THE ECONOMIC GAINS OF TRADING STOCKS AROUND HOLIDAYS

I assess the economic gains of strategies that account for the effect of holiday calendar effects on the daily returns and volatility of the 30 stocks in the Dow Jones Industrial Average index. The dynamic strategies use forecasts from stochastic volatility models that distinguish between regular trading days and different types of holidays. More important, I assess the economic value of conditioning on holiday effects and find that a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy that does not account for the effect of holidays on daily conditional expected returns and volatility to a strategy that does. This result is robust to reasonable transaction costs.     

On the distribution and estimation of trading costs

This paper investigates the uncertainty about the trading costs associated with a given portfolio strategy. I derive accurate approximations of the ex ante probability distributions of proportional trading costs and portfolio turnover under the conventional assumption of normal asset returns. Based on these approximations, I express the expected trading costs as a function of asset and portfolio characteristics. All else equal, the expected trading costs increase with: i) the deviations of the expected asset returns from the expected portfolio return, ii) the assets' volatility and iii) the portfolio volatility. At the same time, they decrease with the covariance between the assets and the portfolio. Furthermore, I propose novel estimators of the expected turnover and trading costs and show that they offer small bias and low variance, even when the sample size is small. Finally, I incorporate my results into a portfolio selection framework to produce portfolios with low levels of risk and trading costs. Several experiments with real and simulated data confirm the practical value of the results.     

PORTFOLIO MANAGEMENT: THE ROLE OF CALIBRATION,SHARPNESS, AND UNCERTAINTY

I evaluate the out‐of‐sample predictability of several major indicators for bull and bear markets in monthly S&P 500 series with three quadratic probability score components: calibration, sharpness, and uncertainty. I find that uncertainty limits the trend characterization and thus provides a new perspective from which to identify bull and bear markets. I also find that sharpness plays a key role in determining portfolio returns. Trading strategies that capitalize on sharpness generate higher Sharpe ratios and portfolio returns. The Aruoba–Diebold–Scotti business conditions index is the most profitable indicator for both medium‐ and long‐term trends.     

The performance of international asset allocation strategies using conditioning information

The existence of predictable components in conditional expected returns has been widely reported. We propose a test of the economic significance of this phenomenon by designing dynamic international allocation strategies based on a conditioning information set. We compare the performance of these dynamic strategies with some market portfolio benchmark and with unconditionally efficient portfolios (among the set of primitive assets). We find the performance of the dynamic strategies to be superior. The difference is not only statistically significant, it is economically large.     

Mean–variance and mean–semivariance portfolio selection: a multivariate nonparametric approach

While univariate nonparametric estimation methods have been developed for estimating returns in mean-downside risk portfolio optimization, the problem of handling possible cross-correlations in a vector of asset returns has not been addressed in portfolio selection. We present a novel multivariate nonparametric portfolio optimization procedure using kernel-based estimators of the conditional mean and the conditional median. The method accounts for the covariance structure information from the full set of returns. We also provide two computational algorithms to implement the estimators. Via the analysis of 24 French stock market returns, we evaluate the in-sample and out-of-sample performance of both portfolio selection algorithms against optimal portfolios selected by classical and univariate nonparametric methods for three highly different time periods and different levels of expected return. By allowing for cross-correlations among returns, our results suggest that the proposed multivariate nonparametric method is a useful extension of standard univariate nonparametric portfolio selection approaches.     

Asset Allocation with a High Dimensional Latent Factor Stochastic Volatility Model

We investigate the implications of time-varying expected returnand volatility on asset allocation in a high dimensional setting.We propose a dynamic factor multivariate stochastic volatility(DFMSV) model that allows the first two moments of returns tovary over time for a large number of assets. We then evaluatethe economic significance of the DFMSV model by examining theperformance of various dynamic portfolio strategies chosen bymean-variance investors in a universe of 36 stocks. We findthat the DFMSV dynamic strategies significantly outperform variousbenchmark strategies out of sample. This outperformance is robustto different performance measures, investor’s objectivefunctions, time periods, and assets.     

TIME-VARYING FACTORS AND CROSS-AUTOCORRELATIONS IN SHORT-HORIZON STOCK RETURNS

In this paper I show that the lead-lag pattern between large and small market value portfolio returns is consistent with differential variations in their expected return components. I find that the larger predictability of returns on the portfolio of small stocks may be due to a higher exposure of these firms to persistent (time-varying) latent factors. Additional evidence suggests that the asymmetric predictability cannot be fully explained by lagged price adjustments to common factor shocks: (i) lagged returns on large stocks do not have a strong causal effect on returns on small stocks; (ii) trading volume is positively related to own- and cross-autocorrelations in weekly portfolio returns; and (iii) significant cross-autocorrelation exists between current returns on large stocks and lagged returns on small stocks when trading volume is high.     

Performance evaluation of portfolio insurance strategies using stochastic dominance criteria

This paper evaluates the performance of the stop-loss, synthetic put and constant proportion portfolio insurance techniques based on a block-bootstrap simulation. We consider not only traditional performance measures, but also some recently developed measures that capture the non-normality of the return distribution (value-at-risk, expected shortfall, and the Omega measure). We compare them to the more comprehensive stochastic dominance criteria. The impact of changing the rebalancing frequency and level of capital protection is examined. We find that, even though a buy-and-hold strategy generates higher average excess returns, it does not stochastically dominate the portfolio insurance strategies, nor vice versa. Our results indicate that a 100% floor value should be preferred to lower floor values and that daily-rebalanced synthetic put and CPPI strategies dominate their counterparts with less frequent rebalancing.     

Differences in Perstistence in Aggregated and Disaggregated UK Stock Returns: A Reconciliation

Studies of the persistence in the returns series of UK stocks, using inter alia variance ratios, have documented clear differences between the relatively low levels of persistence in individual security returns and the relatively high levels of persistence in the returns of portfolios composed of these same securities. In this paper, I reconcile this contrast by showing that portfolio return variance ratios should not be expected to reflect (own) persistence levels in the component security returns, but instead should reflect a 'cross-persistence' between the securities. I calculate synthetic portfolio variance ratios from measures of security return 'cross-persistence' and find that they replicate closely the observed portfolio return variance ratios, which provides empirical support for the theoretical results.     

Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix

The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new estimation framework that focuses on enhancing portfolio performance. The framework applies the statistical methodology of shrinkage directly to the inverse covariance matrix using two non-parametric methods. The first minimises the out-of-sample portfolio variance while the second aims to increase out-of-sample risk-adjusted returns. We apply the resulting estimators to compute the minimum variance portfolio weights and obtain a set of new portfolio strategies. These strategies have an intuitive form which allows us to extend our framework to account for short-sale constraints, transaction costs and singular covariance matrices. A comparative empirical analysis against several strategies from the literature shows that the new strategies often offer higher risk-adjusted returns and lower levels of risk.     

Pricing Currency Risks

The currency market features a small cross-section, and conditional expected returns can be characterized by few signals: interest differential, trend, and mean reversion. We exploit these properties to construct the ex ante mean-variance efficient portfolio of individual currencies. The portfolio is updated in real time and prices all prominent currency trading strategies, conditionally and unconditionally. The fraction of risk in these assets that does not affect their risk premiums is at least 85%. Extant explanations of carry strategies based on intermediary capital or global volatility are related to these unpriced components, while consumption growth is related to the priced component of returns.     

Using industry momentum to improve portfolio performance

Minimum-variance portfolios, which ignore the mean and focus on the (co)variances of asset returns, outperform mean–variance approaches in out-of-sample tests. Despite these promising results, minimum-variance policies fail to deliver a superior performance compared with the simple 1/N rule. In this paper, we propose a parametric portfolio policy that uses industry return momentum to improve portfolio performance. Our portfolio policies outperform a broad selection of established portfolio strategies in terms of Sharpe ratio and certainty equivalent returns. The proposed policies are particularly suitable for investors because portfolio turnover is only moderately increased compared to standard minimum-variance portfolios.     

What Drives the Cross‐Section of Credit Spreads?: A Variance Decomposition Approach

I decompose the variation of credit spreads for corporate bonds into changing expected returns and changing expectation of credit losses. Using a log‐linearized pricing identity and a vector autoregression applied to microlevel data from 1973 to 2011, I find that expected returns contribute to the cross‐sectional variance of credit spreads nearly as much as expected credit loss does. However, most of the time‐series variation in credit spreads for the market portfolio corresponds to risk premiums.     

Corporate governance and stock returns in Asia

Despite years of study, the impact of firm-level governance on stock returns is not clear, especially in non-U.S. markets. We investigate the returns of governance-based trading strategies in Asia, using bias-free return data and CLSA governance ratings. We argue that poor governance should be associated with higher market risk. We find that a portfolio of poorly governed firms has a higher market beta, higher expected return and higher realized return, compared with a good governance portfolio. In contrast to some earlier studies, we find no abnormal returns after adjusting for risk and country effects. Only investors who can predict in advance which firms will improve their governance can earn abnormal returns.     

Portfolio Optimization under Solvency Constraints: A Dynamical Approach

We develop portfolio optimization problems for a nonlife insurance company seeking to find the minimum capital required that simultaneously satisfies solvency and portfolio performance constraints. Motivated by standard insurance regulations, we consider solvency capital requirements based on three criteria: ruin probability, conditional Value-at-Risk, and expected policyholder deficit ratio. We propose a novel semiparametric formulation for each problem and explore the advantages of implementing this methodology over other potential approaches. When liabilities follow a Lognormal distribution, we provide sufficient conditions for convexity for each problem. Using different expected return on capital target levels, we construct efficient frontiers when portfolio assets are modeled with a special class of multivariate GARCH models. We find that the correlation between asset returns plays an important role in the behavior of the optimal capital required and the portfolio structure. The stability and out-of-sample performance of our optimal solutions are empirically tested with respect to both the solvency requirement and portfolio performance, through a double rolling window estimation exercise.     

Does a Held-to-Maturity Strategy Impede Effective Portfolio Diversification for Islamic Bond (Sukuk) Portfolios? A Multi-Scale Continuous Wavelet Correlation Analysis

There is a critical gap in the literature in studying the portfolio diversification opportunities available to sukuk investors and evaluating these in light of held-to-maturity strategies usually adopted by these investors. This article has made an initial attempt to study the portfolio diversification strategies for sukuk portfolios across heterogeneous investment horizons. Our findings critically indicate that returns between local currency sukuk in different markets generally have low levels of correlations across different investor holding periods, thus enabling both short and long-run portfolio diversification benefits. However, in contrast, international currency sukuk issued in different markets exhibits high levels of correlations in the longer-term investor holding periods. Also, in the domestic market context, returns on different classes of domestic sukuk are found to exhibit strong correlations in the longer-holding periods. Our findings critically highlight the feasibility of held-to-maturity sukuk investment strategies from a portfolio diversification perspective.     

Expected returns on value,growth, and HML

In this paper, I analyze the predictability of returns on value and growth portfolios and examine time variation of the expected value premium. As a primary tool, I use the filtering technique, which accounts for time variation in expected cash flows and explicitly exploits the constraints imposed by the present value relation. I demonstrate that returns on value and growth portfolios are predictable, and the predictability is stronger for growth stocks. Applying the filtering technique to the HML portfolio, I build a novel powerful forecaster for the value premium. The new forecaster appears to be only weakly related to business cycle variables.     

Foreign market portfolio concentration and performance

Using security holdings of 49,857 foreign investors on the Oslo Stock Exchange (OSE), I test whether concentrated investment strategies in international markets result in excess risk-adjusted returns. I find that investors with higher learning capacity increase returns, while investors with lower learning capacity decrease returns from the portfolio concentration. I measure learning capacity as institutional classification, geographical proximity to Norway, and cultural closeness to Norwegian investors (as based on the Hofstede cultural closeness measures). I conclude, consistent with the information advantage theory, that concentrated investment strategies in foreign markets can be optimal (disastrous) for investors with higher (lower) learning capacity.     

Timing and diversification: A state-dependent asset allocation approach

Abstract

The influence of changing economic environment leads the distribution of stock market returns to be time-varying. A conditionally optimal investment hence requires a dynamic adjustment of asset allocation. In this context, this paper examines the improvement in portfolio performance by simulating portfolio strategies that are conditioned on the Markov regime switching behaviour of stock market returns. Including a memory effect eliminates the empirical shortcoming of discrete state models, namely that they produce a standard and an extreme state in stock returns. So far, this has prevented the regimes from being used as a valuable conditioning variable. Based on a discrete state indicator variable, is presented evidence of considerable performance improvement relative to the static model due to optimal shifting between aggressive and well diversified portfolio structures.     

An Examination of UK Unit Trust Performance within the Arbitrage Pricing Theory Framework

This paper examines the performance of a sample of 101 United Kingdom unit trusts within an Arbitrage Pricing Theory framework and considers the relationship between performance and the investment objective, size and expenses of the trusts. Also, portfolio strategies using past trust performances to rank the trusts fails to generate significant abnormal returns relative to two different benchmark portfolios.