Piet Sercu

Home Bias in International Equity Portfolios: A Review

This paper reviews the recent literature on equity home bias - the empirical finding that people over invest in domestic stocks relative to the theoretically optimal investment portfolio. We cover different home bias measures and we illustrate the extent and the evolution of equity home bias both with recent portfolio holdings data and longer time series. Institutional-based and behavior-based explanations for the puzzle are considered and discussed. We conclude that none of the proposed theories can explain the full extent of the bias by itself, thus we argue that international portfolio choice should be explained by a mixture of rational and irrational behavior.

Exchange rate volatility and international trade: A general-equilibrium analysis

Abstract We analyze the relation between exchange-rate volatility and the volume of international trade, in a general-equilibrium stochastic-endowment economy with imperfect international commodity markets, and treating both variables as endogenous. Even in the simplest model, the sign of the relation depends on the source for the change in volatility. For instance, more volatility of the endowments and higher costs to international trade both boost exchange risk (and lower welfare); but the first increases the expected volume of trade, while the second decreases trade. Note that even the (inter-equilibria) relation between trade and welfare is ambiguous.

The Equity Home Bias Puzzle: A Survey

Home bias - the empirical phenomenon that investors assign anomalously high weights to their own domestic assets - has puzzled academics for decades. Financial theory predicts that an internationally well diversified portfolio of stocks and short-term bonds can reduce risk significantly without affecting expected return. Although the globalization of international equity markets has increased international investments, equity portfolios remain severely home biased today, and no single explanation seems to solve the puzzle completely. In this paper, we first provide a thorough description of the equity home bias phenomenon by defining, discussing, and applying the competing measures and presenting some estimates of the costs of under-diversification. Second, we evaluate the explanations for the equity home bias proposed in the literature such as information asymmetries, behavioral aspects, barriers to foreign investment, and governance issues, and conclude that each explanation on its own falls short, suggesting that the equity home bias probably reflects a combination of factors. Lastly, we review the implications of international under-diversification for portfolio formation and the cost of capital of companies.

The information content in bond model residuals: An empirical study on the Belgian bond market

We estimate both one-factor Vasicek and CIR bond pricing models and the cubic spline model using Belgian government bond data on each trading day over 1991-1992. We observe humped zero-yield curves with the two economic models but not the spline model during the period. The CIR model scores better than the Vasicek model in terms of goodness-of-fit (an average Std.Err of 12.4 bp versus 13.5 bp), but the curve-fitting spline model easily beats the two economic models with much smaller model residuals (8.0 bp). To make economic sense, we further test whether bond model residuals contain useful information for forecasting bond holding period returns. First, we find that the abnormal bond holding period returns, which are measured by three alternative benchmarks, are negatively related to once- and twice-lagged bond pricing errors. Second, we form trading strategies that exploit the mispricing based on alternative bond models. We find that buying underpriced bonds and selling overpriced bonds generates significant abnormal returns even when the trade is delayed by up to five days after observing the mispricing. Nevertheless, the magnitude of abnormal returns decreases dramatically with the delayed days. In light of that bond pricing errors contain both information and noise, further tests reveal that large model residuals are more likely caused by mis-specification and/or -estimation than small or medium-sized residuals. More profound, the curve-fitting model seem to overfit the data because it shows the least ability to detect mispricing.

Hedging with Two Futures Contracts: Simplicity Pays

We propose to use two futures contracts in hedging an agricultural commodity commitment to solve either the standard delta hedge or the roll-over issue. Most current literature on dual-hedge strategies is based on a structured model to reduce roll-over risk and is somehow dicult to apply for agricultural futures contracts. Instead, we propose to apply a regression based model and a naive rules of thumb for dual-hedges which are applicable for agricultural commodities. The naive dual strategy stems from the fact that in a large sample of agricultural commodities, De Ville, Dhaene and Sercu (2008) find that garch-based hedges do not perform as well as ols-based ones and that we can avoid estimation error with such a simple rule. Our expectations-based naive hedge ratios are driven from two conditions: omitting exposure to spot price and eliminating the expected basis eects on the portfolio values. It is also noisy, though, so the first-pass hedge ratios need to be smoothed. (We use a modified version of Gelper et al. (2007) for the purpose.) We find that, generally, (i) rebalancing helps; (ii) the two-contract hedging rules do better than the one-contract counterparts, even for standard delta hedges without rolling-over; (iii) simplicity pays: within the two-contract group, the expectations-based naive rule systematically beats the others in most cases and garch performs worse than ols for either one-contract or twocontract hedges; and (v) smoothing procedure does help even for regression models. These conclusions are based on the tests on unconditional variance (Diebold and Mariano (1995)) and those on conditional risk (Giacomini and White (2006)).

Countertrade in International and Domestic Markets

The motivation to countertrade (CT) can be attributed to many factors including circumventing credit and foreign exchange problems, surmounting barriers to otherwise closed markets, hiding price cuts, or simply the need by exporters to remain competitive. However, CT arrangements essentially constitute packages of buying, selling, and financing contracts. Hence, to explain CT requires explanation of why package deals are preferred to a set of component contracts. Examination of domestic CT with “international ramifications” stripped out suggests the inherent rationales are wishes to hide price cuts and to overcome cash shortages, motivations that are consistent with data collected from U.K. and Canadian firms, which also stress problems of contracting complexity and of finding uses for the countertraded goods.

Relative PPP in the medium run

Abstract This paper tests the relative purchasing power parity (RPPP) hypothesis on month-by-month, post-1972 data, and still obtains regression coefficients that are close to unity. Two methodological aspects have contributed to this encouraging result. Firstly, although all RPPP tests are vis-a-vis the USA, we nevertheless exploit our a priori knowledge about the implications of these USD-based equations on PPP relations between cross-rates. So, in a sense, we use all information implicit in all cross-rates too. Secondly, we selected an instrumental variable that is specifically designed to cope with lead-and-lag effects in non-traded vs traded-goods inflation. Our estimates indicate that most lead-and-lag effects seem to occur within a six-month window. (JEL F31).

Bond options and bond portfolio insurance

Abstract The paper summarizes the basics of bond price modeling, interest option pricing, and interest risk management, and discusses some of the best-known published special models. Both one- and two-factor models are covered, in continuous time and in a binomial setting

Tactical Asset Allocation with Commodity Futures: Implications of Business Cycle and Monetary Policy

While Gorton and Rouwenhorst (2005) suggest using business cycle in tactical asset allocation with commodity futures, Jensen et al (2002) suggest using monetary policy in guiding the timing of investment. We investigate whether it is useful to watch both.We find that clever timing in asset allocation, taking into account both business cycle and monetary policy, does perform better than either the Gorton and Rouwenhorst (2005) or Jensen et al (2002) strategies. This is true in-sample, also true out-of-sample, and significant - as long as one ignores announcement lags. In short, there is indeed an interaction effect between the effects of the two economic variables. However, the benefits from clever timing of ones asset allocation seem much reduced by the late announcement of the business cycle. When stand-alone trading in commodity futures is evaluated, actually, the strategies even underperform buy and hold. When optimal multi-asset portfolios are constructed, the news is not that negative: the combined allocation rule does offer. Still, even then the returns or standardized excess returns are not significantly better. We even find that static diversified portfolios with commodity futures do not have significantly higher returns and Sharpe ratios than static portfolios without commodity futures. In short, the earlier recommendations may have been too optimistic about the role of commodities for financial investment purposes.

The Forex Forward Puzzle: The Career Risk Hypothesis

We conjecture that the forward puzzle may reflect career risks. When professional investors observe public danger signals about a currency, they require a premium for holding it. We find evidence of this in Exchange Rate Mechanism rates. As deep discounts do signal danger, we next specify nonlinear variants of the Fama regression to capture this risk. We also decompose the forward premium into a long-memory trend and short-term component. We find empirical evidence for a career risk premium; risk is in fact dominant in the trend component while the short-term component loads more on expectations. All confidence intervals are calculated via Monte Carlo.