# Ostasiatische Musikelemente in James-Bond-Filmen (German Edition)

Throughout the series the movies have always been some kind of image of its age, so when analyzing the success or failure of OHMSS it is obligatory to look at the situation in Britain at that time. Affected by Wilson the British society believed in the connection between technological, economical and social improvement Rauscher The hope that a common commitment towards the scientific-technological progress would bear down the class conflicts was dominant.

During the Connery years Bond was portrayed as a working class hero with much elegance, toughness and lots of black humor. With the help of upgraded gadgets Bond managed to survive many tough situations. Doubtlessly the films depicted homage of modernization during that time and represented the belief: It was new in many ways, striking the change from live-saving adventurous gadgets to the focus on personal characterization and deeper feelings, especially seen in the depiction of Tracy and James which I will focus on later. For the first time a Bond movie tried to meet the new social tendencies Rauscher In contrast to most Bond movies its title sequence starts with an instrumental song, composed by John Barry, resigning from the usual theme.

They did so by showing previous enemies and girls from the series in the title sequence and having Lazenby reintroduce some gadgets that his precursor Connery used in order to save his life. Through the, for the movies of the late sixties typical, disenchanting end director Peter Hunt expressed the deep anxiety, caused by political and social insecurity of that time.

Kunst - Fotografie und Film. English - Literature, Works. Kulturwissenschaften - Allgemeines und Begriffe. Informatik - Theoretische Informatik. Communications - Movies and Television. BWL - Investition und Finanzierung. VWL - Geldtheorie, Geldpolitik. GRIN Publishing, located in Munich, Germany, has specialized since its foundation in in the publication of academic ebooks and books. The publishing website GRIN. Free Publication of your term paper, essay, interpretation, bachelor's thesis, master's thesis, dissertation or textbook - upload now!

Register or log in. Our newsletter keeps you up to date with all new papers in your subjects. Request a new password via email. The best Bond movie ever Or: Thirty are better than one. Die "Secret-Stories" als Zeugnis einer Bekehrung? Die fiktiven Welten der amerikanischen Teen Movies. Was macht den Bond zum Bond? Table 3 shows the regression coefficients upper figure and the respective Hansen-Hodrick corrected standard errors lower figure in parentheses for different pairs of maturities, of the regression stated in formula However, for longer maturities of , this regression cannot reject the expectation hypothesis as the coefficients are almost all very close to one.

Overall, results from tables 2 and 3 are called the Campbell and Shiller paradox, as the first regression almost always gives a forecast in the wrong direction whereas the second regression gives a forecast in the right direction. As the expectation hypothesis states inter alia that long-term yields should be equal to the average of the expected future short rates and thus excess return predictability is not feasible, the results of CP have strengthened the evidence against the expectation hypothesis.

Their model regresses the lagged excess return against a linear combination of forward rates. The data is available on a monthly basis apparently produced in the same manner as in Fama and Bliss which I have already presented in a previous section , and contains one- through five-year zero coupon U. There are several issues when working with empirical data, especially for time series of prices. One of the biggest problems encountered is so-called overlapping data, which appears when the analyzed time period is larger than the frequency of the data used. For instance, CP calculate one year excess returns by using a data set of monthly observations.

When rolling from one month to another, the overlapping of observations creates a moving average error term that makes hypothesis tests biased and ordinary least squares OLS parameter estimates inefficient. Furthermore, they also reduced the data in a way that none of it actually overlaps. The Hansen and Hodrick method is the standard approach for handling forecasting regressions with overlapping data; however, it does not always produce a positive definite covariance matrix. This weighted estimator creates a greater chance of correcting for the moving average error that results from overlapping data.

It is always a concern when dealing with relatively small samples and the dynamics of the data imply high serial correlations. It has been pointed out by Mankiw and Shapiro and Stambaugh that the existence of small-sample bias in tests of predictability leads to a rejection of the null hypothesis of nonpredictability too often. The following section describes the methodology that CP used to derive their results.

As in all previous formulas, numbers in parentheses display the maturity of the bonds and lower case denotes logs. The yield is then given by:. As the data consists of one- through five-year zero coupon bonds, log forward rates can by calculated by:. Log holding period returns are then calculated by the difference of the log price of a bond with maturity in time and the log price of a bond with maturity in time:.

The difference of log holding period returns and the yield of the bond with maturity of one year are defined as excess returns and can be expressed by:. The first regression CP run contains the lagged excess return as the dependent variable and a linear combination of all forward rates as the right-hand variable. The regression, which focuses on the one-year return horizon, can be expressed by:. This regression is called the unrestricted model.

As CP constructed a single factor, we first need to estimate a coefficient by regressing the average excess return on all forward rates:. In the second step, we can estimate a loading factor for each bond by regressing the excess return of each maturity on:. As the single-factor model is restricted in , it is therefore also called the restricted model. In this section I describe the main findings of CP that show why their paper has been given such great attention in financial literature. As already mentioned, the analysis incorporates the time period from to The upper part of the table shows the results of the estimated gammas of equation It stands out that, ignoring the intercept, the coefficients are almost symmetric around.

With a of 0. To recapitulate, the highest of the Fama-Bliss model for the same time period was just about 0. The middle portion of the table shows the standard errors in parentheses, as well as the critical values for a hypothesis test that tries to reject the null that all parameters are jointly zero.

Keeping the possibility of small-sample biases in mind, we find the standard errors for the 12 Lag VAR. The bottom part of the table depicts the results for the individual bond regressions of both the restricted and unrestricted models.

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The regression coefficients of the restricted models increase with maturity. The values of the restricted and unrestricted models are practically the same, suggesting that the unrestricted model is not superior to the single-factor model which predicts the excess return of all, not just specific maturities. I will not go into further detail on this as these analyses are not part of my further investigation. Plotting the results of the restricted and unrestricted models reveals an interesting pattern in the coefficients.

As already indicated, the regression coefficients are almost symmetric around , resulting in a tent-shaped pattern. Figure 1 depicts the estimated from the unrestricted regressions, as well as the estimates of from the single-factor model. The x-axis shows the maturity of the forward rates of the independent variables of the regression and the legend exhibits the maturity of the bonds of the dependent variable. The figure clearly shows that the results of both the unrestricted and restricted models are pretty much the same, confirming that the single-factor model almost captures the parameter of the unrestricted model.

Another interesting finding by CP is that their single return-forecasting factor, the CP-factor, is unrelated to movements of the standard term structure model. Most term structure models imply that changes in the yield curve are almost completely described by level, slope and curvature.

The foundation is based on so-called principal component analysis PCA , which decomposes the variance of the data into components that are sorted in order of importance. As the PCA is a pure statistical method, the ordered components need to be interpreted. Figure 2 represents the first three principal components of the yields calculated from the data set.

It shows how the yields for different maturities change when the respective factor changes.

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For example, a change in the level factor increases the yields of all five maturities about 0. The first three principal components in the data set used by CP explain Therefore it is natural to question how we could relate the return forecasting factor to the principal components of the yield curve. In order to make the forecasting factor comparable, we have to run an OLS of the average excess return on the yields instead of forward rates:. As forward rates and yields span the same linear space, we can write.

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As can be clearly seen, the return-forecasting factor is unrelated to the first three principal components of the yield curve. While the curvature-factor is curved at the short end, the return-forecasting factor is curved at the long end of the yield curve. Whether the return-forecasting factor indeed has superior return predictive ability to the first three principal components of the yield curve can be assessed by regressing the average excess return on the principal components and comparing the results to the Cochrane and Piazzesi model.

Table 5 depicts the results for the regression of lagged average excess returns on different combinations of the principal components.

## Bond Return Predictability. The Cochrane and Piazzesi model (CP-factor)

If only regressing on the slope, the is 0. When including the level to the regression, the rises to 0. The peaks at 0. The slope seems to be the most important of the three factors, as it predicts the vast majority of excess returns. It should also be noted that the vast majority of changes in the yields are explained by the level factor, whereas the slope explains changes of expected returns.

As the for the CP-factor regression 0. It turns out that the first five principal components explain 9. It also clearly stands out here that the slope explains the vast of the majority of changes, but in contrast to the principal components of the yield curve, the fourth principal component explains the second highest number of variations.

This is an important discovery, as it seems that the fourth principal component of the CP-factor which has a big impact on the four- to five-year yield spread does not carry much information about changes in the yield curve, although it does carry a great deal of information about future excess returns. As we have seen in the previous section, the CP-factor seems to be a revolutionary factor in predicting risk premiums; however, as of today nobody really knows why the CP-factor actually works so well.

A natural question is therefore what exact time-varying risk premium could actually be captured by the CP-factor? Koijen, Lustig and van Nieuwerburgh then analyzed the CP-factor in detail and found that it clearly forecasts future economic activity. They found that the CP-factor predicts economic activity months in advance. In figure 4, the CP-factor is plotted from March to November It stands out that the CP-factor is often low just before and very high at the end or just after a recession period, underscoring its predicative power for business cycles.

Dai, Singleton and Yang try to assess whether bond return predictability using the CP-factor also works when applying different interpolation mechanisms to the data. In order to overcome this problem, interpolation mechanisms several of which exist must be used. As these methods have an impact on what the yield curve looks like, the interpolation method can influence the results of empirical studies.

As already explained in previous sections, the Fama and Bliss approach which is also called the unsmoothed Fama-Bliss UFB method for example uses bootstrapping to create the yield curve.

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The FW method uses a cubic spline to approximate the forward rate function. The drawback of a cubic spline is that the approximated curve can oscillate between each knot point and thus the interpolation might end up with unrealistic results. The FW method is a modified version of the Fisher, Nychka and Zervos model and uses a so-called variable roughness penalty that lowers the oscillating effect and fits the smoothed yield curve to more realistic results. The idea behind the Nelson-Siegel method is that forward rates can be described by a parametric model with an exponential function of time to maturity:.

Because the spot rate function is defined as the average of the forward rates:. The basis of the interpolation is observed knot points of the term structure. As can be seen in equation 28, the Nelson-Siegel method has four parameters that are estimated in order to fit the observed knot points. There are several ways to make the estimations, although minimizing the sum of squared errors is probably the most popular choice. The drawback of a parametric model is that the fitted curve does not necessarily have to pass through the observed knot points.

The high correlation between the factors of the Nelson-Siegel method might additionally raise potential multicollinearity issues. BWL - Investition und Finanzierung. VWL - Geldtheorie, Geldpolitik. Literaturwissenschaft - Moderne Literatur. GRIN Publishing, located in Munich, Germany, has specialized since its foundation in in the publication of academic ebooks and books. The publishing website GRIN. Free Publication of your term paper, essay, interpretation, bachelor's thesis, master's thesis, dissertation or textbook - upload now!

Register or log in. Our newsletter keeps you up to date with all new papers in your subjects. Request a new password via email. Table of contents List of figures List of tables List of abbreviations 1 Introduction 2 Bond return predictability in financial literature 2. Plot of the regression coefficients of the restricted and unrestricted models Figure 2: Expected Return Factor Figure 4: Yield curves with different interpolation methods Figure 6: Unrestricted and restricted models for extended time period Figure 9: Unrestricted and restricted models for the Gurkaynak data set Figure Multicollinearity test for the Gurkaynak data set Figure Unrestricted and restricted models for the Canadian data set Figure Multicollinearity test for the Canadian data set List of tables Table 1: Fama and Bliss Model Table 2: Campbell and Shiller Results 1 Table 3: Campbell and Shiller Results 2 Table 4: Estimates of the Single-Factor model Table 5: Excess return forecast with principal components Table 6: Coefficients of regression of excess return on five principal components of the yield curve Table Standard deviations of the principal components in basis points Table Estimates of the single factor model for the extended time period until Table Fama-Bliss model vs.

Estimates of the principal components model for the UFB data until Table Estimates of the single-factor model on the Gurkaynak data set Table Fama-Bliss model on the Gurkaynak data set Table Estimates of the single-factor model for the Canadian data set Table Fama-Bliss model on the Canadian data set Table Estimates of the single-factor model for the German data set Table Fama-Bliss model on the German data set Table Fama-Bliss model on the Datastream data set Table