INTERNATIONAL EQUITY MARKETS 2. Practical Issues Why Go International? • Diversification If it is good to diversify in domestic markets, it is even better to diversify internationally. Q: Why does the frontier move in the NW direction? A: Low Correlations! Low correlations are the key to achieve lower risk. • Empirical Fact #1: Low Correlations The correlations across national markets are lower than the correlations across securities in most domestic markets. Return correlations are moderate to low (many lower than .50). There is a regional effect: Correlations between neighboring markets tend to be higher: Correlation between the US and Canada is .72; US and Japan is .30. (Data: 1970-2007). • Empirical fact 2: Correlations are time-varying Correlations change over time. General finding: During bad global times, correlations go up => when you need diversification, you tend not to have it! Rolling Correlations: Japan-USA 1 0.8 0.6 Annual Correlation 0.4 0.2 0 Dec-70 Dec-72 Dec-74 Dec-76 Dec-78 Dec-80 Dec-82 Dec-84 Dec-86 Dec-88 Dec-90 Dec-92 Dec-94 Dec-96 Dec-98 Dec-00 Dec-02 Dec-04 Dec-06 -0.2 -0.4 -0.6 -0.8 • Empirical Fact 3: Risk Reduction Past 12 stocks, the risk in a portfolio levels off, around 27%. For international stocks, the risk levels off at 12% • Empirical Fact 4: Returns Increase Portfolios with international stocks have outperformed domestic portfolios in the past years. About 1% difference (1978-1993). Q: Free lunch? A: In the equity markets: Yes! Higher return (1% more), lower risks (2% less). Q: How to take advantage of facts 2 and 3? A: True diversification: invest internationally. Example: Higher Returns - The Case of Emerging Markets (EM) Example: Lower Risk/Higher Returns! Taken from H. Markowitz’s “A Random Walk Down Wall Street.” Example: Lower Risk/Higher Returns II -The Case of EM • Empirical Fact 5: Investors do not diversify enough Latest report by UBS (2002) on the proportion of foreign bonds and foreign equities in the total equity and bond portfolio of local residents for several OECD countries: - Most internationally diversified investors: Netherlands (62%), Japan (27%) and the U.K. (25%). - U.S. ranks at the bottom of list: only 11%. This empirical fact is called the Home Bias. Proposed explanations for home bias and low correlations: (1) Currency risk. (2) Information costs. (3) Controls to the free flow of capital. (4) Country or political risk. Related Question: What should be your international exposure? - GDP weighted? Related Question: What should be your international exposure? - GDP weighted? - Market capitalization weighted? Country Analysis • Active allocation strategy requires the forecast of changes in macroeconomic variables: currencies, interest rates, and stock markets. Key variable: Choice of a country (currency). But currency forecasting is difficult. Economists monitor a large number of variables such as - anticipated real growth (probably major influence on a national mkt.) - monetary and fiscal policy - wage and employment rigidities - social and political situations - competitiveness Country Risk Definition: Country Risk Country risk (CR) is the risk attached to a borrower by virtue of its location in a particular country. Q: Why do we care about CR? - MNCs make decisions on DFI projects on the basis of NPVs. - MNCs use discount rates to establish NPV for projects (the higher the discount rate, the lower the chances of a project to have a NPV>0). Q: Where do discount rates come from? A: For projects abroad, a key element is Country risk (CR) Note: CR is different than FX risk. CR risk can be zero and FX can be huge for a given country. The reverse, though unusual, can also happen. CR reflects the (potentially) negative impact of a country’s economic and political situation on an MNC’s or an investor’s CFs. Q: Does country risk analysis matter? A: Look at the Argentine default of 2001! Value of Argentine assets went down significantly. Global investors, MNCs, bondholders realize the relevance of country risk analysis. • Measures to reduce country risk: - A cap on the total amount invested in a particular country. - Diversification. • Similar to credit risk ratings, CR is measured (and reported) as a letter (A=excellent, C=bad) => Letter = Grade • Credit and Interest Rate Risk for Bonds: Brief Review Bonds are subject to two types of risk: 1) interest rate risk (risk associated to changes in interest rates) 2) credit/default risk (risk associated to the probability of default combined with the probability of not receiving principal and interest in arrears after default) Credit rating agencies describe (measure) the risk with a credit rating (a letter grade). Rule: The higher the grade, the lower the yield of the bond (measured as a spread over risk-free rate). (For us, the risk-free rate is the yield of government bonds). Two approaches to measure CR (and get a grade) (1) Qualitative – collect data, get an opinion from “experts,” form a “consensus” grade. (2) Quantitative – collect data, process the data with a computer model, get a grade. (1) Qualitative Approach: Talk to experts (politicians, union members, economists, etc) to form a consensus opinion about the risk of a country. The consensus opinion becomes the grade. This process is “subjective.” (2) Quantitative Approach: Start with some quantifiable factors that affect CR. Use a formula to determine numerical scores for each factor. Calculate a weighted average of the factors’ numerical scores. This weighted average determines the final grade. This process is (or seems more) “objective.” We will emphasize the Quantitative Approach. • We will associate CR to the spread over U.S. T-bills. That is, CR influences the interest on the debt issued by a government of a country. Example: Setting yields for Mexico (actually, the Mexican government) Yield on Mexican government debt = US Treasuries + spread (risk premium, a function of CR) Mexico’s grade: BBB -a spread of 140 bps (1.40%) over US Treasuries US Treasuries yield 4%. YieldMex = 4% + 1.40% = 5.40% Risk Rating Method (Check list) • Weighted average of grades for four major aspects of a country: - Economic Indicators (financial condition) - Debt management (ability to repay debt) - Political factors (political stability) - Structural factors (socioeconomic conditions) The grades (between 0 and 100) for each factor are a function of “fundamental data.” For example, the economic indicator’s grade depends on GDP per capita, GDP growth, inflation, interest rates, etc. (A formula is used to compute the grade.) Final Score = wEI Score(EI) + wDM Score(DM) + wPF Score(PF) + wSF Score(SF) Note: The weights should be positive and should up to 1 –i.e., wEI + wDM + wPF + wSF = 1. Q: Where are the weights and the formulae for the grades coming from? A: This method seems more “objective,” because it is based on hard economic data, but weights and formula for grades might be “subjective.” It’s more an art, than a science. The model can deliver different forecasts: Short-term, Medium-term, and Long-term. => The weights and grades can change depending on your horizon. For example: (a) Short-term: more weight to debt management and political factors. (b) Long-term: more weight to economic indicators and structural factor. Each grade is associated with a spread in basis points (bps) over base rate, usually the risk free rate. Note I: As time to maturity increases, the spread (in bps) also increases. Note II: A rating of BBB or better is considered “investment grade.” Note III: A rating of BB or less is considered “junk.” In the U.S., the usual spread of junk debt is between 400 to 600 bps over 1-yr T-bills. Range is very wide: Spreads can go over 2600 bps. Example: Bertoni Bank evaluates the country risk of country DX. Short-term Horizon Medium-term Horizon Factor Weight Grade Weight Grade Economic .3 80 24 .3 70 21 Debt managt .3 90 27 .2 70 14 Political .3 67 20.1 .2 50 15 Structural .1 75 7.5 .3 60 12 Total 78.6 63 Short-term ranking: A Medium-term ranking: BBB That is, the short-term debt of country DX will get a spread in the 80-130 bps range, say 93 bps over US Treasuries; while the medium-term debt will get a higher spread, say 128 bps. Suppose the short-term US Treasuries yield 4% (s.a.). Then, the short- term debt of country DX yields 4% (s.a.) + 0.93% (s.a.) = 4.93% (s.a.) ¶ International Factors in Stock Returns Q: What kind of factors explain security returns? (1) International (2) Domestic (3) Industrial Domestic vs. International Factors • We want to determine the relative importance of factors. A: Separately correlate each individual stock with: i. the world sock index (international factor) ii. the appropriate industrial sector index (international factor) iii.the currency movement (international factor) iv.the appropriate national market index (domestic factor) Example: We regress each individual stock against each factor and obtain its R2. Average R2 of Regression on Factors Single-Factor Model All Factors Market World Indust Curren Domestic Belgium .07 .08 .00 .42 .43 Germany .08 .10 .00 .41 .42 Norway .17 .28 .00 .84 .85 Spain .22 .03 .00 .45 .45 Sweden .19 .06 .01 .42 .43 France .13 .08 .01 .45 .60 Italy .05 .03 .00 .35 .35 Netherlands .12 .07 .01 .34 .31 U.K. .20 .17 .01 .53 .55 U.S. .26 .47 .01 .35 .55 Canada .27 .24 .07 .45 .48 Australia .24 .26 .01 .72 .72 Hong Kong .06 .25 .17 .79 .81 Japan .09 .16 .01 .26 .33 Singapore .16 .15 .02 .32 .33 All .18 .23 .01 .42 .46 => Domestic factors are the most important. Currency factor almost negligible (hedging adds value?) Valuation of MNFs • The extent of foreign operations for many MNFs raises the question: Can a portfolio of MNF stocks achieve true international diversification? A: No! MNFs do not provide all the benefits available from direct investment in foreign securities. Example: We examined firms from nine countries. ri = αi + ßUS rUS + ßNL rNL + ßBEL rBEL + ßGER rGER + ... Nationality Multiple Single of MNF Index Index US GER FRA SWI UK R2 beta R2 Amer. MNF .94 -.01 .02 -.01 -.07 .31 1.02 .29 German MNF .24 1.18 .10 -.15 -.11 .74 1.18 .65 French MNF -.10 .18 .95 -.22 .03 .62 1.08 .45 Swiss MNF -.12 -.09 -.11 1.74 .16 .75 1.39 .52 British MNF -.10 -.09 -.09 .07 .84 .49 1.06 .44 Conclusion: MNF stock prices are more affected by domestic factors. ¶ Possible explanations: - National control - Management policy - Government constraints International Capital Market Integration • Integration and The Pricing of Assets Capital Market Integration: Assets in different currencies or countries display the same risk-adjusted expected returns. Segmentation: The risk-return relationship in each national market is primarily determined by domestic factors. • Tests for integration: (1) Direct: measure barriers to capital movements. (Be careful with loopholes). (2) Indirect: measure stock prices and compare them. (A better measure). Q: Why do we care about International Capital Market Integration? A: (1) Choice of raising capital in two countries. (2) If segmentation, international portfolios should display superior risk-adjusted performance. Country Funds • Close-end funds (CEF) differ from open-end mutual funds: They neither issue nor redeem shares after IPO. To buy or sell shares, you have to go to the market. • Each CEF provides two market-determined prices: - The country fund's share price (P) quoted on the domestic market. - Its NAV determined by prices of the underlying shares traded on the foreign market. If P < NAV, closed-end fund sells at a discount. If P > NAV, closed-end fund sells at a premium. • CEF Puzzle: Domestic closed-end funds, on average, sold at a substantial discount during the 70's and early '80s. • Country CEF: Investment company that invests in a portfolio of assets in a foreign country and issues a fixed number of shares domestically. => Restrictions will raise P relative to its NAV by approximately the amount the marginal domestic investor is willing to pay to avoid them Example: On January 13, 1989: The Korea Fund's share sold at a 65% premium. The Brazil Fund sold at a 35% discount. ¶ • Restricted countries like Korea, Thailand and Taiwan sell at a premium. Less restricted countries like Germany and U.K. sell at a discount. Statistics for Premiums for Closed-End Country Funds (1981-1989) Fund or Portfolio Mean SD ρ1 Brazil -28.82 9.64 .92 Mexico -13.78 33.72 .97 France -20.18 8.41 .92 Germany -4.32 5.90 .77 U.K. -21.37 6.74 .72 Japan -11.73 10.50 .96 Korea 44.35 20.86 .93 Malaysia -7.46 19.75 .97 Taiwan 40.96 36.24 .96 Thai 25.46 12.45 .93 Country Funds -4.54 11.88 .89 Domestic Funds -11.22 5.58 .97 Example: The announcement of changes in investment restrictions decreased country fund premiums by an average of 6.8% in recent years. => Evidence favors International Market Segmentation: Financial restrictions to foreign investment work. Linkages between Stock Markets • The moderate to low correlation coefficients are a good argument internationally diversifying portfolios. • The analysis of correlation coefficients might not be that a correct tool. Example: Situation: No movement of capital is allowed between national stock markets. Common monetary policies induce positive correlations. In such a case, ex ante, or expected, returns could be very different across markets, even with highly correlated ex post returns. ¶ The Crash of October 1987 • Q: Why the October 1987 Crash is important? A: Only month during the 1980's where all the stock markets around the world moved in the same direction. • Q: How did the Crash start? A: The crash started in non-Japanese Asian countries and continued through European markets, the U.S. and finally Japan. The following Table reproduces the daily returns during the Pre-Crash Period, the Crash Period and the Post-Crash Period by Country Daily Returns (percent/day) by Country Country 1/2/87-10/12/87 10/12/87-10/30/87 11/2/87-3/31/89 Australia .2239 (0.850) -3.5160 (8.315) .0475 (1.216) Hong Kong .2218 (1.121) -5.4174 (12.072) .1083 (1.353) Japan .1543 (1.274) -0.9777 (5.567) .0810 (0.946) Malaysia .2821 (1.171) -3.6080 (6.026) .0128 (2.754) N. Zealand .0291 (1.091) -2.0473 (5.296) -.0755 (1.366) Singapore .2508 (1.075) -3.9675 (10.182) .1004 (1.327) Austria -.0202 (0.736) -0.8255 (1.663) .0699 (0.557) Belgium .0808 (0.814) -1.6531 (4.316) .0906 (0.965) France .0114 (0.920) -1.6526 (4.568) .1018 (1.254) Germany -.0296 (1.251) -1.5913 (4.178) .0254 (1.292) Italy -.0338 (1.017) -1.3943 (3.184) .0293 (1.149) Netherlands .0672 (0.993) -1.5985 (5.296) .0633 (1.301) Spain .2143 (1.276) -2.4154 (3.286) .0555 (0.927) Sweden .1272 (1.009) -1.8998 (4.534) .1202 (1.242) Switzerland .0156 (0.917) -2.0706 (5.409) .0025 (1.305) U.K. .1852 (0.865) -2.0759 (4.947) .0524 (0.962) Canada .1143 (0.689) -1.5150 (5.413) .0405 (0.772) Mexico .9831 (2.509) -3.4050 (6.892) .0128 (2.754) U.S. .1213 (0.965) -1.4128 (7.253) .0428 (1.094) Portfolio insurance and computer systems? Journalist and politicians blamed the Crash on a variety of source ranging from portfolio insurance to inadequate computer systems. • Finding: Claims totally unfounded. Many studies have found that countries with portfolio insurance crashed less that countries without it. Futures markets? The argument seems to be that irrational speculators cause instability. • Finding: Stock markets with related futures markets crashed in the same way as countries without futures exchanges. Specific event? Search for a triggering event: (1) announcement on October 14 of a worse than expected trade balance. (2) poor performance of Asian markets in the week before the Crash. (3) introduction in the U.S. Congress of anti-takeover legislation. • Finding: Last event is the most persuasive; however, it is difficult to believe that it had such a extraordinary effect in other markets. Speculative bubble? Eugene Fama, from the University of Chicago, says that the most questionable aspect of 1987 was not the Crash itself, but the incredible market advance during the previous five years. This apparent behavior has been attributed to a speculative bubble. Under this view, the most plausible theory for the Crash is that a speculative bubble burst in October 1987. It is difficult to test this hypothesis. Tests are usually based on autocorrelations. • Finding: Several studies have dismissed it as a plausible explanation for the October 1987 Crash. • Q: Can a Crash be avoided? • The immediate consequence of the Crash was a couple of reports by official agencies with recommendations. The proposed measures include: 1. increase in margin requirements 2. imposition of price limits 3. differential taxing for short and long positions • Finding: There is no evidence that margin requirements or price limits have any impact on stock price volatility. • Summary: - The Crash was an international event. - Countries with different regulations, controls, taxes and trading system. - All experienced a significant negative shock on October 1987. U.S. opening effect Use of daily data has a problem: overlapping trading hours. Difficult for some markets to distinguish: Common movement (caused by world factors) Specific movement (caused by domestic factors) Example: A positive commovement between NY and London (they share 2:30 hours of trading) might reflect common information or the influence of one specific market in the other. • Finding using intradaily data between NY and London: They only affect each other around the time New York is opening (9:30 AM, EST). Big movements, higher correlations When price changes are big, transaction costs become relatively unimportant. Transaction costs are a barrier for instantaneous arbitrage. Big price changes will bring world markets together. • Finding: Cross-market correlations tend to be positively correlated with measures of price volatility. Application: High Volatility, Correlations and Portfolio Choice • The lower the correlation between the assets, the greater are the benefits due to diversification. Empirical Fact: Recall the “Home bias." • Changes in correlations will affect the composition of optimal portfolios. • For the U.S. investor, the benefits of diversification change depending on the state of the volatility structure. => When you really want diversification (high domestic volatility), the benefits are lower (high overseas volatility). In Table X.7, the correlations between the U.S. and other major markets are calculated for two U.S. regimes: high volatility and low volatility.