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Risk Premium: Insights Over The Threshold
Abstract
The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries.
... Our methodology offers reliable VaR measures for thinly traded markets using out-of-sample (one-day ahead) tests. We obtain comparable levels of VaR measures in relation to previous studies, which use similar markets to the one used in our implementation but with complete panel of prices (e.g., Kiesel et al., 2000; Fernandez, 2003; Bao et al., 2006; and Fernandes et al., 2008). Moreover, we show that the best VaR methods to describe the market risk in an environment with thinly traded assets are those that characterize the left tail of the conditional distribution modeling heteroskedastic financial return series (e.g. ...
... Fernandez (2003) finds that the dynamic EVT methods perform best using Chilean market data (but with a complete panel of prices). Fernandes et al. (2008) analyze VaR measures for 41 countries, where they show the superior performance of EVT methods. In addition, Kiesel et al. (2000) provide an analysis for emerging markets using Brady bonds reporting similar measures to those presented in our study for VaR measures at 5% and 1% confidence levels. ...
Thinly traded securities exist in both emerging and well developed markets. However, plausible estimations of market risk measures for portfolios with infrequently traded securities have not been explored in the literature. We propose a methodology to calculate market risk measures based on the Kalman filter which can be used on incomplete datasets. We implement our approach in a fixed-income portfolio within a thin trading environment. However, a similar approach may be also applied to other markets with thinly traded securities. Our methodology provides reliable market risk measures in portfolios with infrequent trading.
... The APM provides an approximate relation for asset returns with an unknown number of unidentified factors. We take the perspective of a US-based, internationally diversified investor [3]. Thus the risk-free rate should compensate this investor for the dollar's expected loss of purchasing power, and the risk premium should compensate the investor for bearing different risks (market risk, credit risk, operational risk, etc.). ...
It is well known that several industries, like the hotel industry, are subject to low frequency high impact events resulting from their operations. However, there is a dearth of academic research in this area. In this paper we propose an innovative methodology to study the problem using a combination of Asset Pricing Models and an original database. We find that asset prices compensate investors not only for market and credit risk, but also for operational risk.
... eni (1989) in a study about organizational bankruptcy posit that creditors wish to avoid recognizing losses and thus tend to assume more risk then they would otherwise take. Wiseman and Gomez-Mejia (1998) argue that prospect and agency theories can be understood as complementing each other for reaching better predictions of risk taking by managers. Fernandes et al. (2008), in an analysis of risk factors in forty-one international stock markets, show that tail risk is a relevant risk factor. We argue that tail risk can be associated with loss aversion and therefore the BAM offers more fruitful results in the professional managers' context. Now, we will comment on the main criticism received by this approa ...
Standard models of moral hazard predict a negative relationship between risk and incentives; however, empirical studies on mutual funds present mixed results. In this article, we propose a behavioral principal-agent model in the context of professional managers, focusing on active and passive investment strategies. Using this general framework, we evaluate how incentives affect the risk-taking behavior of managers, considering the standard moral hazard model as a special case, and solve the previous contradiction. Empirical evidence, based on a comprehensive world sample of 4584 mutual funds, gives support to our theoretical model.
The objective of this paper is twofold. The first is to incorporate mental accounting, loss aversion, asymmetric risk-taking behavior, and probability weighting in a multi-period portfolio optimization for individual investors. While these behavioral biases have previously been identified in the literature, their overall impact during the determination of optimal asset allocation in a multi-period analysis is still missing. The second objective is to account for the estimation risk in the analysis. Considering 26 daily index stock data over the period from 1995 to 2007, we empirically evaluate our model (BRATE - Behavior Resample Technique) against the traditional Markowitz model.
Since the innovative work of Kahneman and Tversky (1979), behavioral finance has become one of the most active areas in financial economics. As compared to traditional models in this area, behavioral models often have the degree of flexibility that permits reinterpretation to fit new facts. Unfortunately, this flexibility makes it hard either to disprove or to validate behavioral models. In the present thesis we try to overcome this problem, by proposing a general framework based on stylized facts of human behavior (invariants); and applying it to three financial contexts. Related to the individual risk taking decision, we focus: on the role of incentives; on how prior outcomes influence future decision; and on the portfolio choice problem. Different from traditional models where risk aversion is usually assumed, in our behavioral framework, the risk preference of the investor varies depending on how he frames his choices. Our main conclusion is that absolute evaluations based on final wealth are limited and the relativity of risk taking decisions, where the perception of gains and losses drives the process, is a requirement to understand individual’s decisions. As a reply to behavioral critics, we reach propositions that can be falsified and make several predictions. Summing up, under the same theoretical framework we make the following contributions: (1) Shed more light on how incentives affect risk taking behavior, proposing a model where the reference plays a central role in the previous relationship; (2) Empirically test our incentives model considering a comprehensive world sample of equity funds (cross-section data); (3) Evaluate which effect is stronger in multi-period risk taking decisions for individual investors: house-money or disposition effect, proposing a novel experiment treatment; (4) Compare survey to experimental results finding that they are not necessarily aligned. (5) Generalize the myopic loss aversion behavior performing an experiment in Brazil and Spain; (6) Propose a theoretical model of how behavioral individuals choose their portfolios in terms of risk taking behavior; (7) Include estimation error in the behavioral portfolio analysis; (8) Evaluate whether theoretical models of portfolio choice should adapt or moderate the individual behavior characteristics. In this case we used a sample of several country equity indices. Our propositions suggest that managers in passive managed funds tend to be rewarded without incentive fee and be risk averse. On the other hand, in active managed funds, whether incentives will reduce or increase the riskiness of the fund will depend on how hard it is to outperform the benchmark. If the fund is (un)likely to outperform the benchmark, incentives (increase) reduce the manager’s risk appetite. Furthermore, the evaluative horizon influences the trader’s risk preferences, in the sense that if traders performed poorly (well) in a period, they tend to choose riskier (conservative) investments in the following period given the same evaluative horizon. Empirical evidence, based on a comprehensive world sample of mutual funds is also presented in this chapter and gives support to the previous propositions. Related to the house-money and disposition effect, we employ a survey approach and find evidence of house money effect. However, in an experiment performed in a dynamic financial setting, we show that the house money effect disappears, and that disposition is the dominant effect. We report supportive results related to existence of myopic loss aversion across countries and some evidence of country effect. Finally, in terms of asset allocation, our results support the use of our behavioral model (BRATE) as an alternative for defining optimal asset allocation and posit that a portfolio optimization model may be adapted to the individual biases implied by prospect theory without efficiency loss. We also explain why investors keep on holding, or even buy, loosing investments. ______________________________________________________________________________________________________________________________________________________ Desde el trabajo innovador de Kahneman y de Tversky (1979), las finanzas del comportamiento se han convertido en una de las áreas más activas en la economía financiera. Comparados con los modelos tradicionales en esta área, los modelos del comportamiento tienen a menudo el grado de flexibilidad que permite su reinterpretación, ajustándoles a nuevos hechos empíricos. Desafortunadamente, esta flexibilidad hace difícil refutar o validar modelos del comportamiento. En la actual tesis intentamos superar este problema, proponiendo un marco general basado en hechos estilizados del comportamiento humano (invariables); y aplicándolo a tres contextos financieros. Relacionado con la decisión individual de toma de riesgo, nos enfocamos: en el papel de los incentivos financieros; en cómo los resultados anteriores influencian la decisión futura; y en el problema de la gerencia de cartera. Diferente de los modelos tradicionales donde la aversión al riesgo se asume generalmente, en nuestro marco del comportamiento, la preferencia del riesgo del inversionista varía dependiendo de cómo él enmarca sus opciones. Nuestra conclusión principal es que las evaluaciones absolutas basadas en la riqueza final son limitadas y la relatividad de las decisiones de riesgo, donde la percepción de las ganancias y de las pérdidas conduce el proceso, son un requisito para entender las decisiones del individuo. Como contestación a los críticos del comportamiento, alcanzamos proposiciones que pueden ser contrastadas y hacemos varias predicciones. Resumiendo, bajo el mismo marco teórico hacemos las contribuciones siguientes: (1) Verificamos cómo los incentivos afectan el comportamiento de toma de riesgo, proponiendo un modelo donde la referencia desempeña un papel central en la relación anterior; (2) Empíricamente contrastamos nuestro modelo de incentivos en vista de una muestra mundial representativa de los fondos de acciones; (3) Evaluamos qué efecto es más fuerte en un estudio dinámico del comportamiento individual de toma de riesgo: house-money o disposition effect, proponiendo un nuevo tratamiento experimental; (4) Comparamos los resultados de una encuesta a los experimentales y verificamos que no están necesariamente alineados. (5) Generalizamos el comportamiento miope de la aversión de la pérdida (myopic loss aversion) con un experimento realizado en Brasil y España; (6) Proponemos un modelo teórico de cómo los individuos sesgados eligen sus carteras en términos de toma de riesgo; (7) Incluimos el error de la estimación en el análisis de la cartera; (8) Evaluamos si los modelos teóricos de la selección de carteras deben adaptarse a las características sesgadas de los individuos o moderar las características individuales del comportamiento. En este caso utilizamos una muestra de varios índices de acciones de diversos países. Nuestras proposiciones sugieren que los gerentes en fondos manejados pasivamente tiendan a ser recompensados sin el honorario variable y sean adversos al riesgo. Por otra parte, en fondos manejados activamente, si los incentivos reducirán o aumentarán la toma de riesgo del fondo dependerá de su posibilidad de superar la rentabilidad de su cartera de referencia. Si es probable que el fondo supere la cartera de referencia, los incentivos (aumentan) reducen el apetito del riesgo del gerente. Además, el horizonte evaluativo influencia las preferencias del riesgo del gerente, en el sentido que si han obtenido mal (buenos) resultados en un período, ellos tienden para elegir inversiones (conservadoras) más arriesgadas en el período siguiente dado el mismo horizonte evaluativo. La evidencia empírica presentada en la tesis considerando una base representativa de fondos de acciones soporta esas predicciones. Relacionado con el house money y disposition effect, empleamos una encuesta y verificamos la existencia de house money. Sin embargo, en un experimento demostramos que desaparece el efecto house money, y que disposition es el efecto dominante. Nuestros resultados también soportan la existencia de la aversión miope de la pérdida a través de países y una cierta evidencia del efecto del país. Finalmente, en términos de gerencia de carteras, nuestros resultados apoyan el uso de nuestro modelo del comportamiento (BRATE) como alternativa para definir la asignación óptima de los activos y postulamos que un modelo optimización de cartera se puede adaptar a los sesgos individuales implicados por la teoría (prospect theory) sin pérdida de la eficacia.
The trustees of funded defined benefit pension schemes must make two vital and inter-related decisions - setting the asset allocation and the contribution rate. While these decisions are usually taken separately, it is argued that they are intimately related and should be taken jointly. The objective of funded pension schemes is taken to be the minimization of both the mean and the variance of the contribution rate, where the asset allocation decision is designed to achieve this objective. This is done by splitting the problem into two main steps. First, the Markowitz mean-variance model is generalised to include three types of pension scheme liabilities (actives, deferreds and pensioners), and this model is used to generate the efficient set of asset allocations. Second, for each point on the risk-return efficient set of the asset-liability portfolio model, the mathematical model of Haberman (1992) is used to compute the corresponding mean and variance of the contribution rate and funding ratio. Since the Haberman model assumes that the discount rate for computing the present value of liabilities equals the investment return, it is generalised to avoid this restriction. This generalisation removes the trade-off between contribution rate risk and funding ratio risk for a fixed spread period. Pension schemes need to choose a spread period, and it is shown how this can be set to minimise the variance of the contribution rate. Finally, using the result that the funding ratio follows an inverted gamma distribution, shortfall risk and expected tail loss are computed for funding below the minimum funding requirement, and funding above the taxation limit. This model is then applied to one of the largest UK pension schemes - the Universities Superannuation Scheme
The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries.
Using synthetic tests performed on time series with time dependence in the volatility with both Pareto and Stretched-Exponential distributions, it is shown that for samples of moderate sizes the standard generalized extreme value (GEV) estimator is quite inefficient due to the possibly slow convergence toward the asymptotic theoretical distribution and the existence of biases in the presence of dependence between data. Thus, it cannot distinguish reliably between rapidly and regularly varying classes of distributions. The Generalized Pareto distribution (GPD) estimator works better, but still lacks power in the presence of strong dependence. Applied to 100 years of daily returns of the Dow Jones Industrial Average and over one years of five-minutes returns of the Nasdaq Composite index, the GEV and GDP estimators are found insufficient to prove that the distributions of empirical returns of financial time series are regularly varying, because the rapidly varying exponential or stretched exponential distributions are equally acceptable.
The paper examines the size effect reversal in the USA over the period 1970-1999, using data for the ten size deciles in the CRSP tapes during this 40-year period. Betas for small-firm portfolios increase as the return interval analysed increases, and are lower than large-firm portfolios for daily data but higher for monthly and quarterly data. Differences between small- and large-firm portfolio returns are associated with higher betas as return intervals increase, with lower betas for daily data, and higher for quarterly data. Before 1981 when the small-firm effect was published, smaller firms' relative risk coefficients were biased downwards compared to aggregated coefficients, while larger firms' were biased upwards, as expected. But after 1981, a partial reversal occurred with larger firms' relative risk coefficients also biased downwards and by more than the smaller firms. In the post-period, relative risk measures generated higher abnormal returns for large firms than for small firms, effectively a large-firm effect, because large firms' risks were more understated, possibly due to their relatively less frequent trading. However, these abnormal returns were reduced for large (and small) firms when using more appropriate aggregated risk coefficients.
The conditional distribution of asset returns is important for a number of applications in finance, including financial risk management, asset pricing and option valuation. In the GARCH framework, it is typically assumed that returns are drawn from a symmetric conditional distribution such as the normal, Student-t or power exponential. However, the use of a symmetric distribution is inappropriate if the true conditional distribution of returns is skewed. This study models the conditional distribution of daily returns in five international equity market indices and a world equity index using the skewed generalised-t (SGT) distribution, a distribution that allows for a very wide range of skewness and kurtosis, and which nests the three most commonly used distributions as special cases. It is shown that the use of a conditional SGT distribution offers a substantial improvement in the fit of both GARCH and EGARCH models. Moreover, for both models, the study strongly rejects the restrictions on the SGT that are implied by the normal, Student-t and power exponential distributions. With the GARCH specification, the conditional distribution is negatively skewed for all six series. However, for three of these series - namely the US, Japan and the World index - this skewness can be explained by leverage effects, which are captured by the EGARCH model. For the remaining three series - the UK, Canada and Germany - the skewness in the conditional distribution of returns remains even after allowing for leverage effects.
We compare expected shortfall with value-at-risk (VaR) in three aspects: estimation errors, decomposition into risk factors, and optimization. We describe the advantages and the disadvantages of expected shortfall over VaR. We show that expected shortfall is easily decomposed and optimized while VaR is not. We also show that expected shortfall needs a larger size of sample than VaR for the same level of accuracy.
The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries.
Introduction There has been a controversy in this journal about using downside risk measures in portfolio analysis. The downside risk measures supposedly are a major improvement over traditional portfolio theory. That is where the battle lines clashed when Rom and Ferguson (1993, 1994b) and Kaplan and Siegel (1994a, 1994b) engaged in a "tempest in a teapot". I should confess that I am strong supporter of downside risk measures and have used them in my teaching, research and software for the past two decades. Therefore, you should keep that bias in mind as you read this article. One of the best means to understand a concept is to study the history of its development. Understanding the issues facing researchers during the development of a concept results in better knowledge of the concept. The purpose of this paper is to provide an understanding of the measurement of downside risk. First, it helps to define terms. Portfolio theory is the application of decision-making tools unde
Good estimates for the tails of loss severity distributions are essential for pricing or positioning high-excess loss layers in reinsurance. We describe parametric curve-fitting methods for modelling extreme historical losses. These methods revolve around the generalized Pareto distribution and are supported by extreme value theory. We summarize relevant theoretical results and provide an extensive example of their application to Danish data on large fire insurance losses. Keywords: Loss severity distributions; high excess layers; extreme value theory; excesses over high thresholds; generalized Pareto distribution. 1 Introduction Estimating loss severity distributions from historical data is an important actuarial activity in insurance. A standard reference on the subject is Hogg & Klugman (1984). In this paper we are specifically interested in estimating the tails of loss severity distributions. This is of particular relevance in re-insurance if we are required to choose or...
Within econometrics, probability theory and statistics, an enormous literature exists on the topic of extremes in economics. See, for instance, Mikosch [47] and Klüppelberg [36] in this volume, and the numerous references listed in those contributions. For a long time, econometric research has shown that, for instance, logarithmic returns of financial data are nonnormal. Extremal moves up or down do occur much more regularly than standard (normal based) models make us believe. Extreme value theory (EVT) has become a standard toolkit within quantitative finance useful for describing these nonnormal phenomena. Statistically exploring and stochastically modelling such extremes in financial data is, however, a rather different task from answering the question: “Given a financial market where such extremes occur, how are they to be handled from an economic point of view?” Perhaps the most striking answer to the question came in the late eighties, early nineties through the emergence of risk management (RM) regulations. In this chapter, I want to highlight some of these developments, stress the interplay between EVT and RM and hint at possible areas of research where the focus is more on the second part of the title, “the Economics of Extremes.” Several references will guide the reader to related publications.
We study whether the behavior of stock prices, in relation to size and book-to-market-equity (BE/ME), reflects the behavior of earnings. Consistent with rational pricing, high BE/ME signals persistent poor earnings and low BE/ME signals strong earnings. Moreover, stock prices forecast the reversion of earnings growth observed after firms are ranked on size and BE/ME. Finally, there are market, size, and BE/ME factors in earnings like those in returns. The market and size factors in earnings help explain those in returns, but we find no link between BE/ME factors in earnings and returns.
A growing literature contends that, since returns are not normal, higherorder comoments matter to risk-averse investors. Fama and French (1993, 1995) find that nonmarket risk factors based on size and book-to-market ratio are priced by investors. We test the hypothesis that the Fama-French factors simply proxy for the pricing of higherorder comoments. Using portfolio returns over various time horizons, we show that adding a set of systematic comoments (but not standard moments) of order 3-10 reduces the explanatory power of the Fama-French factors to insignificance in almost every case.
We consider the estimation of quantiles in the tail of the marginal distribution of financial return series, using extreme value statistical methods based on the limiting distribution for block maxima of stationary time series. A simple methodology for quantification of worst case scenarios, such as ten or twenty year losses is proposed. We validate methods on a simulated series from an ARCH(1) process showing some of the features of real financial data, such as fat tails and clustered extreme values; we then analyse daily log returns on a share price.
A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-a-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently-popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally-integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.
Testing the hypothesis that international equity market correlation increases in volatile times is a difficult exercise and misleading results have often been reported in the past because of a spurious relationship between correlation and volatility. Using “extreme value theory” to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Empirically, we reject the null hypothesis of multivariate normality for the negative tail, but not for the positive tail. We also find that correlation is not related to market volatility per se but to the market trend. Correlation increases in bear markets, but not in bull markets.
A precise estimation of the tail shape of forex returns is of critical importance for proper risk assessment. We improve upon the efficiency of conventional estimators that rely on a first order expansion of the tail shape, by using the second order expansion. Here we advocate a moments estimator for the second term. The paper uses both Monte Carlo simulations and the high frequency foreign exchange recordings collected by the Olsen corporation to illustrate the technique.
In this paper, we develop a Capital Asset Pricing Model (CAPM) using a mean-lower partial moment framework. We explicitly derive formulae for the equilibrium values of risky assets that hold for arbitrary probability distributions. We show that when the probability distributions and portfolio returns are either normal, stable (with the same characteristic exponent between 1 and 2 and the same skewness parameter, not necessarily zero), or Student-t distributions, our CAPM reduces to the traditional mean-scale CAPM's. Consequently, since the traditional equilibrium models are special cases of our model, the mean-lower partial moment framework is guaranteed to do at least as well in explaining market data. As an application of our theory, we derive an acceptance criterion for capital investment projects and note that corporate finance theory results developed, for example, in the well-known mean- variance framework carry over to the mean-lower partial moment framework.
This study examines the use of downside risk measures in the construction of an optimal international portfolio, with particular reference to the estimated allocations in emerging markets and the out-of-sample performance of the optimal portfolios. The use of downside risk measures is assessed due to the problems of using a conventional mean-variance analysis approach in the presence of the non-normality often found to be present in emerging market data. The data set used consists of the MSCI indices for developed equity markets and the IFC data set on emerging markets. The primary component of the paper consists of the construction of optimal portfolios under both mean-variance and downside risk frameworks. In addition, the use of Bayes–Stein estimators is also assessed, in an attempt to reduce estimation error. The resulting estimated allocations are then used to assess the out-of-sample performance of the optimal portfolios. The results indicate that for risk-averse investors the use of downside risk measures can result in significant improvements in performance.
We examine the robustness of size and book-to-market effects in 35 emerging equity markets during 1985–2000. Mean returns for high book-to-market firms significantly exceed mean returns for low book-to-market firms. These findings are robust to tests that control for size effects and that remove extreme returns. Similarly, mean returns for small firms exceed mean returns for large firms. But, the firm size results lack robustness to the removal of extreme returns. Moreover, significant size effects are found in tests that define firm size relative to the local market average, but generally are not found in tests that use absolute firm size. Our findings are confirmed by cross-sectional regressions that control for systematic risk at the global and local levels.
This article gives an empirical view of the ex post equity risk premium in a number of international markets with special attention to emerging ones. Our study yields interesting implications for finance. Conform expectations we find that the equity risk premium in emerging markets is significantly higher than in developed markets. However, the extent to which emerging stock markets reward investors varies through time. We observe that the time varying nature of the equity risk premium relates more to economic cycles than to the presence of some sort of structural break based on stock market liberalisations. The distribution of equity risk premium in emerging market is neither normally nor symmetrically distributed, which suggests that investors should focus more on downside risk instead of standard deviations.
Value-at-Risk (VaR) has become a standard risk measure for financial risk management. However, many authors claim that there are several conceptual problems with VaR. Among these problems, an important one is that VaR disregards any loss beyond the VaR level. We call this problem the “tail risk”. In this paper, we illustrate how the tail risk of VaR can cause serious problems in certain cases, cases in which expected shortfall can serve more aptly in its place. We discuss two cases: concentrated credit portfolio and foreign exchange rates under market stress. We show that expected shortfall requires a larger sample size than VaR to provide the same level of accuracy.
This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation.
The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries.
The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries.
The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries.
Enviroment of international financial mangement -- Foreign exchange and derivatives markets -- Foreign exchange risk management -- Financing the multinational corporation -- Foreign invesment analysis -- Multinational working capital management
A growing literature contends that, since returns are not normal, higher-order comoments matter to risk-averse investors. Fama and French (1993, 1995) find that nonmarket risk factors based on size and book-to-market ratio are priced by investors. We test the hypothesis that the Fama-French factors simply proxy for the pricing of higher-order comoments. Using portfolio returns over various time horizons, we show that adding a set of systematic comoments (but not standard moments) of order 3–10 reduces the explanatory power of the Fama-French factors to insignificance in almost every case.
In this paper, an alternative method of estimating the systematic risk for Canadian stocks is presented and empirically investigated. The method proposed is applied to a set of data impacted by censoring - the presence of zero returns, which occurs in extreme cases of thin trading. The approach used is the sample selectivity model, which is a two-step procedure: with a selectivity component and a regression component. In addition, this study compares the new beta estimate to the standard OLS beta and the Dimson Beta. The results indicate that the selectivity-corrected beta does correct the downward bias of the OLS estimates and possesses desirable statistical properties.
The current study contributes to the empirical literature aimed at testing the Fama and French three-factor model, using daily Australian data. In general, the evidence found is quite favourable to the model based on formal asset pricing tests. However, when the estimated risk premia are taken into account, the support for the Fama-French model is less persuasive. In particular, a negative size premium is uncovered consistent with a wave of recent findings questioning its continued existence over recent years.
This paper provides a statistical analysis of high-frequency recordings of the German share price index DAX. The data set extends from November 1988 to the end of the year 1995 and includes all minute-to-minute changes during trading hours at the Frankfurt Stock Exchange. The focus of this study is on the limiting behaviour characterizing the tail regions of the empirical distribution. Application of the popular Hill estimator for the tail shape yields results very similar to those of other analyses of speculative returns. However, since the reliability of tail index estimation rests on the appropriateness of the tail regions, the question of optimally choosing the sample fraction emerges. Exploiting recent advances in extreme value theory a couple of novel approaches are applied for determining the optimum cut-off value for the 'tail' of the empirical distribution. As it turns out, most algorithms suggest that one has to go out quite far into the tails for estimation of the extremal index. The findings obtained at the highest frequency (minute-to-minute returns) are confirmed when considering data at various levels of time-aggregation. A test for stability of extreme value behaviour over time gives no clear indication of changes of the limiting distribution. It is also illustrated how the approximation of the tails can be used to estimate the likelihood of large returns.
The low correlation between returns in emerging equity markets and industrial equity markets implies that the global investor
would benefit from diversification in emerging markets. This article explores the sensitivity of the emerging-market returns
to measures of global economic risk. When these traditional measures of risk are used, the emerging markets have little or
no sensitivity. This finding is consistent with these markets' being segmented from world capital markets. However, the correlation
between the emerging-market returns and the risk factors appears to be changing over time.
We investigate whether or not a beta increases with bad news and decreases with good news, just as does volatility. Using daily returns for nine stocks in a double beta model with EGARCH specifications, we show that news asymmetrically affects the betas of individual stocks. We find that betas depend on two source of news: market shocks and idiosyncratic shocks. Some stock betas depend on both while others depend on one. We categorize each stock return as belonging to one of three beta process models, a joint, an idiosyncratic, and a market model based on the role of market shocks and idiosyncratic shocks. Our conclusions differ from those of Brown, Nelson, and Sunnier (1995) who worked with monthly aggregated data in a bivariate EGARCH model. We believe that stock price aggregation in this previous research resulted in a loss of cross sectional variation and consequently lead to weak results. If the asymmetric effect is more readily apparent in daily data, then this may again explain previous researchers' inability to detect asymmetric effects. Our findings shed light on the controversy as to whether abnormalities in stock returns result from overreaction to information or from changes in expected returns in an efficient market. Finding an asymmetric effect in betas leads us to conclude that abnormalities can, at least partially, be explained by changes in expected returns through a change in beta.
Recent literature has trumpeted the claim that extreme value theory (EVT) holds promise for accurate estimation of extreme quantiles and tail probabilities of financial asset returns, and hence hold promise for advances in the management of extreme financial risks. Our view, based on a disinterested assessment of EVT from the vantage point of financial risk management, is that the recent optimism is partly appropriate but also partly exaggerated, and that at any rate much of the potential of EVT remains latent. We substantiate this claim by sketching a number of pitfalls associate with use of EVT techniques. More constructively, we show how certain of the pitfalls can be avoided, and we sketch a number of explicit research directions that will help the potential of EVT to be realized.
The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries.
The authors study whether the behavior of stock prices, in relation to size and book-to-market equity (BE/ME), reflects the behavior of earnings. Consistent with rational pricing, high BE/ME signals persistent poor earnings and low BE/ME signals strong earnings. Moreover, stock prices forecast the reversion of earnings growth observed after firms are ranked on size and BE/ME. Finally, there are market, size, and BE/ME factors in earnings like those in returns. The market and size factors in earnings help explain those in returns but the authors find no link between BE/ME factors in earnings and returns. Copyright 1995 by American Finance Association.
Business cycles in different regions of the United States tend to synchronize. This study investigates the reasons behind this synchronization of business cycles and the consequent formation of a national business cycle. Trade between regions may not be strong enough for one region to "drive" business cycle fluctuations in another region. This study suggests that regional business cycles synchronize due to a nonlinear "mode-locking" process in which weakly coupled oscillating systems (regions) tend to synchronize. There is no definitive test for mode-lock. However, simulations, correlations, Granger causality tests, tests for nonlinearities, vector autoregressions, and spectral analysis reveal modest econometric support for the regional mode-locking hypothesis of business cycle synchronization. Copyright Blackwell Publishers, 2005
This paper is based on a talk with the above title given at the SemStat meeting on Extreme Value Theory and Applications in Gothenburg on December 13, 2001
: Every company evaluating an investment project or an acquisition in an emerging market must not only estimate cash flows but also an appropriate discount rate. Although not free from controversy, the cost of equity in developed markets is typically estimated with the CAPM. In emerging markets, however, betas and stock returns seem to be unrelated. This article argues that total risk, idiosyncratic risk, and downside risk are significantly related to emerging-market stock returns, and that a cost of equity based on downside risk, in particular, yields a more plausible estimate than that based on systematic risk or total risk. * Ana Cirera provided valuable research assistance. The views expressed below and any errors that may remain are entirely my own. ** IESE / Avda. Pearson, 21 / 08034 Barcelona / Spain TEL: (34-93) 253-4200 / FAX: (34-93) 253-4343 / EMAIL: jestrada@iese.edu 2 I- INTRODUCTION Every company evaluating an investment project or an acquisition in an emerging mar...
This paper examines a comprehensive list of 18 different risk factors that potentially impact international equity returns. I investigate whether these risk factors explain the cross-section of average returns in 47 countries. I also analyze whether the same risk factors influence developed and emerging market returns. I find evidence that an asset pricing framework that incorporate skewness has success in explaining average returns. 2 1. Introduction What are the drivers of average returns in international markets? Are the sources of risk that impact developed market returns the same as the risk factors that affect emerging market returns? These are the questions that are explored in this study. The work is closely related to Harvey (1995a,b) and especially Estrada (2000) who examines a number of risk measures and relates these measures to emerging market returns. However, there are two key differences. First, we expand the list of risk metrics. Second, we examine both developed a...
If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios. THE SINGLE FACTOR CAPITAL ASSET PRICING MODEL ~CAPM! of Sharpe ~1964! and Lintner ~1965! has come under recent scrutiny. Tests indicate that the crossasset variation in expected returns cannot be explained by the market beta alone. For example, a growing number of studies show that "fundamental" variables such as size, book-to-market value, and price to earnings ratios account for a sizeable portion of the cross-sectional variation in expected returns ~see, e.g., Chan, Hamao, and Lakonishok ~1991! and Fama and French ~1992!!. Fama and French ~1995! document the importance of SMB ~the difference between the return on a portfolio of small size stocks and the return on a portfolio of large size stocks! and HML ~the difference between the return on a portfolio of high book-to-market value stocks and the return on a portfolio of low book-to-market value stocks!. There are a number of responses to these empirical findings. First, the single-factor CAPM is rejected when the portfolio used to proxy for the market is inefficient ~see Roll ~1977! and Ross ~1977!!. Roll and Ross ~1994! an...
Alternative beta risk estimators in cases of extreme thin trading: Canadian evidence USA, AppliedFinancial University,Duke defaulteffects,Bank For Time-varying betas and asymmetric effects of news: empirical analysis of blue chips stocks
- R Brooks
- R Faff
- Fry
- E Bheenick
Brooks,R., Faff,R.,Fry, Bheenick, E. (2005) Alternative beta risk estimators in cases of extreme thin trading: Canadian evidence, Applied Financial Economics, 15, 1251–58. USA, AppliedFinancial University,Duke defaulteffects,Bank For T.andBissoondoyal-Cho, Y. H. and Engle, R. (1999) Time-varying betas and asymmetric effects of news: empirical analysis of blue chips stocks, NBER Working Paper Series (7330)
Toward the development of an equilibrium capital-market model based on semivariance 1–11. co-moments,Journal of inAdvancesin the Environment(Eds) Emerging Markets Journal ofFinancialand 58 J Downloaded By: [University Carlos III of Madrid] At: 08:45 1
- W Hogan
- J . L B Warren
- Fernandes
Hogan, W. and Warren, J. (1974) Toward the development of an equilibrium capital-market model based on semivariance, Journal of Financial and Quantitative Analysis, 9, 1–11. co-moments,Journal of inAdvancesin the Environment(Eds) Emerging Markets Journal ofFinancialand 58 J. L. B. Fernandes et al. Downloaded By: [University Carlos III of Madrid] At: 08:45 1 March 2010 rHwang, S. and Salmon, M. (2006) Sentiment and beta herding, behavioural finance and market efficiency, Warwick Business School
We considered the following devel-oped markets United States; and the following emerging markets: Argentina Robustness of size and value effects in emerging equity markets
- Australia
- Austria
- Belgium
- Canada
- Denmark
- Finland
- France
- Germany
- Ireland
- Italy
- Netherlands
- Norway
- Portugal
- Spain
- Sweden
- Switzerland
- United
- Kingdom
we examined a sample of daily index returns (MSCI database) for the period from 4th April 1995 to 30th December 2005. We considered the following devel-oped markets: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States; and the following emerging markets: Argentina, Brazil, Chile, Colombia, Hong Kong, India, Indonesia, Israel, Jordan, Malaysia, Mexico, New Zealand, Pakistan, Peru, Philippines, Poland, Singapore, South Africa, South Korea, Taiwan, Thailand, (2002) Robustness of size and value effects in emerging equity markets, 1985–2000, Emerging Markets Review, 3, 1–30.
Value at Risk, Irwin
- P Jorion
Jorion, P. (1995) Value at Risk, Irwin, McGraw Hill, New York.
Modelling Extremal Events for Insurance The cost of equity in emerging markets: a downside risk approach, Emerging Markets Quarterly
- P Embrechts
- C Klu¨
- T Mikosch
- Finance
- Springer
- Berlin
- J Estrada
Embrechts, P., Klu¨, C. and Mikosch, T. (1997) Modelling Extremal Events for Insurance and Finance, Springer, Berlin. Estrada, J. (2000) The cost of equity in emerging markets: a downside risk approach, Emerging Markets Quarterly, 4, 19–30.
Sentiment and beta herding, behavioural finance and market efficiency
- S Hwang
- M Salmon
Hwang, S. and Salmon, M. (2006) Sentiment and beta herding, behavioural finance and market efficiency, Warwick Business School.
Extreme value theory for risk managers, Internal Modelling and CAD II published by RISK Books
- A Mcneil
McNeil, A. (1999) Extreme value theory for risk managers, Internal Modelling and CAD II published by RISK Books, London, pp. 93–113.
Systematic risk in emerging markets: the D-CAPM, Emerging Markets Review
- J Estrada
Estrada, J. (2002) Systematic risk in emerging markets:
the D-CAPM, Emerging Markets Review, 3, 365–79.