Portfolio Volatility Research Articles

Regularizing Portfolio Risk Analysis: A Bayesian Approach

It is important for a portfolio manager to estimate and analyze recent portfolio volatility to keep the portfolio's risk within limit. Though the number of financial instruments in the portfolio can be very large, sometimes more than thousands, daily returns considered for analysis are only for a month or even less. In this case rank of portfolio covariance matrix is less than full, hence solution is not unique. It is typically known as the ``ill-posed" problem. In this paper we discuss a Bayesian approach to regularize the problem. One of the additional advantages of this approach is to analyze the source of risk by estimating the probability of positive `conditional contribution to total risk' (CCTR). Each source's CCTR would sum up to the portfolio's total volatility risk. Existing methods only estimate CCTR of a source, and does not estimate the probability of CCTR to be significantly greater (or less) than zero. This paper presents Bayesian methodology to do so. We use a parallelizable and easy to use Monte Carlo (MC) approach to achieve our objective. Estimation of various risk measures, such as Value at Risk and Expected Shortfall, becomes a by-product of this Monte-Carlo approach.

Penalizing variances for higher dependency on factors

Constructing equity portfolios to control for stock market volatility and unforeseen portfolio losses is critical for meeting investment objectives. Mean–variance analysis (Markowitz 1952, 1959) wa...

Dynamic Portfolio Selection on Croatian Financial Markets: MGARCH Approach

Abstract Background: Investors on financial markets are interested in finding trading strategies which could enable them to beat the market. They always look for best possibilities to achieve above-average returns and manage risks successfully. MGARCH methodology (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) makes it possible to model changing risks and return dynamics on financial markets on a daily basis. The results could be used in order to enhance portfolio formation and restructuring over time. Objectives: This study utilizes MGARCH methodology on Croatian financial markets in order to enhance portfolio selection on a daily basis. Methods/Approach: MGARCH methodology is applied to the stock market index CROBEX, the bond market index CROBIS and the kuna/euro exchange rate in order to model the co-movements of returns and risks on a daily basis. The estimation results are then used to form successful portfolios. Results: Results indicate that using MGARCH methodology (the CCC and the DCC model) as guidance when forming and rebalancing a portfolio contributes to less portfolio volatility and greater cumulated returns compared to strategies which do not take this methodology into account. Conclusions: It is advisable to use MGARCH methodology when forming and rebalancing portfolios in terms of portfolio selection.

Does Interest Rate Exposure Explain the Low-Volatility Anomaly?

We show that part of the outperformance of low volatility stocks can be explained by a premium for interest rate exposure. Low volatility portfolios have negative exposure to interest rates, whereas the more volatile stocks have positive exposure. Incorporating an interest rate premium explains part of the anomaly. Dependent on the chosen methodology, interest rate exposure explains up to 80% of the unexplained excess return.

Practical Applications of Lifecycle Goal Achievement or Portfolio Volatility Reduction?

Advances in artificial intelligence mean robo-advisors are more sophisticated than ever. But can these automated systems beat human investment advice? Michael Dempster Managing Director of Cambridge Systems Associates, and his team launched one of the earliest robo-advisors: The iALM (Individual Asset Liability Management) system. “Since the early 1990s, we have done a lot of work with institutional investors to apply stochastic—or scenario-based—optimization techniques, and the idea was to use our models to bring this to the individual investor,” says Dempster. Lifecycle Goal Achievement or Portfolio Volatility Reduction?, compares the results of the iALM’s dynamic strategy with the financial advisory industry’s current best practices and finds that the automated system can provide better outcomes than some of its human counterparts.

Comparison analysis of copula-based and Markowitz portfolio methods

In this paper, the objects of study are securities (stocks) and portfolio. The main problem of the study is portfolio optimization. One of the first portfolio methods was presented by Henry Markowitz with his Modern Portfolio Theory (MPT), which is considered as a classic and the most popular one in modern investing. MPT provides the following assumptions: variance is used as a measure of risk, portfolio stock returns distribution is considered as a normal one. However, these assumptions do not represent real processes in the modern economy. First, in terms of modern volatile economy portfolio stock returns distribution curve has heavy tails, which is not typical for normal distribution. Secondly, in case of variance as a measure of risk probability of extreme events, such as a simultaneous increase or decrease of stock prices, are not taken into account. So Markowitz method no longer meets the requirements of the modern financial market and there is a need to study alternative and more valid portfolio methods. In this paper, copula-based approach is considered in contrast to the classical one. In the method assumption about the normality of stock returns is rejected and Value - at - Risk ( VaR ) is considered as a valid risk measure. VaR assessment is based on an information about random distribution. Since the normality assumption was rejected, to assess portfolio stock returns distribution need to be defined. To do these copula-functions was used. Stochastic optimization problem using VaR was solved with a modified Nelder-Mead method. As a result of the dynamic optimization return of copula-based portfolio for 2015 was 12,1 % of the initial investment sum, while the portfolio, constructed with the classical method, showed losses of 4,1 %. Since in copula-based approach incorrect normality assumption is rejected and a valid risk measure is chosen , copula-based portfolio is much more effective than the Markowitz one.

The Volatility Effect: Evidence from India

We offer empirical evidence that stocks with low volatility earn higher risk-adjusted returns compared to high volatility stocks in the Indian stock market. The annualised excess returns for the low and high volatility decile portfolios amount to 11.40% and 1.30%, respectively, over the period January 2001 to June 2015. The difference of returns is statistically and economically significant for both low and high-risk stocks. Using risk measures of standard deviation and beta, the volatility effect remains after controlling for size, value and momentum. We uncover that the volatility effect is not statistically significant after controlling for beta effect. Our evidence for volatility effect is not dominated by small and illiquid stocks. Our results show that the low volatility portfolio outperforms benchmark portfolio not only in down market but also in up market conditions.

Low volatility sector-based portfolios: a South African case

Portfolios and indices that have been specifically constructed to have low risk attributes have received increasing interest in the recent international literature. It has been found that portfolios constructed by targeting low risk assets have predominantly outperformed portfolios constructed to have higher risks. This anomaly has led to renewed interest in constructing low volatility portfolios by practitioners. This study analyses a variety of low volatility portfolio construction methodologies using sectors as building blocks in the South African environment. The empirical results from back-testing these portfolios show significant promise in the South African setting when compared with a market capitalization-weighted benchmark. In the empirical analysis in the South African environment two techniques stand out as being superior low volatility construction techniques amongst the seven techniques assessed. Furthermore, the low volatility portfolios are blended with typical general equity portfolios (using the Shareholder-Weighted Index (SWIX) as a proxy). It was found that these blended portfolios have useful features which lead to enhanced performance and therefore can serve as effective portfolio strategies.

The economic benefits of market timing the style allocation of characteristic-based portfolios

Many exchange traded funds track simple characteristic-based equity portfolios such as the market capitalization, the fundamental value or the inverse volatility portfolio. This paper provides theoretical and empirical evidence for the economic benefits in exploiting the timing-gains that result from the time-varying relative performance of these characteristic-based portfolios. Under a factor model for expected returns, we show that this dynamic portfolio allocation can be efficient across the low-dimensional set of characteristic-based portfolios. We assess the out-of-sample performance on the S&P 100 universe over the period 1990–2013 and show gains in stability and significant positive risk-adjusted returns for the dynamic style portfolio. We conduct several robustness tests and extensions confirming the benefits of dynamic style allocation across characteristic-based portfolios.

Returns, volatility and investor sentiment: Evidence from European stock markets

In this study, we show that patterns in returns behave as if investors, influenced by their level of optimism, selected stocks according to their volatility. Our goal is to confirm the contribution of behavioral finance while showing that investor sentiment can be profitably used by practitioners. We incorporate volatility in the relationship between investor sentiment and future returns, this is the main originality of our approach. Our methodology consists in comparing returns, volatility and higher-order moments of portfolios managed with investor sentiment against those obtained either with passive (buy and hold) portfolio management or with a minimum variance portfolio. Portfolios managed with investor sentiment have better returns and involve less risk under certain conditions.

Combining value averaging and Bollinger Band for an ETF trading strategy

ABSTRACTThe decision-making of investors is highly influenced by their feelings. According to behavioural finance, investor greed and fear would form irrational behaviour and affect their portfolio allocation. Although well-known mechanical investment strategy of dollar cost averaging (DCA) and value averaging (VA) could eliminate the problems of when to purchase, there are still some disadvantages to consider. For example, using a DCA strategy may be able to decrease volatility in portfolio so as to not effect investment decision, but it gives no rule for selling and may increase the opportunity cost of time if investors start deducted at peak prices. On the other hand, VA gives more aggressive sell signals to control the value of the portfolio to the level desired, but the investor may not have enough money purchase of a large number of shares in sharp decline period. Therefore, we use VA as main strategy and Bollinger Band as assist indicator for check for volatility for entry or exit. Through analysis and simulation, the new strategy we design does improve the performance during both bull and bear market periods.

Returns on Investments and Volatility Rate in the Nigerian Banking Industry

Most investment decisions focus on a forecast of future events that is either explicit or implicit. Generally asset pricing models postulate a positive relationship between a stock portfolio’s expected returns and risk, which is often modeled by the variance of the asset price. The essence of this paper is to use GARCH in mean and EGARCH to examine the relationship between mean returns on the Nigeria commercial banks portfolio investments and its conditional variance or standard deviation. After estimating a variety of models from Central Bank of Nigeria Statistical Bulletin 2010 data, we found out that using the GARCH in mean a positive and significant relationship exist between commercial bank portfolio return and volatility, while the EGARCH model gives a negative relationship. We suggest that market operators should try as much as possible to prevent avoidable bad news.

An Analysis of Guaranteed Lifetime Withdrawal Benefits Linked to Target Volatility Portfolio

GLWB is currently the most popular type of a variable annuity. GWLB promises the policyholder to annually withdraw a fixed amount from his investment account for the rest of his life, even if the value of the investment account drops to zero. However, from the insurer's point of view, GLWB contains several types of risk such as mortality risk, interest rate risk and financial risk. Target volatility strategy is used to create a dynamic re-balancing portfolio such that the overall volatility of portfolio maintains a stable level at all time. The strategy shifts the allocation of equity asset to non-risky asset in order to protect the portfolio from equity market crashes. Many insurance companies offer or consider GLWB linked to target volatility portfolios. This paper provides an extensive analysis of the GLWB linked to target volatility portfolio. In particular, we investigate the impact of stochastic volatility and mortality intensity on the pricing of the GLWB linked to target volatility portfolio. Moreover, we examine the lifetime probability of ruin for the target volatility portfolios.

Pricing and Hedging of the European Option Linked to Target Volatility Portfolio

Advanced risk management strategies have become popular since they protect the investor's investment from market crashes. The latest risk management strategy is called the target volatility strategy. The target volatility strategy is used in order to maintain a stable realized volatility of a portfolio. The strategy re-balances the allocation of risky asset to non-risky asset in order to protect the portfolio from equity market crashes. This paper provides an extensive analysis of the target volatility portfolios and financial derivatives linked to target volatility portfolios. We demonstrate the performance of target volatility portfolios under different financial models. We also examine the effects of re-balancing frequencies on target volatility portfolios. We focus on the pricing and hedging of the European option linked to target volatility portfolios. In particular, we investigate the impact of stochastic equity asset volatility on the pricing and hedging of the European option linked to target volatility portfolios. We also consider different dynamic hedging strategies and compare their performance.

Performance versus turnover: a story by 4000 alphas

We analyze empirical data for 4000 real-life trading portfolios (US equities) with holding periods of about 0.7-19 trading days. We find a simple scaling C ∼ 1 ⁄ Τ , where C is cents-per-share and Τ is the portfolio turnover. Thus, the portfolio return R has no statistically significant dependence on the turnover T. We also find a scaling R ∼ VX, where V is the portfolio volatility and the power X is around 0.8-0.85 for holding periods up to ten days or so. To our knowledge, this is the only publicly available empirical study on such a large number of real-life trading portfolios/alphas.

The Approximation Properties of the Numerical Scheme of the Black-Schole Equation with Volatile Portfolio Risk Measure

We study the numerical approximation in space of the solution of Black-Schole’s equation with volatile portfolio risk measure. Making use of the theorem of solvability in Sobolev spaces, the solution is approximated in space, with finite –difference methods.

Characteristic-Based Equity Portfolios: Economic Value and Dynamic Style-Allocation

Many exchange traded funds track simple characteristic-based equity portfolios such as the market capitalization, the fundamental value or the inverse volatility portfolio. This paper provides theoretical and empirical evidence for the economic benefits in exploiting the timing-gains that result from the time-varying relative performance of these characteristic-based portfolios. Under a factor model for expected returns, we show that this dynamic portfolio allocation can be efficient across the low-dimensional set of characteristic-based portfolios. We assess the out-of-sample performance on the S&P 100 universe over the period 1990-2013 and show gains in stability and significant positive risk-adjusted returns for the dynamic style portfolio. We conduct several robustness tests and extensions confirming the benefits of dynamic style allocation across characteristic-based portfolios.

Practical Applications of The Case for Liquid Alternatives in Defined-Contribution Plans

“We know that retirees need to save more, and plan sponsors have access to investment tools that can help them meet their retirement goals,” says Scott Kilgallen of Neuberger Berman, the co-author of a recent article that explains why DC plan sponsors should consider adding liquid alts to their fund lineups and discuss common approaches and structures. Liquid alts provide a useful way to include hedge fund–like strategies without some of the drawbacks of traditional hedge fund vehicles. “The question is always, how can you achieve better returns over a range of market environments? Liquid alts may complement other asset class offerings in a portfolio by seeking to lower overall portfolio volatility,” says co-author David Kupperman (also of Neuberger Berman).

Lifecycle Goal Achievement or Portfolio Volatility Reduction?

This article is concerned with the use of currently available technology to offer individuals, financial advisors, and pension fund financial planners detailed prospective financial plans tailored to an individual’s financial goals and obligations. By taking account of all an individual’s prospective cash flows, including servicing current liabilities, and simultaneously optimizing prospective spending, saving, asset allocation, tax, and insurance, etc. using dynamic stochastic optimization, the authors compare the results of their goal-based fully dynamic strategy with the financial advisory industry’s representative current best practices. 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 marketcalibrated Monte Carlo stochastic simulation for all the alternative portfolio strategies, the authors find that flexibility is of key importance for both individual portfolio and spending decisions. The authors measure superiority 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). They find that the adaptive dynamic goal-based portfolio strategy’s performance is far superior to all the industry’s Markowitz-based approaches. These empirical results should put paid to the commonly held view that the extra complexity of holistic dynamic stochastic models is not worth the marginal extra value obtained from their use.

Do People Understand the Benefit of Diversification?

Diversification — investing in imperfectly correlated assets — reduces expected volatility without sacrificing expected returns. While the expected return from a diversified portfolio is just the weighted average mean of its constituent parts, the expected variance is less than the weighted average variance of its constituent parts. We demonstrate that very few people have correct statistical intuitions about the effects of diversification. Many people, especially those low in financial literacy, believe diversification increases the volatility of a portfolio because they conflate the predictability of individual assets with the aggregate predictability of the portfolio. Additionally, most people, but especially those high in financial literacy, believe diversification increases the expected return of a portfolio. These errors lead people to construct investment portfolios that mismatch investors’ risk preferences. The fact that people do not understand the benefit of diversification may partially explain why investors are poorly diversified, despite valuing risk reduction.