Portfolio Volatility Research Articles

Explaining Foreign Support for China's Global Economic Leadership

AbstractWe analyze the factors that increase the likelihood that other nations will follow China's global economic leadership. While our theoretical framework incorporates the conventional argument that China pulls in followers with economic benefits, we focus on grievances with the current global order that have the effect of pushing countries toward the rising new leader. We find that grievances about global financial instability are particularly important push factors. Our results show that countries that have experienced more financial crises, more variable capital account policies, more volatile portfolio capital outflows, and more social unrest during IMF programs are more likely to support China's global leadership than leaders of nations that have been less exposed to these problems. We find no evidence that grievances about global governance, or grievances about discriminatory US trade policies, are related to foreign support for China's global economic leadership. Overall, our evidence is consistent with the interpretation that leaders want to reform and preserve the WTO and the IMF, which have worked reasonably well for them under US leadership. At the same time, they have incentives to follow China's economic leadership on global capital flows, emphasizing long-term infrastructure and development finance over short-term flows which, under the current order, have imposed large costs on many economies.

The Four Approaches to Managing Retirement Income Risk

Sequence-of-returns risk amplifies the impacts of investment volatility when taking distributions from a volatile investment portfolio in retirement. There are four techniques for managing this sequence risk: reduce the spending rate, adjust spending to portfolio performance, reduce portfolio volatility in the early retirement years, and draw from a buffer asset outside the portfolio to support spending when the portfolio is underperforming. We examine the peculiarities of sequence risk using an example with U.S. market returns which leads to wealth depletion after a thirty-year retirement. We show how minor spending adjustments can lead to dramatically different outcomes for remaining investment wealth at the end of the thirty-year retirement period. We provide examples for how each of these risk management techniques can be implemented in practice by integrating a wider range of tools to create the potential for greater retirement efficiency in terms of supporting the retirement financial goals with the available asset base. Examples include delaying Social Security, using annuities with lifetime spending protections, employing a rising equity glidepath in retirement, using a time segmentation or bucketing strategy, and creating a “buffer asset” with a reverse mortgage or whole life insurance.

Constructing inverse factor volatility portfolios: A risk-based asset allocation for factor investing

In this study, we investigate risk-based asset allocation approaches for factor investing strategies by constructing a multifactor portfolio based on the inverse weighting method. We propose the inverse factor volatility (IFV) strategy, which is the simplified variant of a factor risk parity, assuming constant factor correlation. In IFV portfolio construction, the portfolio's weights are determined by using scaled inverse factor volatility treated as a proxy for a targeted exposure in the optimization. Based on daily stock and index returns on global markets from 2002 to the end of 2017, we implemented the empirical analysis of IFV portfolios among three stock markets: Japan, Euro, and the US. The results obtained reveal that the IFV portfolios significantly outperformed market capitalization weighted portfolios by successfully acquiring factor risk premiums.

Mathematical modeling for a new portfolio selection problem in bubble condition, using a new risk measure

A portfolio selection model is developed in this study, using a new risk measure. The proposed risk measure is based on the fundamental value of stocks. For this purpose, a mathematical model is developed and transformed into an integer linear programming. In order to analyze the model's efficiency, the actual data of the Tehran Stock Exchange market are used in 12 scenarios to solve the proposed model. In order to evaluate the scenarios, data mining approaches are employed. Data mining methods which are used in this paper include ANFIS, decision tree, random forest, ADF, and GEP. The best method for scenario evaluation is GEP based on numerical results. Hence, the market values are evaluated by this algorithm. Software packages like MATLAB, GEP xpero tools, and LINGO are used to solve the model. Different trends of market value and fundamental value volatility in the optimum stock portfolio are determined. It is possible to examine the optimum portfolio profitability in different scenarios. By using real-world data, trends are extracted and analyzed. Results show that the developed model can be effectively applied in bubble situations.

Public-Private Partnership in Macroeconomic Risk Management

Macroeconomic risk is a financial risk associated with macroeconomic or political factors. There are at least three different areas of application of this term. It may relate to: economic or financial risk found in stocks and funds; political risk in different countries; impact of economic or financial factors on political risk. Macroeconomic risk can also relate to the types of economic factors that affect the volatility of investments, assets, portfolios and intrinsic value of companies over time.

The Specialness of Equally-Weighted Portfolios: The Rebalancing Fallacy

There is overwhelming evidence in equity markets that equally-weighted (𝐸𝑊) portfolios deliver higher average returns and higher Sharpe ratios than value-weighted (𝑉𝑊) portfolios. Rebalancing to fixed weights has typically been advanced as an argument to explain the overperformance of 𝐸𝑊 portfolios. The paper challenges the rebalancing to fixed-weights hypothesis and provides empirical evidence that the overperformance is entirely explained by portfolio volatility and that there is no additional contribution to returns from rebalancing. Conditionally, the long-short 𝐸𝑀𝑉 portfolios, (Equally Minus Value weighted), which are long 𝐸𝑊 portfolios and short 𝑉𝑊 portfolios are shown to overperform only to the extent that they are invested in high variance or in low market capitalization securities. Furthermore, when looking at their dynamics, the overperformance is observed only in a few states of the world. The paper identifies the relevant state variables and documents evidence of predictability using information contained in lagged returns and in lagged volatility. Both variables are found to predict the 𝐸𝑀𝑉 portfolio returns positively. The information is exploited to test simple dynamic strategies which allow the 𝐸𝑀𝑉 portfolio to be held long or short depending on the signals contained in the two conditioning variables. The Sharpe ratios of various long-short dynamic strategies significantly overperform the long-only strategy. When looking at risk-adjusted returns, factor models fail to explain the 𝐸𝑀𝑉 portfolio returns. However, 𝐸𝑀𝑉 portfolios display pricing performance by driving well known factors to statistical insignificance. More generally, the unconditional and the conditional results obtained for the 𝐸𝑀𝑉 portfolios are shown to depend on two cross-sectional correlations, the first between the securities’ active weights and their variances and the second between the securities’ active weights and their growth rates. The cross-sectional correlations are shown to be security and state dependent. The average return on the 𝐸𝑀𝑉portfolio can be positive or negative depending on which one of the two cross-sectional correlations dominates.

Naïve diversification in thematic investing: heuristics for the core satellite investor

In recent years, thematic exchange-traded funds (ETF) have given core satellite strategies a new impetus. Thematic investing attempts to participate in certain trends, or to serve any conceivable subjective interest such as ethics and sustainability by supplementing the corresponding ETFs to conventional ones. Hence, the question arises how to weight the thematic satellite in relation to the diversified core portfolio. Complex research and factor models are hardly suitable for private investors, and the short history of thematic products would not provide reliable information anyway. Therefore, this study develops naive diversification for thematic core satellite investors and provides three heuristics. The first strategies focus on portfolio and stock amounts; the later considers minimum concentration as an allocation rule based on the Herfindahl index. The heuristics prove to be useful and competitive to provide diversification regarding volatility of portfolio returns compared to a minimum variance optimization in out-of-sample tests. Hence, this study offers some pragmatic and truly practical aid for thematic investors.

Low-Volatility Strategy: Can We Time the Factor?

We show that the slope of the volatility decile portfolio's return profile contains valuable information that can be used to time volatility under different market conditions. During good (bad) market condition, the high- (low-) volatility portfolio produces the highest return. We proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio's return profile. Volatility timing is achieved by being aggressive during strong growth periods, while being conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a five-fold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the Information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets, while holding a concentrated growth portfolio during bull markets.

Practical Applications of Managing Risks Beyond Volatility

<h3>Practical Applications Summary</h3> In <b><i>Managing Risks Beyond Volatility</i></b>, from the Fall 2017 issue of the <b><i>Journal of Index Investing</i></b>, authors <b>Mehdi Alighanbari, Stuart Doole</b>, and <b>Dimitris Melas</b>, all of <b>MSCI</b>, discuss how well investment strategies that minimize price-volatility risk address other risks not directly targeted by a traditional minimum-volatility strategy, such as concentration, sustainability, and crowding. By applying factor models, the authors examine whether minimum-volatility strategies guard against these other risks, or if they do not, how such strategies could be modified to do so without compromising their fundamental mission of minimizing overall portfolio volatility. Minimum-volatility strategies, by selecting for low-volatility and weakly correlated assets, innately promote diversification. The authors demonstrate that adding a diversification constraint to portfolio management may in fact be counterproductive to a minimum-volatility investment approach. In contrast, mitigation of crowding risk, and, to some extent, sustainability risk, can be enhanced through portfolio-construction constraints without compromising a minimum-volatility strategys fundamental objective. Therefore, a minimum-volatility strategy properly incorporating some methodological constraints into portfolio design can effectively mitigate some risks other than volatility, especially from overvalued assets that may increasingly take over a minimum-volatility portfolio’s composition over time. <b>TOPICS:</b>Analysis of individual factors/risk premia, portfolio construction, ESG investing

Machine Learning Portfolios with Equal Risk Contributions

We investigate the use of machine learning (ML) to forecast stock returns in the Brazilian market usinga rich proprietary dataset. While ML portfolios can easily outperform the local market, the performanceof long-short strategies using ML is hampered by the high volatility of the short portfolios. We showthat an Equal Risk Contribution (ERC) approach significantly improves risk-adjusted returns. We furtherdevelop an ERC approach that combines multiple long-short strategies obtained with ML models, equal-izing risk contributions across ML models, which outperforms, on a risk-adjusted basis, all individualML long-short strategies, as well as alternative combinations of ML strategies.

A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio

In this paper, we have integrated numerous stylized facts, namely co-jumps, long memory, measurement error and leverage effects, in dynamic factor multivariate stochastic volatility (DfMSV) model for realized covariance measures using high frequency data. The aim is to examine the performance of these new models in reproducing characteristic features of foreign currency exchange series, which gather these stylized facts, and capturing the most of the information provided by financial time series. The dynamic conditional correlation model (DCC-DfMSV) combining Wishart autoregressive model (WAR) was applied to capture the selected stylized fact. The results proved that the DCC-DfMSV outperforms the asymmetric-DCC (ADCC), and fractionally integrated matrix-exponential-DCC (FIEDCC) models in forecasting future volatilities series. Along with the DfMSV model, the DCC-DfMSV combined with the overall stylized facts have proved to be better for forecasting portfolio volatility and providing better portfolio optimization, via efficient frontier, than the basic model.

Geographical optimization of variable renewable energy capacity in China using modern portfolio theory

The large-scale deployment of wind and solar, which are variable renewable electricity (VRE) technologies, is indispensable to decarbonise China’s power sector. However, variability in VRE outputs poses challenges in power system operation in terms of increased demand for backup and reserve capacity. These challenges can be effectively mitigated by “geographical smoothing”, because spreading VRE deployment over a large area largely reduces the variability associated with the collective output of VRE. Based on meteorological reanalysis data, this study characterised the return and volatility (i.e. mean and standard deviation of hourly capacity factor) per VRE asset in China at a high-resolution grid cell level. This enabled to identify the efficient frontier of optimal VRE portfolios that capture the geographical smoothing effect for China’s future power system, using modern portfolio theory. The portfolio volatility is minimized for each attainable return. We analysed key statistics of optimal portfolios, including technology shares, levelized cost of electricity and capacity factor at-risk values. Our results show complementarity between wind and solar in China, reflected in more optimal return-volatility performance of wind & solar portfolios, as compared to wind-only and solar-only portfolios. In addition, our results show that portfolios with unconstrained technology shares perform much better in return-volatility performance than portfolios with constrained technology shares. This suggests existing scenarios in literature with pre-defined shares of different VRE technologies might be sub-optimal. This study also shows that for optimal wind & solar portfolios a “firm” non-zero minimum portfolio capacity factor (1.4–5.5%) can exist with 100% availability.

Explaining Foreign Interest in China's Global Leadership

We analyze the factors that increase the likelihood that other nations will follow China’s global economic leadership. While our theoretical framework incorporates the conventional argument that China pulls in followers with economic benefits, we focus on grievances with the current global order that have the effect of pushing countries toward the rising new leader. We find that grievances about global financial instability are particularly important push factors. Our results show that countries that have experienced more financial crises, more variable capital account policies, more volatile portfolio capital outflows, and more social unrest during IMF programs are more likely to support China’s global leadership than leaders of nations that have been less exposed to these problems. However, we find no evidence that grievances about global governance, or grievances about discriminatory U.S. trade policies, are related to foreign support for China’s global economic leadership. Overall, our evidence is consistent with the interpretation that leaders want to reform and preserve the WTO and the IMF, which have worked reasonably well for them under U.S. leadership. At the same time, they have incentives to follow China’s economic leadership on capital flows, emphasizing long-term infrastructure and development finance over short-term flows which, under the current order, have imposed large costs on their economies.

A Novel Template for Understanding Priced Factors

Asset managers are increasingly incorporating equity factors that deviate from traditional academic definitions in their stock selection process. We show that these factors frequently exhibit strong industry biases, making it crucial to understand the interaction between factor exposure and traditional industry exposure. Industry exposure plays a major role in the risk profile of a portfolio, making unintended industry exposures costly. For an extensive set of 21 equity factors, beyond the standard academic factors, we examine which equity factors are rewarded for their industry allocation. This set spans the value, quality, momentum, low volatility, and size investment styles. We use a global and liquid investment universe, as is commonly used by large institutional asset managers. We find that equity factors from the same investment style, most notably momentum and quality, exhibit strong differences in their returns from industry allocation. Understanding the interaction between factors and industry exposures can lead to higher return premiums, and lower portfolio volatility without harming performance.

Life Cycle Goal Achievement or Portfolio Volatility Reduction?

This paper is concerned with the use of currently available technology to provide individuals, financial advisors and pension fund financial planners with detailed prospective financial plans tailored to an individual's financial goals and obligations. By taking account of all prospective cash flows of an individual, including servicing current liabilities, and simultaneously optimizing prospective spending, saving, asset allocation, tax, insurance, etc. using dynamic stochastic optimization, the paper addresses the question of the title by comparing the results of such a goal-based fully dynamic strategy with representative current best practices of the financial advisory industry. These include piecemeal fixed allocation portfolios for specific goals, target-date retirement funds and fixed real income post-retirement financial products, all using Markowitz mean variance optimization based on the very general goal of minimizing portfolio volatility for a specific portfolio expected return over a finite horizon. Making use of the same data and market calibrated Monte Carlo stochastic simulation for all the alternative portfolio strategies, we find that flexibility turns out to be of key importance to individuals for both portfolio and spending decisions. The performance of the adaptive dynamic goal-based portfolio strategy is found to be far superior to all the industry’s Markowitz-based approaches. Superiority is measured here by the certainty equivalent increase in expected utility of individual lifetime consumption (gamma) and the extra initial capital required by an individual to put the dominated strategy on the same expected utility footing as the optimal dynamic strategy (initial capital gap). These empirical results should put paid to the commonly held view amongst finance professionals that the extra complexity of holistic dynamic stochastic models is not worth the marginal extra value obtained from their employment. We hope that such approaches implemented in currently available technologies will rapidly find acceptance by individuals, financial advisors and pension funds to the genuine benefit of individual investors.

Trade Duration, Volatility and Market Impact

We perform an empirical investigation of 'market impact' of trades using a large dataset of transactions executed by institutional investors in the US equity market. We find that price variations during trade execution are mainly driven by the aggregate order flow imbalance rather than the direction or size of individual trades. We find the main determinants of the amplitude of these price variations to be market volatility and trade duration. By contrast, trade size and execution speed, as measured by the participation rate, are found to have little or no influence on 'market impact' for orderly trade executions. Conditional on trade duration, trade size is found to have little influence on price variations during execution. We find evidence for a square-root dependence of price changes on duration rather than trade size and propose a simple explanation for this dependence in terms of the well-known square-root scaling of volatility as a function of duration. Our explanation is consistent with previous empirical studies on market impact and provides a simple rationale for the ubiquity of the 'square-root law' in these studies. We also examine the role of the participation rate in determining 'market impact': using evidence from large VWAP trades with high participation rates, we show that, conditional on duration, even large changes in participation rate have negligible influence on market impact, contradicting the assumption, common in optimal execution models, that impact increases with the participation rate. In fact, we provide evidence for the opposite effect: for a given trade size, the slower the execution, the higher the amplitude of price variations during the trade. Our findings highlight the need to revisit some common models of market impact and their use in the design of optimal execution, and suggest that it is more meaningful to focus on the modelling of aggregate order flow dynamics and the management of portfolio volatility during execution rather than the optimisation of 'impact' at a trade-by-trade level.

Performance of Contrarian Investing for Shariah Compliant Stock Portfolio

Contrarian strategy is applied in the stock market by buying past losers and selling past winners. In this study, the profitability of contrarian strategy on Shariah compliant stock is explored for Malaysian stock market and the application is targeted for individual investors. Monthly closing price of 50 Shariah Compliant individual stock from January 2012 to December 2016 are utilized in this study. Shariah compliant individual stock portfolios based on contrarian strategy are constructed and the performance of the developed portfolio are measured. This study utilizes three ranking and holding period strategy which are the 12/12 strategy, 12/6 strategy and 6/6 strategy. The results indicate that contrarian investment strategy is profitable in Malaysian market using short-term ranking and holding period which is the 6/6 strategy. The return generated is 3.42% as compared to market benchmark of 2.78% for the same period. Thus, the strategy beats the market benchmark by 0.64%. Furthermore, this strategy gives the highest return per unit of risk taken by investors as measured by the portfolio volatility. This means that shortterm contrarian strategies may provide abnormal returns for Shariah compliant investors.

Bank Regulatory Reforms and Institutional Equity Holdings

This paper examines the effects of changes in bank regulatory environment on the risk, return, and liquidity characteristics of equity portfolios of U.S. bank holding companies between 1997 and 2016. Using a comprehensive sample of bank and hedge fund holdings data we examine the impact of the repeal of the Glass-Steagall Act, the introduction of the second and third Basel Capital Accords, and the implementation of the Dodd-Frank legislation on institutional portfolios. We document a significant increase in both the idiosyncratic volatility and illiquidity of banks' portfolios during the period of financial deregulation initiated by the formal removal of restrictions prohibiting banks from engaging in securities trading. In contrast, subsequent reforms of the bank capital requirements system and the prohibition of banks from proprietary trading activities lead to reductions in those metrics. Our results suggest that banks' restricted ability to engage in market-making can be offset by the activities of hedge funds, although the consequences of this substitution for long-term market stability remain unclear.

Financial Knowledge and 401(K) Investment Performance

Using a unique new dataset linking administrative data on investment performance and financial knowledge, we examine whether investors who are more financially knowledgeable earn more on their retirement plan investments, compared to their less sophisticated counterparts. We find that risk-adjusted annual expected returns are 130 basis points higher for the most financially knowledgeable employees, and those scoring higher on our Financial Knowledge Index have slightly more volatile portfolios while they do no better diversifying their portfolios than their peers. Overall, financial knowledge does appear to help people invest more profitably; this may provide a rationale for efforts to enhance financial knowledge in the population at large.

Basis Risk When Hedging a Global Credit Portfolio

We examine the efficiency of hedging a credit derivative portfolio with a contrary position in a credit index in the face of decreased correlations between single name CDSs and credit indices. The interest of such hedge comes from the fact that the calculation of the capital charge for CVA risk, as required by the Basel Committee on Banking Supervision, is usually rather unstable due to the volatility of CDS spreads. Since credit derivatives on single names are not very liquid, the implied adjustments in capital charges could be reduced by the mentioned hedging strategy, and we show that there is enough diversification of risk in a global credit portfolio to allow for a good hedge. Over the whole sample, the reduction in the variance of the portfolio’s profit and losses (P&L) is of 80% for a European portfolio, 60% for North American and Japanese portfolios, and around 70% for a global portfolio. Using a strategy that takes into account the quality of the credit counterpart improves the effectiveness of the hedge, although it requires using less liquid credit indices, with higher transaction costs. Standard conditional volatility models provide the same results as a least squares hedge. The efficiency of the hedge for a credit portfolio made up of the most idiosyncratic firms seems to require more than 50 firms, while the hedge for portfolios made up of the less idiosyncratic firms achieves high efficiency even for a small number of firms. The efficiency of the hedge is higher when portfolio volatility is high and also when short term interest rates or exchange rate volatility are high. Increases in VIX, in the 10-year swap rate or in liquidity risk tend to decrease hedging efficiency.