Improving Portfolio Selection Using Option-Implied Volatility and Skewness

2013 ◽  
Vol 48 (6) ◽  
pp. 1813-1845 ◽  
Author(s):  
Victor DeMiguel ◽  
Yuliya Plyakha ◽  
Raman Uppal ◽  
Grigory Vilkov
Keyword(s):  
Risk Premium ◽  
Implied Volatility ◽  
Substantial Improvement ◽  
Sharpe Ratio ◽  
Expected Returns ◽  
Portfolio Performance ◽  
Short Sales ◽  
Volatility Risk ◽  
Out Of Sample ◽  
Mean Variance

AbstractOur 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 to improve 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 short sales and accounting for transaction costs.

Factor Timing

2020 ◽  
Vol 33 (5) ◽  
pp. 1980-2018 ◽  
Author(s):  
Valentin Haddad ◽  
Serhiy Kozak ◽  
Shrihari Santosh
Keyword(s):  
Stock Returns ◽  
Substantial Improvement ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Portfolio Performance ◽  
Oxford University ◽  
Static Factor ◽  
Cyclical Behavior ◽  
New Challenges ◽  
Published Paper

Abstract The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, is more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Optimal Option Portfolio Strategies: Deepening the Puzzle of Index Option Mispricing

2017 ◽  
Vol 52 (1) ◽  
pp. 277-303 ◽  
Author(s):  
José Afonso Faias ◽  
Pedro Santa-Clara
Keyword(s):  
Asset Allocation ◽  
Risk Premia ◽  
Volatility Risk ◽  
Short Sample ◽  
Option Returns ◽  
Portfolio Strategies ◽  
Portfolio Strategy ◽  
Out Of Sample ◽  
Index Option ◽  
Mean Variance

Traditional methods of asset allocation (such as mean–variance optimization) are not adequate for option portfolios because the distribution of returns is non-normal and the short sample of option returns available makes it difficult to estimate their distribution. We propose a method to optimize a portfolio of European options, held to maturity, with a myopic objective function that overcomes these limitations. In an out-of-sample exercise incorporating realistic transaction costs, the portfolio strategy delivers a Sharpe ratio of 0.82 with positive skewness. This performance is mostly obtained by exploiting mispricing between options and not by loading on jump or volatility risk premia.

scholarly journals Beyond the Carry Trade: Optimal Currency Portfolios

2015 ◽  
Vol 50 (5) ◽  
pp. 1037-1056 ◽  
Author(s):  
Pedro Barroso ◽  
Pedro Santa-Clara
Keyword(s):  
Exchange Rate ◽  
Real Exchange Rate ◽  
Current Account ◽  
Sharpe Ratio ◽  
Optimal Portfolio ◽  
Crash Risk ◽  
Portfolio Performance ◽  
Carry Trade ◽  
The Real ◽  
Out Of Sample

AbstractWe test the relevance of technical and fundamental variables in forming currency portfolios. Carry, momentum, and value reversal all contribute to portfolio performance, whereas the real exchange rate and the current account do not. The resulting optimal portfolio produces out-of-sample returns that are not explained by risk and are valuable to diversified investors holding stocks and bonds. Exposure to currencies increases the Sharpe ratio of diversified portfolios by 0.5 on average, while reducing crash risk. We argue that besides risk, currency returns reflect the scarcity of speculative capital.

scholarly journals The Economic Value of Predicting Stock Index Returns and Volatility

2004 ◽  
Vol 39 (2) ◽  
pp. 407-429 ◽  
Author(s):  
Wessel Marquering ◽  
Marno Verbeek
Keyword(s):  
Linear Models ◽  
Market Timing ◽  
Economic Value ◽  
Trading Strategies ◽  
Stock Index ◽  
Short Sales ◽  
Monthly Data ◽  
Out Of Sample ◽  
Mean Variance ◽  
Stock Index Returns

AbstractIn this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the economic value of a number of alternative trading strategies over the period 1970–2001. It appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. Most of the profitability of the dynamic strategies, however, is located in the first half of our sample period.

scholarly journals Estimating Market Expectations for Portfolio Selection Using Penalized Statistical Models

2020 ◽  
Vol 38 (2) ◽  
pp. 133-146
Author(s):  
Carlos Felipe Valencia-Arboleda ◽  
Diego Hernan Segura-Acosta
Keyword(s):  
Portfolio Selection ◽  
Historical Data ◽  
Selection Problem ◽  
Expected Returns ◽  
Portfolio Performance ◽  
Market Prices ◽  
Portfolio Selection Problem ◽  
Implicit Information ◽  
Decision Variables ◽  
Out Of Sample

The portfolio selection problem can be viewed as an optimization problem that maximizes the risk–return relationship. It consists of a number of elements, such as an objective function, decision variables and input parameters, which are used to predict expected returns and the covariance between the said returns. However, the real values of these parameters cannot be directly observed; thus, estimations based on historical data are required. Historical data, however, can often result in modelling errors when the parameters are replaced by their estimations. We propose to address this by using some regularization mechanisms in the optimization.  In addition, we explore the use of implicit information to improve the portfolio performance, such as options market prices, which are a rich source of investor expectations. Accordingly, we propose a new estimator for risk and return that combines historical and implicit information in the portfolio selection problem. We implement the new estimators for the mean-VAR and mean-VaR2 problems using an elastic-net model that reduces the risk of all estimations performed. The results suggest that the model has a good out-of-sample performance that is superior to models with pure historical estimations.

scholarly journals The role of an option-implied distribution in improving an asset allocation model

2020 ◽  
Vol 16 (1) ◽  
pp. 64-69
Author(s):  
Hafizah Bahaludin ◽  
Mimi Hafizah Abdullah
Keyword(s):  
Asset Allocation ◽  
Risk Premium ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Portfolio Performance ◽  
Allocation Model ◽  
Risky Assets ◽  
Risk Neutral ◽  
Naïve Diversification ◽  
Risk Neutral Density

The objective of this paper is to extend the information embedded in option-implied distribution to asset allocation model. This paper examines whether a parameter estimated from an option-implied distribution can improve a minimum-variance portfolio which consists of many risky assets. The option-implied distribution under a risk-neutral assumption is called risk-neutral density (RND) whereas a risk-world density (RWD) is calculated by incorporating a risk-premium. The computation of option-implied distributions is based on the Dow Jones Industrial Average (DJIA) index options and its constituents. The data covers the period from January 2009 until December 2015. Portfolio performance is evaluated based on portfolio volatility and Sharpe ratio. The performance of a portfolio based on an option-implied distribution is compared to a naive diversification portfolio. The empirical evidence shows that for a portfolio based on an option-implied distribution, the volatility of the portfolio is reduced and the Sharpe ratio is increased.

The informational quality of implied volatility and the volatility risk premium

2010 ◽  
Vol 17 (5) ◽  
pp. 445-450 ◽  
Author(s):  
Stephen P. Ferris ◽  
Woojin Kim ◽  
Kwangwoo Park
Keyword(s):  
Risk Premium ◽  
Implied Volatility ◽  
Volatility Risk ◽  
Volatility Risk Premium

scholarly journals Regularized Maximum Diversification Investment Strategy

Econometrics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
N’Golo Koné
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Investment Strategy ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Tuning Parameter ◽  
Portfolio Performance ◽  
Asset Return ◽  
Out Of Sample ◽  
Global Minimum Variance Portfolio

The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return covariance matrix. In practice, these two quantities need to be replaced by their sample statistics. The estimation error associated with the use of these sample statistics may be amplified due to (near) singularity of the covariance matrix, in financial markets with many assets. This, in turn, may lead to the selection of portfolios that are far from the optimal regarding standard portfolio performance measures of the financial market. To address this problem, we investigate three regularization techniques, including the ridge, the spectral cut-off, and the Landweber–Fridman approaches in order to stabilize the inverse of the covariance matrix. These regularization schemes involve a tuning parameter that needs to be chosen. In light of this fact, we propose a data-driven method for selecting the tuning parameter. We show that the selected portfolio by regularization is asymptotically efficient with respect to the diversification ratio. In empirical and Monte Carlo experiments, the resulting regularized rules are compared to several strategies, such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements.

scholarly journals Optimal omega-ratio portfolio performance constrained by tracking error

2020 ◽  
Vol 17 (3) ◽  
pp. 263-280
Author(s):  
Wade Gunning ◽  
Gary van Vuuren
Keyword(s):  
Recent Work ◽  
Tracking Error ◽  
Sharpe Ratio ◽  
Main Axis ◽  
Portfolio Performance ◽  
Optimal Portfolios ◽  
Investment Style ◽  
The Mean ◽  
Omega Ratio ◽  
Mean Variance

The mean-variance framework coupled with the Sharpe ratio identifies optimal portfolios under the passive investment style. Optimal portfolio identification under active investment approaches, where performance is measured relative to a benchmark, is less well-known. Active portfolios subject to tracking error (TE) constraints lie on distorted elliptical frontiers in return/risk space. Identifying optimal active portfolios, however defined, have only recently begun to be explored. The Ω – ratio considers both down and upside portfolio potential. Recent work has established a technique to determine optimal Ω – ratio portfolios under the passive investment approach. The authors apply the identification of optimal Ω – ratio portfolios to the active arena (i.e., to portfolios constrained by a TE) and find that while passive managers should always invest in maximum Ω – ratio portfolios, active managers should first establish market conditions (which determine the sign of the main axis slope of the constant TE frontier). Maximum Sharpe ratio portfolios should be engaged when this slope is > 0 and maximum Ω – ratios when < 0.

Fundamental Analysis and Mean-Variance Optimal Portfolios

2021 ◽  
Author(s):  
Matthew R. Lyle ◽  
Teri Lombardi Yohn
Keyword(s):  
Portfolio Optimization ◽  
Expected Returns ◽  
Optimization Approach ◽  
Fundamental Analysis ◽  
Out Of Sample ◽  
Sharpe Ratios ◽  
Mean Variance Portfolio ◽  
Portfolio Policy ◽  
Performance Gains ◽  
Mean Variance

We integrate fundamental analysis with mean-variance portfolio optimization to form fully optimized fundamental portfolios. We find that fully optimized fundamental portfolios produce large out-of-sample factor alphas with high Sharpe ratios. They substantially outperform equal-weighted and value-weighted portfolios of stocks in the extreme decile of expected returns, an approach commonly used in fundamental analysis research. They also outperform the factor-based and parametric portfolio policy approaches used in the prior portfolio optimization literature. The relative performance gains from mean-variance optimized fundamental portfolios are persistent through time, robust to eliminating small capitalization firms from the investment set, and robust to incorporating estimated transactions costs. Our results suggest that future fundamental analysis research could implement this portfolio optimization approach to provide greater investment insights.

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