The distinguishing feature of this book is its attempt to incorporate recent methodological advances in the treatment of the various topics. Finally, a significant part of the recent progress in the asset pricing literature relies on testing strategies based on stochastic deflators. We also acknowledge the motivating comments and suggestions by the participants in the conferences, as well as by our students and colleagues.
Globalization influences expected risk premia through three main channels: corporate profitability, market portfolio structure, and global risk pricing. The trend towards international investment is a natural consequence of the overall globalization of economies and the international financial system. Globalization influences expected returns through three main channels: corporate profitability, market portfolio structure, and the pricing of global risks.
This has had a substantial effect on the composition of the relevant market (or benchmark) portfolio, which in turn affects the expected equilibrium return on individual assets.
An Overview
In such a case, the weighting schemes of the goods in the national consumption baskets differ. Finally, βciwm stands for the sensitivity of the real return of the ith asset to the real return of the world market portfolio (the real world market beta). In the following, based on Jorion and Khoury (1995), we highlight the main features and implications of the model.
To start with, we take up the remark of Adler and Dumas (1983) and do not distinguish between foreign stocks (fund 1) and foreign risk-free assets (fund 2) in the characterization of the risky part of the portfolio. of expected excess returns in domestic currency is denoted by and the variance-covariance matrix of the domestic excess returns is. Because investors with a logarithmic utility function (which exhibits a relative risk tolerance of one) would optimally invest all of their wealth in this risky fund, the portfolio is sometimes called the log portfolio. The disadvantage of this characterization is that it does not reveal the currency hedging decision followed by the investors. Where investors with zero risk tolerance in the previous case invest all their wealth in the risk-free asset, here they invest only a fraction - the remaining part is allocated to the real hedge portfolio.
Perold and Schulman (1988) argue that exchange rate risk can be hedged at zero cost - the expected excess return on currency forward contracts is zero. L(currencies), where sitnotes the rate of change in the price of that country's currency calculated in relation to the domestic currency. In the previous section, we simply looked at historical volatility in different states of the world.
Note: The changes in the correlation coefficients between the Swiss stock market and other MSCI markets are regressed on a constant basis, as are the innovations in the associated national volatility. Note: The changes in the correlation coefficients between the United States stock market and other MSCI markets are regressed on a constant basis, as are the innovations in the associated national volatility. If we were to find that correlations also increase precisely when markets are falling (i.e. in response to downward volatility), then the strength of the link between volatility and correlation would be valuable.
Expansion correlations are shown on the left of the diagonal and recession correlations on the right. The future payout of the option is 22.12 in the upstate and zero in the downstate. An examination of the tail of the return distributions shows that the correlation structure for large returns is asymmetric.
The volatility and covariance asymmetry due to extreme negative returns is only apparent. Accordingly, the following analysis focuses directly on the tails of the distribution of monthly returns. Turning directly to the tails of the return distributions, we show that the correlation structure of large returns is asymmetric.
Analyzing Value and Volatility Drivers in Global Markets
We find that only three out of the seven global risk factors qualify as value drivers. The residuals of the VAR system form the time series of unexpected changes in the global risk factors. Five of the seven global factors are compiled by aggregating economic data from the G7 countries.
The change in G7 industrial production is the weighted average of the simultaneous monthly growth rates in industrial production in the respective countries. The change in the level of the long G7 interest rates is constructed on the basis of the interest rates on long government bonds in the G7 currencies. ING7C Change in global (G7) inflation rate IPG7C Change in global (G7) industrial production ILG7C Change in global (G7) long-term interest rates.
Monthly price changes of this basket of currencies reflect changes in the external value of the Swiss franc. Descriptive statistics for time series of global risk factors are shown in Table 5.3. For ease of comparison, Figure 5.2 shows the t-values of the sensitivity coefficients for representative stock and bond markets.
The Wald test rejects the null hypothesis of joint zero factor sensitivities in the cross-section of bond markets at the 10 percent level of significance for the global stock market excess return, the measures of both G7 short-term and long-term interest rate shifts, and the change in the price of the G7 currencies measured in Swiss francs. In the previous section, we analyzed which of the seven risk factors (volatility drivers) have the potential to explain cross-sectional differences in average stock and bond returns (which of the factors are potential value drivers). A three-factor model together with the world stock market excess return, the change in the level of G7 long-term interest rates, and the change in the price of the G7 currency basket as global sources of risk.
Three of the seven predefined global risk factors systematically affect the returns of both equity and bond investments: the excess return of the global market portfolio, the change in global long-term interest rates and the change in the price of the G7 ex - exchange rate basket. The three-factor model appears to provide a more representative picture of the factors that influence stock and bond market returns.
The Case of Switzerland and Germany
In the world of the Capital Asset-Pricing Model (CAPM), this means that the integration test examines whether foreign investment returns are consistent with the domestic security market (see Wheatley, 1988). The purpose of this chapter is to test the null hypothesis of the integration of the Swiss and German stock markets with the world market. Next, we present a consumption-based test for the integration of the German and Swiss stock markets with the global market.
This model represents the discrete version of the Consumption Capital Asset Pricing Model (CCAPM). The main limitation of the model is that expected (and not simply historical) returns are perfectly correlated across countries. Provided that the empirical relationship is consistent with the specification of a single latent variable model, all residuals, denoted as out, must be orthogonal to each of the lagged instrumental variables in the.
The chi-square (χ2) test statistic for model fit allows us to determine whether the null hypothesis of integration of the German and/or Swiss stock market with the world market can be rejected. We interpret this in favor of the null hypothesis that the German and Swiss stock markets are integrated with the world stock market. Overall, the results of the single latent variable model underscore the findings of the consumption-based test.
Thus, four degrees of freedom are left in the χ2 test of the overidentifying restrictions. It is not possible to reject the null hypothesis of integration for any of the country pairs. The aim of the empirical work in this chapter was to test the null hypothesis of the integration of the German and Swiss stocks.
Neither model leads to a rejection of the null hypothesis of the integration of the German and Swiss stock markets with the world stock market. They provide evidence for the validity of the null hypothesis of integration, but our asset pricing models can still be misspecified.
Myth or Reality?
In the late 1980s, investors began pouring money into the emerging markets of Latin America, Asia, the Middle East and Africa. Direct access to emerging stock markets at a reasonable cost will become much easier for investors in the near future. All summary statistics are presented in Table 7.1.3 To provide a more comprehensive illustration, Table 7.1 contains return characteristics for the entire set of Latin American and Southeast Asian countries in the IFC database.
This is in stark contrast to average returns in developed markets. In the limited MSCI sample used here, no arithmetic average for any country's return comes close to 15 percent. With high volatility, there are likely to be large differences in the arithmetic and geometric mean returns.
MSCI countries have volatility in the known region of between 15 and 25 percent per year. All countries in the IFC sample exhibit volatility above the maximum volatility of 25 percent (UK) in the MSCI sample. In the sample of emerging markets, seven countries have autocorrelations greater than 0.1, some of them even above 0.2.
Correlations are well above 0.6 for all countries in the MSCI sample; for Canada, the UK and the US they are even higher than 0.8. To save space, Table 7.2 contains only the correlations among the 14 stock markets analyzed later in this chapter. The values of the test statistic and the associated p-value are listed in the two right-hand columns of Table 7.3.
Applying the 10 percent significance level leads to a rejection of normality for all emerging markets in the sample except Mexico and Venezuela. In classical portfolio theory, we search for the portfolio with the least volatility, given a target level of expected return.
