Thematic portfolio optimization: challenging the core satellite approach

In recent years, thematic exchange-traded funds (ETF) have increased in economic significance. Investors in thematic ETFs have more than just financial objectives and gain a non-monetary added value from a thematic portion in their portfolios. Therefore, traditional portfolio optimization models which target only financial criteria cannot suit these investors’ needs anymore. Nevertheless, to account for their thematic interests, investors adapt a core satellite strategy in which conventional core portfolios and thematic satellite portfolios are combined. Thus, these portfolios are separately optimized without further considering inter-portfolio correlation effects. Since modern portfolio theory has originally been established to, inter alia, optimize these correlation effects, portfolios can only be efficient by chance. Therefore, this study targets the correlation effects between conventional and thematic portfolios and uses a tri-criterion thematic portfolio optimization model as an overall framework. Throughout a two-part analysis with tradable ETFs and a simulation with 250,000 draws and 1,750,000 portfolio optimizations performed, the status quo is compared to the tri-criterion model. Quantifying the suboptimality, simulation results show a mean portfolio improvement of 6.23% measured as relative yield enhancement. Further, our analysis concludes that the more narrowly a theme is defined and the more particular it is, relative yield enhancements can increase up to 46.88%.

     

Modeling of Contagious Credit Events and Risk Analysis of Credit Portfolios

We present a new model of the occurence of credit events such as rating changes and defaults for risk analyses of some portfolio credit derivatives. The framework of our model is based on a so-called top-down approach. Specifically, we first consider modeling the point process of each type of credit event in the whole economy using a self-exciting intensity process. Next, we characterize the point processes of credit events in the underlying sub-portfolio using random thinning processes specified by the distribution of credit ratings in the sub-portfolio. One of the main features of our model is that the model can capture credit risk contagion simultaneously among several credit portfolios. We present a credit event simulation algorithm based on our model and illustrate an application of the model to risk analyses of loan portfolios.     

Optimal reserve composition in the presence of sudden stops

We analytically derive optimal central bank portfolios in a minimum variance framework with two assets and transaction demands caused by sudden stops in capital inflows. In this model, transaction demands become less important relative to traditional portfolio objectives as debt to reserve ratios decrease. We empirically estimate optimal dollar and euro shares for 23 emerging market countries and find that optimal reserve portfolios are dominated by anchor currencies and, at current debt-to-reserve ratios, introducing transaction demand has a relatively modest effect for most countries. We find that, in general, the dollar acts as a safe haven currency during sudden stops for country specific and global sudden stops, increasing the optimal share of dollar bonds in central bank portfolios. Correspondingly, our model predicts that dollar shares should decline as debt-to-reserve ratios fall, as observed in recent data. We also find that the denomination of foreign currency debt has little importance for optimal reserve portfolios.     

Haar wavelets-based approach for quantifying credit portfolio losses

This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is particularly suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. WA is an accurate, robust and fast method, allowing the estimation of the VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability.     

Simulation-based Value-at-Risk for nonlinear portfolios

Value-at-risk (VaR) has been playing the role of a standard risk measure since its introduction. In practice, the delta-normal approach is usually adopted to approximate the VaR of portfolios with option positions. Its effectiveness, however, substantially diminishes when the portfolios concerned involve a high dimension of derivative positions with nonlinear payoffs; lack of closed form pricing solution for these potentially highly correlated, American-style derivatives further complicate the problem. This paper proposes a generic simulation-based algorithm for VaR estimation that can be easily applied to any existing procedures. Our proposal leverages cross-sectional information and applies variable selection techniques to simplify the existing simulation framework. Asymptotic properties of the new approach demonstrate faster convergence due to the additional model selection component introduced. We have also performed sets of numerical results that verify the effectiveness of our approach in comparison with some existing strategies.     

Are good-news firms riskier than bad-news firms?

This paper examines the relative risk of good-news firms, i.e., those with high standardized unexpected earnings (SUE), and bad-news (low SUE) firms using a stochastic discount factor approach. We find that a stochastic discount factor constructed from a set of basis assets helps explain post-earnings-announcement drift (PEAD). The risk exposures on the pricing kernel increase monotonically from the lowest to highest SUE sorted portfolios. Specifically, good-news firms always have higher risk exposures than bad-news firms in both 10 SUE sorted portfolios and 25 size and SUE sorted portfolios. However, the estimated expected risk premium is too small to explain the observed magnitude of returns on the PEAD strategy. Our risk adjustment can explain only about one-fourth of the total magnitude of the average realized return to the PEAD strategy. As a result, the average risk-adjusted returns of earnings momentum strategies are mostly positive and significant. Overall, our results support the view that at least some portion of the returns to the earnings momentum strategies examined represent compensation for bearing increased risk.     

An analytic approach to credit risk of large corporate bond and loan portfolios

We derive an analytic approximation to the credit loss distribution of large portfolios by letting the number of exposures tend to infinity. Defaults and rating migrations for individual exposures are driven by a factor model in order to capture co-movements in changing credit quality. The limiting credit loss distribution obeys the empirical stylized facts of skewness and heavy tails. We show how portfolio features like the degree of systematic risk, credit quality and term to maturity affect the distributional shape of portfolio credit losses. Using empirical data, it appears that the Basle 8% rule corresponds to quantiles with confidence levels exceeding 98%. The limit law's relevance for credit risk management is investigated further by checking its applicability to portfolios with a finite number of exposures. Relatively homogeneous portfolios of 300 exposures can be well approximated by the limit law. A minimum of 800 exposures is required if portfolios are relatively heterogeneous. Realistic loan portfolios often contain thousands of exposures implying that our analytic approach can be a fast and accurate alternative to the standard Monte-Carlo simulation techniques adopted in much of the literature.     

Factor-based,Non-parametric Risk Measurement Framework for Hedge Funds and Fund-of-Funds

Abstract

A factor-decomposition based framework is presented that facilitates non-parametric risk analysis for complex hedge fund portfolios in the absence of portfolio level transparency. This approach has been designed specifically for use within the hedge fund-of-funds environment, but is equally relevant to those who seek to construct risk-managed portfolios of hedge funds under less than perfect underlying portfolio transparency. Using dynamic multivariate regression analysis coupled with a qualitative understanding of hedge fund return drivers, one is able to perform a robust factor decomposition to attribute risk within any hedge fund portfolio with an identifiable strategy. Furthermore, through use of Monte Carlo simulation techniques, these factors can be employed to generate implied risk profiles at either the constituent fund or aggregate fund-of-funds level. As well as being pertinent to risk forecasting and monitoring, such methods also have application to style analysis, profit attribution, portfolio stress testing and diversification studies. This paper outlines such a framework and presents sample results in each of these areas.     

Targeting market neutrality

Neutralizing portfolios from overall market risk is an important part of investment management, particularly for hedge funds. In this paper we show an economically significant improvement in the accuracy of targeting market neutrality for equity portfolios. Key features of the approach are the relatively short forecast horizon of one week and forecasting with realized beta estimators computed using high quality, error corrected, intraday returns. We also find that too long and too short estimation windows result in poor beta forecasts and that the optimal length of estimation window depends on the frequency of return observations.     

An empirical analysis of marginal conditional stochastic dominance

Stochastic dominance is a more general approach to expected utility maximization than the widely accepted mean–variance analysis. However, when applied to portfolios of assets, stochastic dominance rules become too complicated for meaningful empirical analysis, and, thus, its practical relevance has been difficult to establish. This paper develops a framework based on the concept of Marginal Conditional Stochastic Dominance (MCSD), introduced by Shalit and Yitzhaki (1994), to test for the first time the relationship between second order stochastic dominance (SSD) and stock returns. We find evidence that MCSD is a significant determinant of stock returns. Our results are robust with respect to the most popular pricing models.     

Modeling time variation and asymmetry in foreign exchange exposure

Many recent studies suggest that exchange rate exposure is unstable over time and exhibits asymmetric behavior during currency appreciations and depreciations. This paper proposes a dynamic framework for the study of such questions and our empirical findings show that exchange rate exposure of U.S. stocks is time varying. Using decile and sector portfolios, we find asymmetric exposure to be pervasive across the decile portfolios as well as the financial and industrial sectors. Moreover, the response of return variance to past innovations is asymmetric for the majority of cases. The dynamic exchange rate exposure parameters are found to be mean-reverting with low persistence, generally ranging from 1 to less than 2 days. The average time-varying exposure is statistically significant for the size-based and sector-based portfolios. Lastly, the variability in the time-varying exposure is smaller (larger) for the largest (smallest) firms and for industrial (technology) firms.     

Dynamic factor Value-at-Risk for large heteroskedastic portfolios

We propose a methodology that can efficiently measure the Value-at-Risk (VaR) of large portfolios with time-varying volatility and correlations by bringing together the established historical simulation framework and recent contributions to the dynamic factor models literature. We find that the proposed methodology performs well relative to widely used VaR methodologies, and is a significant improvement from a computational point of view.     

Myopic loss aversion and stock investments: An empirical study of private investors

Myopic loss aversion was suggested by Benartzi and Thaler (1995) as an explanation for the equity premium puzzle. Its main prediction is that loss averse investors, who evaluate their investment performance too frequently and therefore often observe small losses on their stock portfolios, would invest too little in equity. We investigate the link between myopic loss aversion and actual investment decisions of individual investors, using survey data. Our results are consistent with the predictions of Benartzi and Thaler. Higher myopic loss aversion is associated with lower stock investment as a share of total assets. Investors tend to evaluate their stock portfolio performance too often, which contributes to the prevalence of myopic loss aversion. The effect of myopia is most apparent when investors both evaluate their portfolios frequently and trade stocks regularly.     

Shrinkage estimation of Kelly portfolios

Although the Kelly portfolio is theoretically optimal in maximizing the long-term log-growth rate, in practice this is not always so. In this paper, we first show that the sample plug-in estimator of the Kelly portfolio weights is actually biased, and we then propose an unbiased estimator as an alternative. We further derive a shrinkage estimator under the objective of minimizing the expected growth loss of the actual growth relative to the true growth. An explicit formula for the shrinkage coefficient is established. Statistical properties for the shrinkage coefficient are studied through extensive Monte Carlo simulations, and conditions for obtaining accurate estimates for the shrinkage coefficient are also discussed. The effectiveness of the proposed unbiased and shrinkage Kelly portfolios in reducing the expected growth loss are validated by various simulation studies. It is found that our proposed shrinkage Kelly portfolio has superior performances in growth loss reduction, followed by the unbiased Kelly portfolio, and the sample plug-in Kelly portfolio. The advantages of our proposed unbiased and shrinkage Kelly portfolios for long-term investments are additionally confirmed by stock investment in the U.S. market.     

A Tractable Model to Measure Sector Concentration Risk in Credit Portfolios

We explore a simplified version of the value-at-risk approximation developed by Pykhtin (Risk Magazine, March, 85–90, 2004), which only requires risk parameters on a sector level. We measure the impact of credit concentrations in business sectors on the economic capital of credit portfolios. We base our portfolios’ sector composition on credit information from the German central credit register. Our results show that the approximation formula performs well for fine-grained portfolios that are homogeneous on a sector level in terms of probability of default (PD) and exposure size. We explore the robustness of our results for portfolios which are heterogeneous in terms of these two characteristics. We find that low granularity ceteris paribus causes the approximation formula to underestimate economic capital, whereas heterogeneity in individual PDs causes overestimation. Indicative results imply that in typical credit portfolios of banks, PD heterogeneity will at least compensate for the granularity effect. This result suggests that the approximation estimates economic capital reasonably well and/or errs on the conservative side.     

Bank lending opportunities and credit standards

This article empirically tests the hypothesis that credit-screening standards can be first increasing and then decreasing in the quality of the bank's pool of potential borrowers, which in turn may vary through the business cycle or across different segments of the lending markets. A key implication is that banks with lending opportunities toward the middle of the quality spectrum can have loan portfolios that perform better than do the portfolios of banks with loan-origination opportunities that are either too weak or too strong. Using banks’ volume of secondary-market loan sales as a proxy for the richness of lending opportunities, I find an inverse U-shaped relation between the performance of banks’ loan portfolios and their activity in the loan sales market. The pattern deserves scrutiny for its policy implications, as many regulators hold the view that countercyclical variation in credit standards may have a destabilizing effect on business cycles.     

THE ECONOMIC SIGNIFICANCE OF CONDITIONING INFORMATION ON PORTFOLIO EFFICIENCY IN THE PRESENCE OF COSTLY SHORT‐SELLING

The economic significance of conditioning information in the presence of costly short‐selling is investigated. Using a compact testing framework, results demonstrate that fixed‐weight stock‐bond portfolios appear inefficient with respect to stock‐bond portfolios with weights determined by extant predictors. However, this result is highly dependent on ex ante knowledge of the predictor set and the ability to short‐sell at low cost. In the absence of such conditions, fixed‐weight stock‐bond portfolios appear efficient with respect to conditioning information.     

Re-examining the small-cap myth: problems in portfolio formation and liquidation

This study investigates the realizable returns on portfolios at the turn-of-the-year. Using an intraday simulation that accounts for the volumes offered or wanted at market bid-ask prices, large-capitalization securities significantly outperform small-capitalization securities by 2.4% and 6.5%, depending on whether the portfolios were formed on the last day of the taxation year or were formed over the last month of the trading year. In no one year could the small-capitalization portfolio be completely divested by the end of the holding period, suggesting that investors are not remunerated for the illiquidity in this portfolio. Results based on returns calculated by using the mean of the bid-ask spread show that the results are not derived solely from transaction costs.     

Risk optimizations on basis portfolios: The role of sorting

This paper investigates the mean–variance and diversification properties of risk-based strategies executed on style or basis portfolios. We show that the performance of these risk strategies is highly sensitive to the sorting procedure used to form the basis assets. Whereas the extant literature provides mixed support for the outperformance of smart beta strategies based on scientific diversification, our designed strategies outperform both the market model and multifactor model. Our testing framework is based on bootstrapped mean–variance spanning tests and shows valid conclusions when controlling for multiple testing, transaction costs, and luck from random basis portfolio construction rules. Economically, our results are supported by diversification-based properties.     

Least-squares approach to risk parity in portfolio selection

The risk parity portfolio selection problem aims to find such portfolios for which the contributions of risk from all assets are equally weighted. Portfolios constructed using the risk parity approach are a compromise between two well-known diversification techniques: minimum variance optimization and the equal weighting approach. In this paper, we discuss the problem of finding portfolios that satisfy risk parity over either individual assets or groups of assets. We describe the set of all risk parity solutions by using convex optimization techniques over orthants and we show that this set may contain an exponential number of solutions. We then propose an alternative non-convex least-squares model whose set of optimal solutions includes all risk parity solutions, and propose a modified formulation which aims at selecting the most desirable risk parity solution according to a given criterion. When general bounds are considered, a risk parity solution may not exist. In this case, the non-convex least-squares model seeks a feasible portfolio which is as close to risk parity as possible. Furthermore, we propose an alternating linearization framework to solve this non-convex model. Numerical experiments indicate the effectiveness of our technique in terms of both speed and accuracy.