Kirt C. Butler

Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia

Abstract This study examines stock returns in Saudi Arabia and Kuwait over the period 1985–1989. The Kuwaiti market is similar to other thinly traded markets in the proportion of individual stocks exhibiting statistically significant autocorrelations and price change runs. In contrast, all 35 Saudi stocks show a significant departure from the random walk. The mean Saudi autocorrelation coefficient of −0.471 is opposite in sign and is huge in magnitude in comparison to autocorrelations reported in other stock market studies. Institutional factors contributing to market inefficiency include illiquidity, market fragmentation, trading and reporting delays, and the absence of official market makers.

Are the Gains from International Portfolio Diversification Exaggerated? The Influence of Downside Risk in Bear Markets

The fundamental rationale for international portfolio diversification is that it expands the opportunities for gains from portfolio diversification beyond those that are available through domestic securities. However, if international stock market correlations are higher than normal in bear markets, then international diversification will fail to yie ld the promised gains just when they are needed most. We evaluate the extent to which observed correlations to monthly returns in bear, calm and bull markets are captured by three popular bivariate distributions: (1) the normal, (2) the restricted GARCH(1,1) of J. P. Morgans RiskMetrics, and (3) the Student-t with four degrees of freedom. Observed correlations during calm and bull markets are unexceptional compared to these models. In contrast, observed correlations during bear markets are significantly higher than predicted. Higher-than-normal correlations during extreme market downturns result in monthly returns to equal-weighted portfolios of domestic and international stocks that are, on average, more than two percent lower than those predicted by the normal distribution. If the extent of non-normality during bear markets persists over time, then a U.S. investor allocating assets into foreign markets might want to allocate more assets into foreign markets with near-normal correlation profiles and avoid markets with higher-than-normal bear market co-movements.

Risk, diversification, and the investment horizon

T he asset allocation decision is the single most important financial decision facing individual investors and portfolio managers. For both individual investors and fund managers, asset choice is largely determined by the investment or performance horizon. Individual investors with long investment horizons usually ”take the long view” and invest a greater proportion of wealth in equities than in debt securities. The portfolio manager, facing periodic performance evaluation, however, is faced with pressure to outperform similar risk portfolios in every period. Unless the portfolio manager is able to time market moves successfully, long-run performance is determined largely by the proportion of time the fund is invested in equity versus debt securities. “Time diversification” is the idea that the risk of meeting an investment objective can be reduced if risky portfolios with high expected returns are held over long periods of time. Time diversification is possible because security returns are not perfectly positively correlated over time. The debate about the impact of time diversification on portfolio risk usually takes the form of a “stock versus bond” comparison over various holding periods. . The method used to forecast future stock and bond returns is central to this debate. For instance, Leibowitz and Langetieg [1989, p. 611 develop a simulation model based on assumed relationships between stock and bond returns and inflation and claim that ”there is a 36% chance that stocks will underperform bonds over a five-year horizon and even a 24% chance over twenty years.” In a rebuttal, Ambachtsheer [ 19891 observes that Leibowitz and Langetieg’s conclusions are inconsistent with capital market history and questions the assumptions in their simulation model. All decisions, including financial decisions, are made by first identifying possible alternatives and then selecting the best alternative according to personal preference. Studies such as Leibowitz and Langetieg’s are valuable because they more clearly define the potential outcomes of alternative investment strategies. Investors can then make a more informed choice from among available investment alternatives. Two factors hamper estimation of the return distributions facing investors over long holding periods. First, little is known about what long-run returns and risks to expect from different asset classes because we have so few observations of long holding period returns. Second, return variabilities and risk premiums do not appear to be constant over time, so extrapolations from past data must be tempered with common sense as well as knowledge of prevailing market conditions. Despite these limitations, capital market history represents an objective foundation on which to base forecasts of future investment performance, especially for long holding periods. We use observed capital market history and an empirical resampling procedure to estimate the impact of time diversification on portfolio risk.

Long-run returns on stock and bond portfolios: Implications for retirement planning

Abstract This paper presents asset returns over long holding periods in a form useful for retirement planning. Time diversification, heretofore analyzed for lump-sum investments, still serves to reduce the risk of stock investments when funds are accumulated month by month. We consider investments in five stock and bond asset classes as well as various asset allocation strategies. Probability distributions are computed for retirement wealth over a range of investment horizons.

The relative contribution of conditional mean and volatility in bivariate returns to international stock market indices

We compare the relative contribution of conditional mean and conditional volatility terms in vector autoregression–exponential generalized autoregression conditional heteroskedasticity models of bivariate returns to international stock indices. Conditional mean terms are relatively unimportant for bivariate returns to country pairs that trade synchronously such as Australia/Japan, where they account for only 8% of the increase in log-likelihood over an unconditional model, on average. They are more important in nonsynchronous domestic/world-ex-domestic series such as Japan/world-ex-Japan, where they account for 24% of the increase in log-likelihood over an unconditional model, on average. Despite their increased prominence in the domestic/world-ex-domestic series, conditional mean terms detract from residual behaviours in these series. They also detract from some out-of-sample return and volatility predictions in both synchronous and nonsynchronous series.

A Test for Long Memory in the Conditional Correlation of Bivariate Returns to Stock and Bond Market Index Futures

Long memory is found in the conditional volatilities of financial returns measured at daily or higher frequencies, as well as in residual cross-products in bivariate series. We test for long memory in conditional correlations by extending the fractionally integrated GARCH model to include previous conditional correlations and standardized cross-products in a bivariate system. We apply the model to stock-stock and stock-bond futures index returns using Engle’s (2002) two-stage dynamic conditional correlation approach. We find that cross-products are indeed long memory processes, but that this feature arises from long memory in conditional volatilities and not from long memory in conditional correlation.

Bivariate and higher-order terms in models of international equity returns

Nonsynchronous measurement induces significant higher-order auto and serial cross correlations in observed bivariate returns and squared returns to international equity indices. In order to investigate the statistical and economic significance of bivariate and higher-order terms in conditional models of international equity returns, we fit a VMA(2)–EGARCH(2,2) model with normal errors and a constant conditional correlation using MSCI index pairs for Japan, the UK and the USA. First-order univariate and bivariate conditional mean and volatility terms are statistically significant in each series. Second-order own- and cross-volatility terms also are significant, although second-order conditional mean terms are not. We investigate the economic significance of bivariate and second-order terms by comparing the return and volatility predictions of various models using out-of-sample regressions of returns or squared returns on conditional means or volatilities. Bivariate terms significantly improve return prediction in three of six series and volatility prediction in one of six series. Higher-order conditional volatility terms do not improve predictions of returns and squared returns despite the fact that they are statistically significant in these series. We conclude that it is important to include cross-effects, but not higher-order effects, when modelling conditional returns to international stock market indices.