Covariance Of Returns Research Articles

ANALISIS PORTOFOLIO SAHAM LQ45 MENGGUNAKAN FUNGSI UTILITAS KUADRATIK

Utility function can use to give risk preference for investors who want to get the benefits gained meets investment targets. Quadratic utility functions on optimal portfolio is strongly influenced by the expected return and standard deviation. The establishment of optimal portfolios using a quadratic utility function optimization problems. Under the settlement portfolio optimization, the necessary data is expected return, variance, and variance covariance matrix. The optimal portfolio is affected by some factors Risky less Rate, risk aversion index, and Borrow Rate. The results of settlement portfolio optimization is obtaining the utility value while the relatively large changes influencing by risk averse index.

Who Participates in Corporate Income Tax Consolidation? Evidence from Japan

When a group of affiliated corporations have the option to file a single tax return based on a combined income, what types of groups would take up the option? This study empirically analyses decisions to participate in a single-jurisdiction consolidated tax filing. The data consists of 2,782 Japanese corporate groups headed by publicly-traded corporations observed over 2002-2007. Results indicate higher likelihood of participation among groups characterised by low correlation in returns among group members, high variance in returns, large number of subsidiaries, and losses accumulated in parents. The significant influence of variance and covariance of returns suggests that a consolidation scheme improves the efficiency of corporate income tax through reducing profit shifting.

Eigenvector dynamics: General theory and some applications

We propose a general framework to study the stability of the subspace spanned by P consecutive eigenvectors of a generic symmetric matrix H0 when a small perturbation is added. This problem is relevant in various contexts, including quantum dissipation (H0 is then the Hamiltonian) and financial risk control (in which case H0 is the assets' return covariance matrix). We argue that the problem can be formulated in terms of the singular values of an overlap matrix, which allows one to define an overlap distance. We specialize our results for the case of a Gaussian orthogonal H0, for which the full spectrum of singular values can be explicitly computed. We also consider the case when H0 is a covariance matrix and illustrate the usefulness of our results using financial data. The special case where the top eigenvalue is much larger than all the other ones can be investigated in full detail. In particular, the dynamics of the angle made by the top eigenvector and its true direction defines an interesting class of random processes.

Pricing Deviation, Misvaluation Comovement, and Macroeconomic Conditions

We measure individual stocks' misvaluation based on their firm-specific deviations from predicted intrinsic values. The misvaluation measure exhibits association with stocks' valuation uncertainty and arbitrage difficulty, and has significant power to forecast stock returns incremental to size, book-to-market ratio, momentum, and various return anomalies. Based on the misvaluation measure, we form a misvaluation factor and find that stocks' return covariances with this factor strongly predict the cross-section of returns even after the control of stocks' sensitivities to other return factors. We further show that the misvaluation factor and market-wide misvaluation waves predict future macroeconomic conditions, which provides further insights into the pricing of systematic misvaluation in the market.

A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns

A multivariate positive definite estimator of the integrated covariance matrix of noisy and asynchronously observed asset returns is proposed. We adopt a Bayesian Dynamic Linear Model where microstructure noise is interpreted as measurement error, and asynchronous trading as missing observations in an otherwise synchronous series. Missing observations are treated as any other parameter, as typical in a Bayesian framework. An augmented Gibbs algorithm is used since all full conditionals are available and its convergence and robustness are discussed. A realistic simulation study compares our estimator with existing alternatives, under different liquidity and microstructure noise conditions. The results suggest that our estimator is superior in terms of RMSE particularly under severe conditions, such as portfolios of assets with heterogeneous liquidity and high level of microstructure noise. The application to the empirical dataset of ten tick-by-tick stock price series confirms the simulation results.

Covariance Estimation with Price Range Information

The security price range has been demonstrated to contain rich information associated with return volatility. In this paper, the information content of the price range is further investigated with regard to the return covariance estimation. A copula-based dynamic conditional correlation range-augmented GARCH (CDCC-RGARCH) model is proposed to investigate the accuracy of covariance estimation with respect to the volatility estimator and error distribution. The empirical results show that price range is crucial to multivariate volatility modeling with regard to estimation accuracy.

The FTSE StableRisk Indices

Implicit in most asset-allocation policies is the statistical assumption of “stationarity,” which means that the means, variances, and covariances of asset returns are assumed to be constant over time. This assumption is a reasonable approximation during normal market conditions but fails dramatically during periods of market turmoil and dislocation. In such periods, market volatility is highly dynamic, correlations can jump to 100% in a matter of days, and risk premia can become negative for months at a time. FTSE and AlphaSimplex Group have developed a family of rule-driven (passive), transparent, and high-capacity indices whose volatilities are rescaled as often as daily with the goal of maintaining more stable risk levels. By stabilizing the risk of each asset class over time, the FTSE StableRisk Indices have the potential to capture the long-term risk premia of asset classes and simple strategies with less severe maximum drawdowns than those of traditional indices, which have no risk controls. <b>TOPICS:</b>Passive strategies, futures and forward contracts, volatility measures, statistical methods

The Effect of Smoothing on Optimal Portfolio Choice: Some Analytical and Computational Results

This paper analyzes the economic cost of Mean-Variance portfolios that involve asset returns which are smoothed. In this situation, variance and covariance of returns will be understated, resulting in sub-optimal allocation. Certainty equivalent loss (CEL), associated with this ‘smoothed’ portfolio, is analytically derived as a function of the smoothing parameter alpha. The results show that CEL grows hyperbolically with alpha. The paper shows further that the practice of unsmoothing asset returns before constructing portfolios helps reducing CEL, but is unlikely to solve the problem completely. This is due to the risk of mis-unsmoothing that also escalates with the parameter alpha.

Housing, Wealth Composition and Expected Stock Return

This paper considers a consumption-based asset pricing model where housing is explicitly modeled both as an asset and consumption good. As consumption good, housing introduces housing expenditure share as a novel risk factor. As an asset, it is the major component of wealth with financial asset. The fluctuation of aggregate housing-financial wealth ratio, as a consequence of irrational housing market, impacts the budget constraints of households. It increases household's exposure to risk and shifts the conditional distribution of consumption growth. Using aggregate data for the United States, we find that the fluctuation of housing-financial wealth ratio is a strong predictor for expected stock return. Conditional on this factor, the covariances of returns with aggregate risk factors explain high ratio of the cross-sectional variation in annual size and book-to-market portfolio returns. The micro mechanism of this asset pricing model is also supported by the micro data from subprime crisis.

Time-varying stock returns and labor income risks in the US and UK

I propose and estimate conditional asset pricing models where the risk premiums of the markets are related to the conditional covariance of the markets with labor income growth within and across countries and the volatility of the markets are related to the shocks and interactions of stock returns and labor income growth. I document that the risk premiums for the US and UK stock markets are more related to the conditional covariance of returns with the labor income growth within countries than across countries. I also find significant interactions of volatilities between stock returns and labor income within countries but not across countries. The results are consistent with the hypothesis that prices of domestic stocks are determined to a greater extent by stochastic discount factors of domestic investors than foreign investors and vice versa.

Pre-Earnings Announcement Drift

We present evidence of a predictable drift in stock prices before the earnings announcements of firms that announce their earnings later than other firms in their industry. We form portfolios based on the returns of later announcers that are implied by the abnormal returns of earlier announcers and the historical pair-wise covariance of the abnormal earnings announcement date returns of earlier and later announcers. A long-short trading strategy based on these implied returns generates monthly returns of more than 100 basis points. The drift is neither due to the well-known momentum effect nor a manifestation of post-earnings announcement drift; it is evident both between the earlier announcers’ earnings announcement dates and the later announcers’ earnings announcement dates and at the later announcers’ earnings announcement dates. The continued under-reaction after later announcers’ earnings announcements is shown to be an under-reaction to the later announcers’ own earnings announcements (i.e., post-earnings announcement drift) rather than a continued under-reaction to the earnings news of earlier announcers (i.e., pre-earnings announcement drift). We show that transaction costs explain the predictability of later announcers’ returns.

Jump Robust Two Time Scale Covariance Estimation and Realized Volatility Budgets

We estimate the daily integrated variance and covariance of stock returns using high-frequency data in the presence of jumps, market microstructure noise and non-synchronous trading. For this we propose jump robust two time scale (co)variance estimators and verify their reduced bias and mean square error in simulation studies. We use these estimators to construct the ex-post portfolio realized volatility (RV) budget, determining each portfolio component's contribution to the RV of the portfolio return. These RV budgets provide insight into the risk concentration of a portfolio. Furthermore, the RV budgets can be directly used in a portfolio strategy, called the equal-risk-contribution allocation strategy. This yields both a higher average return and lower standard deviation out-of-sample than the equal-weight portfolio for the stocks in the Dow Jones Industrial Average over the period October 2007-May 2009.

Construction of Risk-Averse Enhanced Index Funds

We propose a partial replication strategy to construct risk-averse enhanced index funds. Our model takes into account the parameter estimation risk by defining the asset returns and the return covariance terms as random variables. The variance of the index fund return is required to be below a low-risk threshold with a large probability, thereby limiting the market risk exposure of the investors. The resulting stochastic integer problem is reformulated through the derivation of a deterministic equivalent for the risk constraint and the use of a block decomposition technique. We develop an exact outer approximation method based on the relaxation of some binary restrictions and the reformulation of the cardinality constraint. The method provides a hierarchical organization of the computations with expanding sets of integer-restricted variables and outperforms the Bonmin and the CPLEX solvers. The method can solve large instances (up to 1,000 securities), converges fast, scales well, and is general enough to be applicable to problems with buy-in-threshold constraints.

A conditional asset-pricing model with the optimal orthogonal portfolio

This paper employs a conditional asset-pricing model based on the optimal orthogonal portfolio approach to construct a factor portfolio that embodies all the latent factors important for pricing a given set of test assets. The advantage of this portfolio to the anomaly related mimicking portfolios is its ability to separate out the components of average return that are not related to the return covariation. The performance of this portfolio is evaluated against several conventional factors, using both cross-sectional and time-series regression approaches, as well as the Hansen and Jagannathan (1997) distance measure. Its strong out-of-sample results indicate that our suggested methodology may have important applications in risk management, portfolio selection and performance evaluation.

Portfolio allocation and asset demand with mean-variance preferences

We analyze the comparative static effects of changes in the means, the standard deviations and the covariance of asset returns in a standard portfolio selection problem when investors have mean variance preferences. Simple and intuitive characterizations in terms of the elasticity of risk aversion are provided.

Factoring Information into Returns

AbstractWe examine the potential profits of trading on a measure of private information (PIN) in a stock. A zero-investment portfolio that is size-neutral but long in high PIN stocks and short in low PIN stocks earns a significant abnormal return. The Fama-French, momentum, and liquidity factors do not explain this return. However, significant covariation in returns exists among high PIN stocks and among low PIN stocks, suggesting that PIN might proxy for an underlying factor. We create a PIN factor as the monthly return on the zero-investment portfolio above and show that it is successful in explaining returns to independent PIN-size portfolios. We also show that it is robust to inclusion of the Pástor-Stambaugh liquidity factor and the Amihud illiquidity factor. We argue that information remains an important determinant of asset returns even in the presence of these additional factors.

Construction of Risk-Averse Enhanced Index Funds

We propose a partial replication strategy to construct risk-averse enhanced index funds. Our model takes into account the parameter estimation risk by defining the asset returns and the return covariance terms as random variables. The variance of the index fund return is forced to be below a low-risk threshold with a large probability, thereby limiting the market risk exposure of the investors and the moral hazard associated with the wage structure of fund managers. The resulting stochastic integer problem is reformulated through the derivation of a deterministic equivalent for the risk constraint and the use of a block decomposition technique. We develop an exact outer approximation method based on the relaxation of some binary restrictions and the reformulation of the cardinality constraint. The method provides a hierarchical organization of the computations with expanding sets of integer-restricted variables and outperforms the Bonmin and the Cplex 12.1 solvers. The method can solve very large (up to 1000 securities) instances, converges fast, scales well, and is general enough to be applicable to problems with buy-in threshold constraints. Cross-validation tests show that the constructed funds track closely and are consistently less risky than the benchmark on the out-of-sample period.

Range-Based Covariance Estimation Using High-Frequency Data: The Realized Co-Range

textabstractWe introduce the realized co-range, utilizing intraday high-low price ranges to estimate asset return covariances. Using simulations we find that for plausible levels of bid-ask bounce and infrequent and non-synchronous trading the realized co-range improves upon the realized covariance, which uses cross-products of intraday returns. One advantage of the co-range is that in an ideal world it is five times more efficient than the realized covariance when sampling at the same frequency. The second advantage is that the upward bias due to bid-ask bounce and the downward bias due to infrequent and non-synchronous trading partially offset each other. In a volatility timing strategy for S\\&P500, bond and gold futures we find that the co-range estimates are less noisy as exemplified by lower transaction costs and also higher Sharpe ratios when using more weight on recent data for predicting covariances.

An extended Stein's lemma for asset pricing

Stein's lemma is extended to the case where asset returns have skewed and leptokurtic distributions. The risk premium is still the negative of the covariance of the excess return with the log stochastic discount factor. The risk-neutral distribution has a simple form but is a nontrivial transformation of the physical distribution.

High-dimensional covariance forecasting for short intra-day horizons

Asset return covariances at intra-day horizons are known to tend towards zero due to market microstructure effects. Thus, traders who simply scale their daily covariance forecast to match their trading horizon are likely to over-estimate the actual experienced asset dependence. In this paper, some of the key challenges are discussed that are encountered when forecasting high-dimensional covariance matrices for short intra-day horizons. Based on a novel evaluation methodology, and extensive empirical analysis, specific recommendations are made regarding model design and data sampling.