Momtchil Pojarliev

Are All Currency Managers Equal

Pojarliev and Levich present a post-sample study of currency fund managers showing that alpha hunters and especially alpha generators are more effective in providing diversification benefits for a global equity portfolio than currency managers who earn beta returns from popular style strategies or managers with high total returns regardless of their source. The authors’ study is unusual in that they 1) measure the alpha from currency investing using a simple factor model rather than using total excess returns, 2) use rankings of currency managers from an earlier published study and examine their performance truly out of sample, and 3) use data that reflect actual trades and returns earned by these managers so that the data are not contaminated by the usual biases in hedge fund databases. Their results suggest that a factor model approach to analyzing currency fund returns, coupled with the revealed degree of alpha and beta persistence in their data, offer institutional investors with large equity exposure a useful tool for improving performance.

The Impact of Currency Exposure on Institutional Investment Performance: The Good, the Bad, and the Ugly

Institutional investor portfolios typically hold a significant allocation of foreign currency denominated assets. Very often at least some of this currency exposure has been viewed as bad or unwanted. On the other hand, recent studies have concluded that currency is itself an asset class that offers investors favorable risk-adjusted returns that could benefit a typical portfolio of stocks and bonds. There are two basic types of currency management mandates. In a currency hedging mandate (currency overlay), investors strive to manage (usually meaning reduce) the risk associated with the foreign currency exposure in their international assets. With an absolute return mandate (currency alpha), investors seek to earn a positive return subject to acceptable risk levels. The existing literature analyses these two basic types of currency management mandates separately. In this study, we investigate their impact on institutional investment performance simultaneously. We find that both absolute return and currency hedging mandates can have a positive impact on the portfolio risk/return characteristics. Absolute return mandates have the potential to increase the portfolio return with little impact on volatility. Risk reduction mandates tend to reduce portfolio volatility with little impact on returns. Due to the safe haven status of the U.S. Dollar, higher hedge ratios provide diversification for US dollar based investors when needed most, namely in periods of negative equity returns. Combining both currency hedging and absolute return mandates improves overall investment performance while allowing managers to stay within their portfolio risk budget.

Is There Skill or Alpha in Currency Investing

In this paper, we provide an overview of the main features of active currency management programs, highlighting the mandates and the types of trading strategies that are often used. The traditional benchmark used to measure skill or alpha in currency investing is that the expected excess rate of return is zero. We offer an alternative standard where the expected rate of return is related to naive style factors based on strategies that an investor could adopt assuming no special expertise. We review empirical evidence on the performance of both individual currency fund managers and indices of managers using the alternative benchmark. We find that a large percentage of variation in currency fund returns can be attributed to style indices. As a result, performance measures and rankings of currency funds may vary greatly depending on the benchmark used. We review related empirical evidence on fund management styles and survivorship and discuss the implications for currency management strategy and setting currency fund management fees.

Global European portfolio construction: Does a changing volatility structure matter?: Research Articles

In market research, such as for the measure of the customer satisfaction, data are collected through questionnaires. Responses are often classified into ordered categories, so observed variables are ordinal and the rate of missing data may be very high. In this paper, a method for the analysis of a categorical and incomplete data matrix is proposed. Our methodology is applied to data collected by a market survey of Fiat Auto in order to show the latent dimensions underlying the customer satisfaction with car dealers. After multiple imputation of missing values the polychoric correlation matrix, measuring the manifest variables correlations, is computed and used as a proper input to factor analysis. Two factors underlying the several judgement items are thus obtained and their weights on the global judgement ordinal variable are then estimated by ordered probit regression. Copyright

Evaluating absolute return managers

One traditional measure of investment performance, the information ratio (IR), is defined as the active return (alpha) divided by the tracking error (the standard deviation of the active return). Calculating an IR is straightforward when the benchmark for performance is a buy-and-hold standard like the S&P 500. For absolute return managers, however, the typical benchmark is zero, meaning that any excess return is classified as alpha and deemed to represent the return from active management or skill. In this paper, we argue that this standard approach confuses beta returns and alpha returns. The former can be earned by following generic strategies that are easily implemented and often replicated by ETFs, while the later are associated with more original or complex strategies that more genuinely reflect unique skills or expertise. We propose a new performance metric that strips out beta returns associated with investment-style factors. This approach leads to a new statistic, the alpha ratio, which can dramatically impact the relative performance rankings of managers and provide a clearer signal of manager skill.

Volatility Forecasts and Value at Risk Evaluation for the MSCI North America Index

This paper compares different models for volatility forecasts with respect to the value at risk performance (VaR). The VaR measures the potential loss of a portfolio for the next period at a given significance level. We focus on the question if the choice of the appropriate volatility forecasting model is important for the VaR estimation. We compare the forecasting performance of several volatility models for the returns of the MSCI North America index. The resulting VaR estimators are evaluated by comparing the empirical failure rate with the forecasting performance.