Portfolio Volatility Research Articles

Low-Volatility Portfolio Construction: Ranking Versus Optimization

We analyze the low-volatility effect in the U.S. equity market with a focus on the common properties of various low-volatility strategies. Drawing from the extensive academic literature that exists on the topic, we examine the two major approaches to constructing low-volatility portfolios and apply them to the U.S. equity market: mean variance optimization–based and rankings-based approaches. Our analysis shows that both approaches are equally effective in reducing portfolio volatility over a long-term investment horizon. We then extend our analysis to the international and emerging markets. Our findings confirm that the low-volatility effect is not unique to the U.S. equity markets; it is present on a global scale.

Low- (Economic) Volatility Investing

Low-volatility investing has recently witnessed a surge in media coverage and experienced renewed interest in academic research. We assess various low- (economic) volatility portfolios against the S&P 500 Index, S&P 500 Low Volatility Index, and Fama–French-inspired U.S. Core Equity 1 Portfolio. Our portfolios outperformed the benchmarks, for the whole period as well as subperiods, and even more so when economic variables and criteria were incorporated with the down-market beta. This study shed further light on the efficacy of economic factors—that by constructing a low-volatility portfolio with economic factors would enhance a portfolio’s risk–reward ratio over a portfolio constructed purely with return-based measures.

Técnicas Quantitativas de Otimização de Carteiras Aplicadas ao Mercado de Ações Brasileiro

Neste artigo examinamos a aplicabilidade e o desempenho fora da amostra das estratégias quantitativas de otimização por média-variância e mínima-variância com relação ao desempenho da carteira ingênua igualmente ponderada (1/N) e da carteira teórica do índice Ibovespa, bem como avaliamos a estabilidade das composições ótimas obtidas. Na obtenção de carteiras ótimas, restritas para venda a descoberto, foram empregadas matrizes de covariâncias estimadas com base em cinco abordagens alternativas: matriz de covariância amostral, matriz RiskMetrics, e três estimadores propostos por Ledoit & Wolf (2003), Ledoit & Wolf (2004a) e Ledoit & Wolf (2004b). Tomando como base diferentes frequências de rebalanceamento das carteiras, as medidas de desempenho fora da amostra indicam que as estratégias quantitativas de otimização proporcionam resultados estatisticamente significativos em termos de menor volatilidade e desempenho ajustado ao risco superior. Além disso, o uso de estimadores mais sofisticados para a matriz de covariâncias gerou carteiras com menor turnover ao longo do tempo.

Black-Litterman Based Portfolio Optimization

This article presents an example of portfolios optimized by AlternativeSoft's BL model based optimization with the following objectives: - Minimize the portfolio volatility, - Produce a well-diversi fied portfolio, - Allow the investors to de fine their views (absolute as well as relative) on the funds' (mutual funds and hedge funds) expected returns, - Tilt the portfolio from an equally weighted portfolio towards the funds with good future expected returns if the investors are con dent about the future returns of these funds.

Leverage Aversion and Portfolio Optimality

Portfolio volatility is the only source of risk in mean–variance optimality, but it fails to capture all the risks faced by leveraged portfolios. These risks include the possibility of margin calls and forced liquidations at adverse prices and losses beyond the capital invested. To recognize these risks, the authors incorporated leverage aversion into the optimization process and examined the effects of volatility and leverage aversion on optimal long–short portfolios.

Indexing Volatility Risk Control

Volatility risk control strategies have become increasingly popular in this post-financial crisis environment where diversification as the sole form of portfolio risk management has been put under the microscope. The recent anomaly that investors may possibly reduce their overall portfolio volatility without sacrificing return has led to the success of several low volatility index launches over the past couple of years. This article studies the rapid evolution of volatility risk control indexes intended to gauge portfolio risk by targeting desired risk tolerances. <b>TOPICS:</b>Volatility measures, VAR and use of alternative risk measures of trading risk, mutual fund performance

The Low Volatility Effect: A Comprehensive Look

We analyze the low volatility effect in the U.S equity market with a focus on the common properties of various low volatility strategies. We examine the two major approaches to constructing low volatility portfolios and apply them to the U.S. equity market: mean-variance optimization-based versus the rankings or quantile-based approaches. Our analysis shows that both approaches are equally effective in reducing portfolio volatility over a long-term investment horizon. We then extend our analysis to the international and emerging markets. Our findings confirm that the low volatility effect is not unique to the U.S. equity markets; it is present on a global scale.

Aggregation of exponential smoothing processes with an application to portfolio risk evaluation

In this paper we propose a unified framework to analyse contemporaneous and temporal aggregation of a widely employed class of integrated moving average (IMA) models. We obtain a closed-form representation for the parameters of the contemporaneously and temporally aggregated process as a function of the parameters of the original one. These results are useful due to the close analogy between the integrated GARCH (1,1) model for conditional volatility and the IMA (1,1) model for squared returns, which share the same autocorrelation function. In this framework, we present an application dealing with Value-at-Risk (VaR) prediction at different sampling frequencies for an equally weighted portfolio composed of multiple indices. We apply the aggregation results by inferring the aggregate parameter in the portfolio volatility equation from the estimated vector IMA (1,1) model of squared returns. Empirical results show that VaR predictions delivered using this suggested approach are at least as accurate as those obtained by applying standard univariate methodologies, such as RiskMetrics.

"Relation Between Expected Return And Volatility At Bucharest Stock Exchange, On Business Cycle Stages "

The study of the relation between risk and return is an important topic forinvestors in financial assets, which is the reason why many researchers have tackled it. It isonly natural for an investor with aversion for risk, who undertakes a higher risk investment,mare to expect be rewarded accordingly, that is to achieve higher return rates. The researchconducted on various stock markets had contradictory results, which means that the existenceof such a connection is not certain on all stock markets. According to a new hypothesis,tackled by the latest studies, the aversion for risk of rational investors may be related to thestages of the business cycles. This paper deals with the connection between expected returnand volatility at Bucharest Stock Exchange, by analyzing the return and volatility of the BETindex portfolio. In order to assess this relation, we employed heteroskedastic autoregressivemodels. The study was conducted between January 2000 and April 2011, as well as duringtwo sub-periods determined by different business cycle phases: economic growth andrecession. The results revealed significant differences between the whole analyzed period andthe economic growth and recession sub-periods. By studying BSE return throughout theanalyzed period, we conclude that there is no relationship between expected return and risk,whereas volatility is asymmetric. Actually, one may witness a relation between return andrisk, as well as a non-asymmetric response of volatility to shocks during economic growth,and no risk-return relationship and asymmetric volatility during economic recession. Also,results have shown a positive relationship between return and volatility during economicgrowth, and a negative relationship between the same during economic recession.

Diversifying the Diversifiers and Tracking the Tracking Error: Outperforming Cap-Weighted Indices with Limited Risk of Underperformance

A number of quantitative or fundamental weighting schemes have been shown to produce robust outperformance with respect to standard cap-weighted equity indices over long time periods. Over periods ranging from a few months to a few years, however, such alternative weighting schemes can generate substantial downside risk relative to cap-weighted indices, which would be a source of concern for most investment managers or chief investment officers. In this article, the authors focus on two reasonable proxies for well-diversified, efficient frontier portfolios, namely, the maximum Sharpe ratio (MSR) portfolio and the global minimum volatility (GMV) portfolio. They address the question of how to use these building blocks to design an improved equity benchmark while satisfying target levels of average and extreme tracking error with respect to cap-weighted indices. The authors find that robust proxies for the GMV portfolio provide defensive exposure to equity that does well in adverse market conditions, while robust proxies for MSR portfolios provide greater access to the upside of equity markets. Because the relative performance of these two diversification approaches depends on market conditions, they expect a combination of both approaches to lead to a smoother conditional performance and higher probability of outperformance of the cap-weighted index, an intuition that is confirmed in empirical tests. Empirical analysis also suggests that “diversifying the diversifiers” still leads to high levels of relative downside risk, in particular when the performance of cap-weighted indices is unusually strong. In this context, the authors introduce an explicit relative risk control mechanism designed to reduce the consequences of severe short-term underperformance with respect to the cap-weighted index and confirm through out-of-sample empirical tests that “tracking the tracking error” would allow investors to achieve better access to outperformance per unit of extreme relative risk taken. Overall, the results reported in this article suggest that it is possible to achieve robust outperformance versus cap-weighted indices by diversifying model risk and by controlling relative risk compared to the cap-weighted indices. <bold>TOPICS:</bold> <ext-link>Portfolio construction</ext-link>, <ext-link>portfolio theory</ext-link>, <ext-link>factor-based models</ext-link>

The Death of Diversification Has Been GreatlyExaggerated

Diversification is famously referred to as the only “free lunch” in investing, but it has been under assault since the 2007–2009 global financial crisis, when virtually all longonly asset classes moved down together. Ilmanen and Kizer argue that the attacks are undeserved. Most investors were never as diversified as they thought they were, and there is ample room for improvement by shifting the focus from asset class diversification to factor diversification. They show that diversification into and across factors has been much more effective in reducing portfolio volatility and market directionality than asset class diversification. The benefits are greatest for long–short investing, which requires shorting and leverage but are also meaningful in a long-only context. <b>TOPICS:</b>Portfolio theory, analysis of individual factors/risk premia, financial crises and financial market history

Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR). The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporatesthe mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark ExponentialWeighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

Distribution Effect and Idiosyncratic Volatility Discount

Based on information signaling theory, this paper empirically shows that distribution effect matters for idiosyncratic volatility discount. Idiosyncratic volatility discount arises primarily among stocks with non-cash distributions. Such stocks show a significant idiosyncratic volatility discount, measured by the return difference between high and low idiosyncratic volatility portfolios. For stocks with cash distributions, this discount is statistically insignificant. We attribute these effects to the fact that the information content of cash distributions reduces the uncertainty of future capital gains, which in turn drives out the idiosyncratic volatility discount. We test several explanations of idiosyncratic volatility discount under the framework of distribution effect and find evidence contrary to those explanations. In addition, our empirical results suggest that extreme values may obscure some of the extant explanations on idiosyncratic volatility discount.

Scaling portfolio volatility and calculating risk contributions in the presence of serial cross-correlations

In practice, the daily volatility of portfolio returns is transformed to longer holding periods by multiplying by the square root of time, which assumes that returns are not serially correlated. Under this assumption, this scaling procedure can also be applied to contributions to the volatility of the assets in the portfolio. Close prices are often used to calculate the profit and loss of a portfolio. rading at exchanges located in distant time zones, this can lead to significant serial cross-correlations of the closing-time returns of the assets in the portfolio. These serial correlations cause the square-root-of-time rule to fail. Moreover, volatility contributions in this setting turn out to be misleading due to nonsynchronous correlations. We address this issue and provide alternative procedures for scaling volatility and calculating risk contributions for arbitrary holding periods.

Bayesian Value-at-Risk and Expected Shortfall for a Large Portfolio (Multi- and Univariate Approaches)

A. Pajor and J. Osiewalski Department of Econometrics and Operations Research, Cracow University of Economics Rakowicka 27, 31-510 Krakow, Poland Bayesian assessments of value-at-risk and expected shortfall for a given portfolio of dimension n can be based either on the n-variate predictive distribution of future returns of individual assets, or on the univariate model for portfolio volatility. In both cases, the Bayesian VaR and ES fully take into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this (more demanding) multivariate approach and the much simpler univariate approach based on modelling volatility of the whole portfolio (of a given structure).

Evaluating Covariance Forecasts Via Mean-Variance Portfolio Decisions

This paper presents an empirical comparative study of di fferent covariance estimators. The Engle-Colacito test is used for an indirect evaluation of alternative out-of-sample covariance forecasts in a portfolio setting for varying sample sizes, short selling constraints and market conditions. Errors in the estimation of variances have a higher impact on realized portfolio variance than errors in the estimation of covariances. Bayesian shrinkage estimators and the orthogonal GARCH estimator of covariance matrices lead to signi ficantly lower realized portfolio volatility compared to benchmark estimators.

Security Transaction Taxes and Market Quality

We study changes in market quality variables associated with nine modifications to the New York State Securities Transaction Tax (STT) between 1932 and 1981. We find that when there is an increase in the level of an STT, individual stock volatility increases, bid-ask spreads widen, price impacts are greater, and volume decreases. Our evidence suggests that there is no significant relation between the level of an STT and volatility of portfolios. We examine the propensity of traders to switch trading locations to avoid the tax and find mixed evidence that they will change locations.

Improving Portfolio Selection Using Option-Implied Volatility and Skewness

Our objective in this paper is to examine whether one can use option-implied information to improve the selection of mean-variance portfolios with a large number of stocks, and to document which aspects of option-implied information are most useful for improving their out-of-sample performance. Portfolio performance is measured in terms of volatility, Sharpe ratio, and turnover. Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility. Using option-implied correlation does not improve any of the metrics. Using option-implied volatility, risk-premium, and skewness to adjust expected returns leads to a substantial improvement in the Sharpe ratio, even after prohibiting shortsales and accounting for transactions costs.

The Determinants of Household Savings in South Africa

In South Africa, substantial government dissaving as well as poor household savings performance has caused a decline in aggregate savings. Whilst government dissaving has been successfully reversed, household savings continue to fall. Low domestic savings have required South Africa to attract large, volatile portfolio capital inflows to fund a structural current account deficit. Repeated reversals of such inflows have constrained domestic growth and hence an understanding of the factors that have caused this decline in savings is essential in order to formulate policies supportive of sustained higher rates of economic growth. Within the context of the existing literature, this article examines the various determinants of household savings using a vector error-correction model (VECM). The results suggest that interest rates, a wealth effect and upturns in the business cycle all contribute to explaining the decline in household savings. The presence of a partial offset between household savings and government savings also has important implications for the effectiveness of using the fiscal position of the South African government to boost savings.

Difference in interim performance and risk taking with short-sale constraints

Absent much theory, empirical works often rely on the following informal reasoning when looking for evidence of a mutual fund tournament: If there is a tournament, interim winners have incentives to decrease their portfolio volatility as they attempt to protect their lead, while interim losers are expected to increase their volatility so as to catch up with winners. We consider a rational model of a mutual fund tournament in the presence of short-sale constraints and find the opposite: Interim winners choose more volatile portfolios in equilibrium than interim losers. Several empirical works present evidence consistent with our model. However, based on the above informal argument, they appear to conclude against the tournament behavior. We argue that this conclusion is unwarranted. We also demonstrate that tournament incentives lead to differences in interim performance for otherwise identical managers and that mid-year trading volume is inversely related to mid-year stock return.