Moments Of Returns Research Articles

Spin Gapless Semiconductor–Nonmagnetic Semiconductor Transitions in Fe-Doped Ti2CoSi: First-Principle Calculations

Employing first-principle calculations, we investigated the influence of the impurity, Fe atom, on magnetism and electronic structures of Heusler compound Ti2CoSi, which is a spin gapless semiconductor (SGS). When the impurity, Fe atom, intervened, Ti2CoSi lost its SGS property. As TiA atoms (which locate at (0, 0, 0) site) are completely occupied by Fe, the compound converts to half-metallic ferromagnet (HMF) TiFeCoSi. During this SGS→HMF transition, the total magnetic moment linearly decreases as Fe concentration increases, following the Slate–Pauling rule well. When all Co atoms are substituted by Fe, the compound converts to nonmagnetic semiconductor Fe2TiSi. During this HMF→nonmagnetic semiconductor transition, when Fe concentration y ranges from y = 0.125 to y = 0.625, the magnetic moment of Fe atom is positive and linearly decreases, while those of impurity Fe and TiB (which locate at (0.25, 0.25, 0.25) site) are negative and linearly increase. When the impurity Fe concentration reaches up to y = 1, the magnetic moments of Ti, Fe, and Si return to zero, and the compound is a nonmagnetic semiconductor.

DRAWDOWN MEASURES AND RETURN MOMENTS

This paper provides an investigation of the effects of an investment’s return moments on drawdown-based measures of risk, including Maximum Drawdown (MDD), Conditional Drawdown (CDD), and Conditional Expected Drawdown (CED). Additionally, a new end-of-period drawdown measure is introduced, which incorporates a psychological aspect of risk perception that previous drawdown measures had been unable to capture. While simulation results indicate many similarities in the first and second moments, skewness and kurtosis affect different drawdown measures in radically different ways. Thus, users should assess whether their choice of drawdown measure accurately reflects the kind of risk they want to measure.

Optimal demand in a mispriced asymmetric Carr–Geman–Madan–Yor (CGMY) economy

We employ a simple numerical scheme to compute optimal portfolios and utilities of informed and uninformed investors in a mispriced Carr–Geman–Madan–Yor (CGMY) Levy market under information asymmetry using instantaneous centralized moments of returns (ICMR). We also investigate the impact on investors’ demand for stocks and indices at different levels of asymmetric information, mispricing, investment horizon, jump intensity, and volatility. Our simulations not only confirm that uninformed expected demand falls as information asymmetry increases but also offer strong evidence that informed expected demand behaves in a similar manner. In particular, expected demand of informed investors falls whenever information asymmetry exceeds 50%. The investor that demands more of the risky asset maintains that position over the entire investment horizon at each level of mispricing and information asymmetry. The absolute difference in expected demand between the uninformed and informed investors increases with the investment horizon, but decreases with the level of information asymmetry.

Characterizing the Variance Risk Premium in Consumption-Based Models

The conditional covariance between the market return and its variance, which we refer to as the leverage effect, is positively related to the variance risk premium. It contains incremental information about the variance risk premium after controlling for other return moments and additional variables suggested by the literature as determinants of the variance risk premium. This empirical finding is supported by theory: the pricing of volatility risk is the economic channel behind the strong positive relation between the two variables. We use this relation to construct a time series of the variance risk premium dating back to 1926.

Empirical analysis of intertemporal relations between downside risks and expected returns—Evidence from Asian markets

This paper tests the risk-return relations for Asian stock markets by employing conditional volatility, local downside risk, regional downside risk, and world/U.S. downside risk. We find positive and significant intertemporal relations between excess stock returns and various risks. The evidence supports the risk-return tradeoff not only from local risk but also from external risk. The model is robust as it pertains to the risk of small variations as well as big shocks. The evidence supports positive risk-return relations across 10 Asian markets after controlling for the lagged dividend yield, higher moments of stock returns, and exchange rate variations.

Multi-moment risk, hedging strategies, & the business cycle

We study the asymmetric responses of hedge fund return moments—especially higher moments as measured by return co-skewness and co-kurtosis—to macroeconomic and financial shocks depending on the phase of the business cycle. Similarly to previous papers on hedge fund systematic market risk (beta), we find that hedge funds seem to monitor their return co-skewness and co-kurtosis. The response of their return moments to VIX shocks—our indicator of macroeconomic and financial uncertainty—is particularly important, hedge funds reducing their beta and co-kurtosis and increasing their co-skewness following a (positive) VIX shock. Overall, the representative hedge fund tends to behave as an insurance seller in economic expansion and as an insurance buyer in recession or crisis. Finally, VIX shocks contribute to increase systemic risk in the hedge fund industry.

The Skewness of the Stock Market at Long Horizons

Higher moments of long-horizon returns are important for asset pricing but are hard to measure accurately using standard techniques. We provide theory showing that short-horizon (e.g. daily) returns can be used to construct precise estimates of long-horizon (e.g. annual) moments without making strong assumptions about the data generating process. Skewness comprises two components: skewness of short-horizon returns and a leverage effect, i.e. covariance between variance and lagged returns. We provide similar results for kurtosis. An application to US stock-index returns shows that skew is large and negative and attenuates only slowly as one moves from monthly to multi-year horizons.

Educação do campo e políticas educacionais: avanços, contradições e retrocessos

The current article aims to make a reflection how a policy for Field Education, rises on Brazil, in last 20 years, as a founder strategy of the Field Education Movement. This Movement has emerged against the society model that reproduces social, political and economic asymmetries which marks the past and present Brazil history, and displays a critical position against the precarious and decontextualized school model implemented in Brazilian country. The dialetic approach guided the investigative process that had in the documentary analysis and in the literature review fundamental procedures for the construction of this text. The contradiction posed by the State as an instrument of capital regulation, has generated over the years a tension in the materialization of the policies and programs claimed by the Field Education Movement, especially at this moment of retreats and returns in social rights in our Country. However, we identify achievements in educational practices, in the articulation of the diversity of rural, water and forest subjects in the fight against oppression and by the affirmation of a territorialization of family and agroecological agriculture.  

Bias correction in the realized stochastic volatility model for daily volatility on the Tokyo Stock Exchange

The realized stochastic volatility model has been introduced to estimate more accurate volatility by using both daily returns and realized volatility. The main advantage of the model is that no special bias-correction factor for the realized volatility is required a priori. Instead, the model introduces a bias-correction parameter responsible for the bias hidden in realized volatility. We empirically investigate the bias-correction parameter for realized volatilities calculated at various sampling frequencies for six stocks on the Tokyo Stock Exchange, and then show that the dynamic behavior of the bias-correction parameter as a function of sampling frequency is qualitatively similar to that of the Hansen–Lunde bias-correction factor although their values are substantially different. Under the stochastic diffusion assumption of the return dynamics, we investigate the accuracy of estimated volatilities by examining the standardized returns. We find that while the moments of the standardized returns from low-frequency realized volatilities are consistent with the expectation from the Gaussian variables, the deviation from the expectation becomes considerably large at high frequencies. This indicates that the realized stochastic volatility model itself cannot completely remove bias at high frequencies.

Do Commodities Add Economic Value in Asset Allocation? New Evidence from Time-Varying Moments

We conduct a comprehensive out-of-sample assessment of the economic value adding of commodities in multiasset investment strategies that exploit the predictability of asset return moments. We find that predictability makes the inclusion of commodities profitable even when short selling and high leverage are not permitted. For instance, a mean-variance (non-mean-variance) investor with moderate risk aversion and leverage, rebalancing quarterly, would be willing to pay up to 108 (155) basis points per year after transaction cost for adding commodities to her stock, bond, and cash portfolio. Previous research had reached mixed or even opposite conclusions, especially in an out-of-sample context.

A General Theory of Option Pricing

We present a new formalism for option pricing that does not require an assumption on the stochastic process of the underlying asset price and yet produces remarkably accurate results versus the market. The new formalism applies for general Markovian stochastic behavior including continuous and discontinuous (jump) processes and in its broadest scheme contains all known models for Markovian option pricing and some new ones. The method is based on obtaining the risk neutral density function that satisfies a consistency condition, guaranteeing no arbitrage. For example, we show that when the underlying asset undergoes a continuous stochastic process with deterministic time dependent standard deviation the formalism produces the Black-Scholes-Merton formula without using a Wiener process. We show that in the general case the price of European options depends only on all the moments of the price return of the underlying asset. We offer a method to calculate the prices of European options when the volatility smile at maturity is independent of the term structure prior to the maturity, as observed in options markets. In the continuous case where only moments up to second order contribute to the price then any set of three option prices with the same maturity contains the information to determine the whole volatility smile for this maturity. In all the many examples we examined our method generates option prices that match the option markets prices very accurately in all asset classes. This confirms that the options market exhibits no-arbitrage. Moreover, using bootstrapping we demonstrate how to determine the conditional density function from inception to maturity, thus allowing the calculation of path dependent options. The new formalism also allows for the replication of ‘W-shape’ volatility smile that infrequently appears in some equity markets.

Non-structural approach to implied moments extraction

Moments of future prices and returns are not observable, but it is possible to measure them indirectly. A set of option prices with the same maturity but with different exercise prices are used to extract implied probability distribution of the underlying asset at the expiration date. The aim is to obtain market expectations from options and to investigate which non-structural model for estimating implied probability distribution gives the best fit. Non-structural models assume that only dynamics in prices is known. Mixture of two log-normals (MLN), Edgeworth expansions and Shimko’s model (representatives of parametric, semiparametric and nonparametric approaches respectively) are compared. Previous researches are inconclusive about the superiority of one approach over the others. This article contributes to finding which approach dominates. The best fit model is used to describe moments of the implied probability distribution. The sample covers one-year data for DAX index options. The results are compared through models and maturities. All models give better short-term forecasts. In pairwise comparison, MLN is superior to other approaches according to mean squared errors and Diebold-Mariano test in the observed period for DAX index options.

Large Dynamic Covariance Matrices

Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroscedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In the cross-section, the key is to correct in-sample biases of sample covariance matrix eigenvalues; a favored model is nonlinear shrinkage, derived from Random Matrix Theory (RMT). The present article marries these two strands of literature to deliver improved estimation of large dynamic covariance matrices. Supplementary material for this article is available online.

A Recovery that We Can Trust? Deducing and Testing the Restrictions of the Recovery Theorem

How reliable is the recovery theorem of Ross (2015)? We explore this question in the context of options on the 30-year Treasury bond futures, allowing us to deduce restrictions that link the physical and risk-neutral return distributions. Our empirical results undermine the implications of the recovery theorem. First, we reject an implicit assumption of the recovery theorem that the martingale component of the stochastic discount factor is identical to unity. Second, we consider the restrictions between the physical and risk-neutral return moments when the recovery theorem holds, and reject them in both forecasting regressions and generalized method of moments estimations. Received November 7, 2016; editorial decision July 24, 2017 by Editor Stijn Van Nieuwerburgh. 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.

A Theory of Dissimilarity between Stochastic Discount Factors

This paper proposes a measure of dissimilarity between stochastic discount factors (SDFs) in different economies. The SDFs are made comparable using the respective bond prices as the numeraire. The measure is dimensionless, synthesizes features of the risk-neutral moments of excess currency returns, and can be extracted from currency option prices. Linking theory to data, we provide evidence gathered from (i) the cross-section of 45 currency option prices, (ii) the time-series of currency returns, (iii) estimated SDFs using model-free restrictions, and (iv) structural models in international finance.

An Approach to Measure the Expectation of Generic Functions of the Market Return

This paper proposes an approach that associates the risk-neutral probability measure with option prices and then computes the expectation of quantities under the real world probability measure, exploiting the form of the stochastic discount factor. This approach deviates from foundational approaches that embrace assumptions about preferences and economic primitives to propose formulas for prices and risk premiums. Our method is analytically tractable, absolved of distributional assumptions, and we use the approach to elaborate on empirical questions regarding disaster probabilities, conditional return moments, and conditional return asymmetries.

Risk-adjusted option-implied moments

This paper provides a new way of converting risk-neutral moments into the corresponding physical moments, which are required for many applications. The main theoretical result is a new analytical representation of the expected payoffs of put and call options under the physical measure in terms of current option prices and a representative investor’s preferences. This representation is then used to derive analytical expressions for a variety of ex-ante physical return moments, showing explicitly how moment premiums depend on current option prices and preferences. As an empirical application of our theoretical results, we provide option-implied estimates of the representative stock market investor’s disappointment aversion using S&P 500 index option prices. We find that disappointment aversion has a procyclical pattern. It is high in times of high index levels and declines when the index falls. We confirm the view that investors with high risk aversion and disappointment aversion leave the stock market during times of turbulence and reenter it after a period of high returns.

The Diversification Delta: A Different Perspective

In a 2012 article published in The Journal of Portfolio Management, Vermorken, Medda, and Schroder introduce a new measure of diversification, the Diversification Delta (DD), based on the entropy of the portfolio return distribution. Entropy as a measure of uncertainty has been used successfully in several frameworks and takes into account the entire statistical distribution, rather than just the first two moments. In this article, the authors highlight some drawbacks of the DD measure and go on to propose an alternative measure based on exponential entropy that overcomes the identified shortcomings. The authors present the properties of this new measure and propose it as an alternative for portfolio optimization that incorporates higher moments of asset returns, such as skewness and excess kurtosis.

Recent Developments in Copula Models

Copula models have become very popular and well studied among the scientific community.[...]

Disaster risk and asset returns: An international perspective

Recent studies have shown that disaster risk can generate asset return moments similar to those observed in the U.S. data. However, these studies have ignored the cross-country asset pricing implications of the disaster risk model. This paper shows that standard U.S.-based disaster risk model assumptions found in the literature lead to counterfactual international asset pricing implications. Given consumption pricing moments, disaster risk from this literature cannot explain the range of equity premia and government bill rates. Furthermore, the independence of disasters presumed in some studies generates counterfactually low cross-country correlations in equity markets. Alternatively, if disasters are all shared, the model generates correlations that are excessively high. We show that common and idiosyncratic components of disaster risk are needed to explain the pattern in consumption and equity co-movements.