 3t + ? 4 ? x 4 t + e t ? = ? 0 + ? 1 ? 2 t et2? 1 + ? 2 ? et2? 2 + + ? q ? et2? q (8) (9) where et ~ N (0, ? ) . 2 Since ? 2 represents the conditional variance, its value must be strictly t positive (a negative variance at any point in time is meaningless). So all the coefficient s in the conditional variance equation must be positive: ? i ? 0, (? )i = 0,1,2, , q . A natural extension of the ARCH( q ) model is the GARCH model. 80 Oana Madalina Predescu, Stelian Stancu 2. 2. The generalized autoregressive conditional heteroscedastic model (GARCH) The GARCH model has been developed independently by Bollerslev (1986) and Taylor (1986). This model allows the conditional variance to be dependent upon previous own lags, so that the simplest equation form of the conditional variance is: ? t2 = ? 0 + ? 1 ? et2? 1 + ? ? ? t2? 1 (10) This is a GARCH(1,1) model and the conditional variance can be interpreted as a weighted function of a long term average value (dependent on ? 0 ), of the information related to the volatility during the previous period ( ? 1 ? et2? 1 ) and of the variance during the previous period ( ? ? ? t2? 1 ). The general form of the GARCH( q , p ) model, where the conditional variance depends on q lags of the squared error and p lags of the conditional variance is: ? t2 = ? 0 + ? 1 ? et2? 1 + ? 2 ? et2? 2 + + ? q ? et2? q + ? 1 ? ? t2? 1 + + ? 2 ? ? t2? 2 + + ? p ? ? t2? p (11) or ? t2 = ? 0 + ? ? i ? et2? i + ? ? j ? ? t2? j i =1 j =1 q p (12) In academic literature a GARCH(1,1) model is considered to be sufficient in capturing the evolution of the volatility. A GARCH(1,1) model is equivalent to an ARCH(2) model and a GARCH( q, p ) model is equivalent to an ARCH ( q + p ) model (Gujarati, 2004, p. 62). The unconditional variance of the error term et is constant and given by the following equation: var(et ) = ?0 1 ? (? 1 + ? ) (13) as long as ? 1 + ? lt; 1 . For ? 1 + ? ? 1 , the unconditional variance of the error et is not defined (non-stationarity in variance), and ? 1 + ? = 1 represents Integrated GARCH or IGARCH (unit root in variance). Portfolio Risk Analysis using ARCH and GARCH Models in the Context of the Global Financial Crisis 81 3. Empirical results 3. 1. Data and descriptive statistics We selected three benchmark indexes from 3 different countries, namely Romania (BET), UK (FTSE100) and USA (SP500). We chose these indexes because according to the Pearson correlation coefficient the 3 analyzed markets are not perfectly correlated, representing thus the â€Å"right candidates† to be included in the portfolio (Table 1). However, Aloui (2010) underlined in a recent paper that this coefficient is not the best indicator for measuring market interdependence. He stated that this coefficient can not make a clear distinction between large and small returns, or between positive and negative returns. Moreover, the Pearson correlation estimate is constructed on the basis of the hypothesis of a linear association between the financial return series under consideration, whereas their linkages may well take non-linear causality forms. The solutions that can solve these problems are found in the transformations that can be applied to non-linear models (logarithms) or by using GARCH models. Thus, the study developed in this paper aims to analyze both the benefits of choosing an internationally diversified portfolio and the evolution of the portfolio risk in the context f the current global financial crisis. BET 1. 00 0. 26 0. 43 FTSE100 0. 26 1. 00 0. 58 Table 1 SP500 0. 43 0. 58 1. 00 BET FTSE100 SP500 We computed a database containing daily returns over the period January 4, 2005 to May 5, 2010, being registered 1,297 observations using the following formula: ? p ? rit = ln? it ? ? 100% ? p ? ? it ? 1 ? (14) where rit represents the continuously compounded return of security i at tim e t , pit represents the price for security i at time t , i = 1, n and t = 1, T ( n = 3 and T = 1297 ). We chose a 0. 5 weight for the investment in the BET index, a 0. 5 weight for the investment in the FTSE100 and a 0. 25 weight for the investment in the SP500, being preferred a passive strategy of portfolio management. 82 Oana Madalina Predescu, Stelian Stancu Table 2 Min Max Mean Standard Deviation Skewness Kurtosis Jarque-Bera Descriptive statistics for the daily index returns BET FTSE100 SP500 -0. 135461 -0. 092646 -0. 094695 0. 105645 0. 093842 0. 109572 0. 000011 0. 000049 0. 000080 0. 021893 0. 014304 0. 015155 -0. 665793 0. 062484 -0. 029192 8. 364870 11. 03812 12. 24093 1651. 241 3492. 551 4615. 061 Prob. 0. 00000 Prob. 0. 000000 Prob. 0. 000000 Portfolio -0. 088843 0. 087974 -0. 000003 0. 014761 -0. 446707 9. 151124 2087. 874 Prob. 0. 000000 The results show that the riskiest market is the national capital market, the less risky being the UK market. The portfolio risk is moderate in comparison to the risks registered on the markets. The biggest return is obtained in the case of the SP500 index, the lowest one being obtained by the chosen portfolio. According to the skewness and kurtosis indexes, all data series are asymmetrical and exhibit excess kurtosis. The Jarque-Bera statistics are highly significant for all return series for a significance level of 1%, being confirmed the assumption that the series are not normally distributed. In Figure 1 is illustrated the variation of the daily returns over the period January 4, 2005 to May 5, 2010. We can observe from the graphics that the returns were fairly stable over the period January 2005 to September 2008. After this date all return series manifested instability especially due to the effects of the global financial crisis. Moreover, we can observe that the series present two specific features of non-linear models (volatility clustering and leverage). In order to analyze portfolio risk we first estimated a non-linear model with the capacity to capture the evolution of portfolio volatility over the specified time horizon, and the model most commonly used in financial applications of this nature is GARCH(1,1). Before estimating the model we had to detect any ARCH effects in the portfolio return series. Thus we performed the Engle (1982) test. We chose a number of five lags and using the â€Å"least squares method and ARMA† we estimated an ARMA(1,1) model in order to perform afterwards the heteroscedasticity test. According to the test results there are ARCH effects in the portfolio return series (Table 3). 0. 10 -0. 10 -0. 05 -0. 05 0. 00 0. 00 -0. 15 0. 05 -0. 10 0. 10 0. 15 0. 05 0. 15 -0. 15 -0. 15 0. 00 0. 05 0. 10 0. 15 -0. 10 -0. 05 -0. 10 4/1/2005 4/4/2005 4/7/2005 4/10/2005 4/1/2006 4/4/2006 4/7/2006 4/10/2006 4/1/2007 4/4/2007 4/7/2007 4/10/2007 4/1/2005 4/5/2005 4/9/2005 4/1/2006 4/5/2006 4/9/2006 4/1/2007 /5/2007 4/9/2007 SP500 4/1/2008 4/4/2008 4/7/2008 4/10/2008 4/1/2009 4/4/2009 4/7/2009 4/10/2009 4/1/2010 4/4/2010 4/1/2008 FTSE100 Portfolio Portfolio Risk Analysis using ARCH and GARCH Models in the Context of the Global Financial Crisis Figure 1 SP500 4/5/2008 4/9/2008 4/1/2009 4/5/2009 4/9/2009 4/1/2010 4/5/2010 -0. 05 BET BET 0. 00 FTSE100 0. 05 0. 10 Portofoliu 83 84 Heteroskedasticity Test: ARCH F-statistic Obs*R-squared Oana Madalina Predescu, Stelian Stancu Table 3 49. 01776 206. 7917 Prob. F(5,1285) Prob. Chi-Square(5) 0. 0000 0. 0000 3. 2. Estimation results Using the â€Å"ARCH method† we estimated the GARCH(1,1) model (the results are shown in Table 4). The coefficients of the squared error and of the conditional variance are highly statistically significant (for a significance level of 1%, 5% and 10%). As expected, in a typical GARCH model for financial data the sum of the coefficients is close to 1. The coefficient of conditional variance is almost 0. 9 and this implies that the shocks to the conditional variance are persistent and that large changes in the conditional variance are followed by other large changes and small changes are followed by other small changes. The variance intercept coefficient is very small and the coefficient of the squared error is 0. 1. Table 4 Dependent Variable: PORTFOLIO Method: ML ARCH Sample: 1 1297 Included observations: 1297 Convergence achieved after 14 iterations Presample variance: backcast (parameter = 0. 7) GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*GARCH(-1) Variable Coefficient Std. Error C 0. 000879 0. 000269 Variance Equation C 1. 44E-06 3. 74E-07 RESID(-1)^2 0. 108449 0. 010648 GARCH(-1) 0. 891154 0. 008593 R-squared -0. 003570 Mean dependent var Adjusted R-squared -0. 03570 S. D. dependent var S. E. of regression 0. 014787 Akaike info criterion Sum squared resid 0. 283384 Schwarz criterion Log likelihood 3934. 400 Hannan-Quinn criter. Durbin-Watson stat 1. 806960 z-Statistic 3. 263635 3. 860082 10. 18496 103. 7114 Prob. 0. 0011 0. 0001 0. 0000 0. 0000 -2. 57E-06 0. 014761 -6. 060756 -6. 044818 -6. 054775 In order to validate this model we had to verify whether the squared errors presented ARCH effects. T hus, we analyzed both the correlogram of the squared error series and the results of the ARCH test. According to the correlogram there are no additional ARCH terms, a result confirmed also by the ARCH test for the significance levels of 1% and 5% (Tables 5 and 6). Portfolio Risk Analysis using ARCH and GARCH Models in the Context of the Global Financial Crisis 85 Table 5 Heteroskedasticity Test: ARCH F-statistic Obs*R-squared Sample: 1 1297 Included observations: 1297 Autocorrelation |* | | | | | | | | | | | | | | | | | | | 1. 895668 9. 452893 Prob. F(5,1286) Prob. Chi-Square(5) 0. 0922 0. 0923 Table 6 Partial Correlation |* | | | | | | | | | | | | | | | | | | | AC 0. 129 -0. 009 0. 18 0. 023 0. 006 0. 021 0. 039 0. 016 0. 021 0. 027 PAC 0. 129 -0. 026 0. 023 0. 018 0. 002 0. 021 0. 034 0. 007 0. 019 0. 020 Q-Stat 21. 486 21. 587 22. 029 22. 741 22. 792 23. 355 25. 363 25. 707 26. 297 27. 238 Prob 0. 000 0. 000 0. 000 0. 000 0. 000 0. 001 0. 001 0. 001 0. 002 0. 002 1 2 3 4 5 6 7 8 9 10 Based on the estimated volatility equation, we generated the historical series of conditional vo latility. Volatility can be measured through variance or standard deviation. So, the portfolio volatility (measured through standard deviation) is presented in the graphic below: Figure 2 According to Figure 2 there are more volatile periods than others over the analyzed time period. The most pronounced volatility can be noted between 2008 and 2009 (the highest peak), being observed a slight fall in the next period and then in May 2010 (the end of the chosen time horizon) it can be observed another abrupt increase in the volatility. This evolution of the portfolio 86 Oana Madalina Predescu, Stelian Stancu volatility is attributed to the effects of the current financial crisis that has put a print on the financial markets around the world. In order to confirm this conclusion we analyzed the daily returns of the indexes over the period 2008-2009 (Figure 3). BET evolution betw een 2008-2009 0. 15 0. 10 0. 05 0. 00 0. 05 -0. 10 -0. 15 Ti me BET FTSE100 evolution betw een 2008-2009 0. 15 0. 10 0. 05 0. 00 0. 05 -0. 10 -0. 15 Ti me FTSE100 SP500 evolution betw een 2008-2009 0. 15 0. 10 0. 05 0. 00 -0. 05 -0. 10 -0. 15 T ime SP500 Figure 3 As it can be observed in September 2008 (20 September-12 October 2008 the speculative crisis in the US) represents the moment when the evolution of the return series starts to present considerable fluctuations. Moreover, the fallwinter of 2008 represents also the period when the crisis extended in Europe. Portfolio Risk Analysis using ARCH and GARCH Models in the Context of the Global Financial Crisis 87 4. Conclusions Studies of the transmission of return and volatility shocks from one market to another as well as studies of cross-market correlations are essential in finance, because they present numerous implications for capital allocation. As it was emphasized in this paper, at first sight some markets may seem slightly correlated, so international portfolio diversification can be in this case an optimal solution. However, on the background of a highly integrated global financial system, eroded strongly by the effects of the current crisis, international diversification does not reduce portfolio risk. Using in this paper the ARCH and GARCH models we have been able to analyze the evolution of the risk of an internationally diversified portfolio, being chosen three benchmark indexes from three different countries, namely Romania (BET), UK (FTSE100) and USA (SP500) to be included in the portfolio. We chose a 0. 5 weight for the investment in the BET index, a 0. 25 weight for the investment in the FTSE100 and a 0. 5 weight for the investment in the SP500. Starting from this portfolio we used the Engle (1982) test in order to track any ARCH effects in the portfolio return series. Taking into consideration that these effects were detected, we were able to estimate a GARCH(1,1) model. The estimation results showed that the volatility is persistent because the coefficient of the conditional variance i s 0. 9 and this means that the shocks to the conditional variance are persistent and that large changes in the conditional variance are followed by other large changes and small changes are followed by other small changes. On the basis of the estimated volatility equation we generated the historical series of the conditional volatility. The graphic of this series shows that there are more volatile periods than others, the most pronounced volatility being observed over the period 2008-2009. 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