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Return Series (return + series)
Selected AbstractsTesting Parameters in GMM Without Assuming that They Are IdentifiedECONOMETRICA, Issue 4 2005Frank Kleibergen We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic ,2 distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic ,2 distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series. [source] Modelling volatility clustering in electricity price return series for forecasting value at riskEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 1 2009R. G. Karandikar Abstract Modelling of non-stationary time series using regression methodology is challenging. The wavelet transforms can be used to model non-stationary time series having volatility clustering. The traditional risk measure is variance and now a days Value at Risk (VaR) is widely used in finance. In competitive environment, the prices are volatile and price risk forecasting is necessary for the market participants. The forecasting period may be 1 week or higher depending upon the requirement. In this paper, a model is developed for volatility clustering in electricity price return series and its application for forecasting VaR is demonstrated. The first model is using GARCH (1, 1). The VaR of variance rate series, that is worst-case volatility is calculated using variance method using wavelet transform. The model is used to forecast variance rate (volatility) for a sample case of 1-week half-hourly price return series. The second model developed is for forecasting VaR for price return series of 440 days. This model is developed using wavelets via multi-resolution analysis and uses regime-switching technique. The historical data of daily average prices is obtained from 100% pool type New South Wales (NSW), a zonal market of National Electricity Market (NEM), Australia. Copyright © 2007 John Wiley & Sons, Ltd. [source] Volatility forecasting with double Markov switching GARCH modelsJOURNAL OF FORECASTING, Issue 8 2009Cathy W. S. Chen Abstract This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first-order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value-at-risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd. [source] Comparing density forecast models,JOURNAL OF FORECASTING, Issue 3 2007Yong Bao Abstract In this paper we discuss how to compare various (possibly misspecified) density forecast models using the Kullback,Leibler information criterion (KLIC) of a candidate density forecast model with respect to the true density. The KLIC differential between a pair of competing models is the (predictive) log-likelihood ratio (LR) between the two models. Even though the true density is unknown, using the LR statistic amounts to comparing models with the KLIC as a loss function and thus enables us to assess which density forecast model can approximate the true density more closely. We also discuss how this KLIC is related to the KLIC based on the probability integral transform (PIT) in the framework of Diebold et al. (1998). While they are asymptotically equivalent, the PIT-based KLIC is best suited for evaluating the adequacy of each density forecast model and the original KLIC is best suited for comparing competing models. In an empirical study with the S&P500 and NASDAQ daily return series, we find strong evidence for rejecting the normal-GARCH benchmark model, in favor of the models that can capture skewness in the conditional distribution and asymmetry and long memory in the conditional variance.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] Bias in the estimation of non-linear transformations of the integrated variance of returnsJOURNAL OF FORECASTING, Issue 7 2006Richard D. F. Harris Abstract Volatility models such as GARCH, although misspecified with respect to the data-generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non-linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non-linear transformations of these forecasts will be biased estimators of the same non-linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non-linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non-linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in-sample and out-of-sample forecasts of three non-linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] House Prices and InflationREAL ESTATE ECONOMICS, Issue 1 2002Ali Anari The present paper examines the long-run impact of inflation on homeowner equity by investigating the relationship between house prices and the prices of nonhousing goods and services, rather than return series and inflation rates as in previous empirical studies on the inflation hedging ability of real estate. There are two reasons for this methodological departure from prior practice: (1) while the total return on housing cannot be accurately measured, the total return on housing is fully reflected in housing prices, and (2) given that using returns or differencing a time series leads to a loss of long-run information contained in the series, valuable long-run information can be captured by using prices. Also, unlike previous related studies, we exclude housing costs from goods and services prices to avoid potential bias in estimating how inflation affects housing prices. Monthly data series are collected for existing and for new house prices as well as the consumer price index excluding housing costs for the period 1968,2000. Based on both autoregressive distributed lag (ARDL) models and recursive regressions, the empirical results yield estimated Fisher coefficients that are consistently greater than one over the sample period. Thus, we infer that house prices are a stable inflation hedge in the long run. [source] Random Walks and the Cointegration of the ACLI and NCREIFREAL ESTATE ECONOMICS, Issue 3 2000Leon Shilton Do NCREIF returns influence commercial mortgage underwriters when they adjust capitalization rates? Are the ACLI capitalization series and the NCREIF return series cointegrated at the national and the smaller geographic sub-division levels? This research uses a two-step procedure to test for cointegration. First, the Phillips,Perron unit-root procedure must show that each series is a unit-root random walk. Previous research usually has assumed that these series are random walks, with the implication that the commercial mortgage market is efficient. Second, the Phillips,Ouliaris test of the residuals of a function of the two series determines the possibility of cointegration. At the national level and for the Northeast and Pacific regions the two series are random walks and cointegrated. In other geographic sub-divisions, neither or only one series is a random walk and therefore the data does not support a relationship. The lack of functional relationships in four of the six smaller geographic regions suggests that underwriters are not obtaining the NCREIF information or are ignoring it. The lack of random walks with the implication about capital-market efficiency invites further research. [source] Estimating Returns on Commercial Real Estate: A New Methodology Using Latent-Variable ModelsREAL ESTATE ECONOMICS, Issue 2 2000David C. Ling Despite their widespreao use as benchmarks of U.S. commercial real estate returns, indexes produced by the National Council of Real Estate Investment Fiduciaries (NCREIF) are subject to measurement problems that severely impair their ability to capture the true risk,return characteristics,especially volatility,of privately held commercial real estate. We utilize latent-variable statistical methods to estimate an alternative index of privately held (unsecuritized) commercial real estate returns. Latent-variable methods have been extensively applied in the behavioral sciences and, more recently, in finance and economics. Unlike factor analysis or other unconditional statistical approaches, latent variable models allow us to extract interpretable common information about unobserved private real estate returns using the information contained in various competing measures of returns that are measured with error. We find that our latent-variable real estate return series is approximately twice as volatile as the aggregate NCREIF total return index, but less than half as volatile as the NAREIT equity index. Overall, our results strongly support the use of latent-variable statistical models in the construction of return series for commercial real estate. [source] Rolling over stock index futures contractsTHE JOURNAL OF FUTURES MARKETS, Issue 7 2009Óscar Carchano Derivative contracts have a finite life limited by their maturity. The construction of continuous series, however, is crucial for academic and trading purposes. In this study, we analyze the relevance of the choice of the rollover date, defined as the point in time when we switch from the front contract series to the next one. We have used five different methodologies in order to construct five different return series of stock index futures contracts. The results show that, regardless of the criterion applied, there are not significant differences between the resultant series. Therefore, the least complex method can be used in order to reach the same conclusions. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 28:684,694, 2009 [source] Value at risk and conditional extreme value theory via markov regime switching modelsTHE JOURNAL OF FUTURES MARKETS, Issue 2 2008Yau Man Ze-to Samuel This study develops a new conditional extreme value theory-based (EVT) model that incorporates the Markov regime switching process to forecast extreme risks in the stock markets. The study combines the Markov switching ARCH (SWARCH) model (which uses different sets of parameters for various states to cope with the structural changes for measuring the time-varying volatility of the return distribution) with the EVT to model the tail distribution of the SWARCH processed residuals. The model is compared with unconditional EVT and conditional EVT-GARCH models to estimate the extreme losses in three leading stock indices: S&P 500 Index, Hang Seng Index and Hang Seng China Enterprise Index. The study found that the EVT-SWARCH model outperformed both the GARCH and SWARCH models in capturing the non-normality and in providing accurate value-at-risk forecasts in the in-sample and out-sample tests. The EVTSWARCH model, which exhibits the features of measuring the volatility of a heteroscedastic financial return series and coping with the non-normality owing to structural changes, can be an alternative measure of the tail risk. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:155,181, 2008 [source] A simplified approach to modeling the co-movement of asset returnsTHE JOURNAL OF FUTURES MARKETS, Issue 6 2007Richard D. F. Harris The authors propose a simplified multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model (the S-GARCH model), which involves the estimation of only univariate GARCH models, both for the individual return series and for the sum and difference of each pair of series. The covariance between each pair of return series is then imputed from these variance estimates. The proposed model is considerably easier to estimate than existing multivariate GARCH models and does not suffer from the convergence problems that characterize many of these models. Moreover, the model can be easily extended to include more complex dynamics or alternative forms of the GARCH specification. The S-GARCH model is used to estimate the minimum-variance hedge ratio for the FTSE (Financial Times and the London Stock Exchange) 100 Index portfolio, hedged using index futures, and compared to four of the most widely used multivariate GARCH models. Using both statistical and economic evaluation criteria, it was found that the S-GARCH model performs at least as well as the other models that were considered, and in some cases it was better. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:575,598, 2007 [source] An empirical analysis of the relationship between hedge ratio and hedging horizon using wavelet analysisTHE JOURNAL OF FUTURES MARKETS, Issue 2 2007Donald Lien In this article, optimal hedge ratios are estimated for different hedging horizons for 23 different futures contracts using wavelet analysis. The wavelet analysis is chosen to avoid the sample reduction problem faced by the conventional methods when applied to non-overlapping return series. Hedging performance comparisons between the wavelet hedge ratio and error-correction (EC) hedge ratio indicate that the latter performs better for more contracts for shorter hedging horizons. However, the performance of the wavelet hedge ratio improves with the increase in the length of the hedging horizon. This is true for both within-sample and out-of-sample cases. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:127,150, 2007 [source] On the Quantile Regression Based Tests for Asymmetry in Stock Return VolatilityASIAN ECONOMIC JOURNAL, Issue 2 2002Beum-Jo Park This paper attempts to examine whether the asymmetry of stock return volatility varies with the level of volatility. Thus, quantile regression based tests (,-tests) are presupposed. These tests differ from the diagnostic tests introduced by Engle and Ng (1993) insofar as they can provide a complete picture of asymmetries in volatility across quantiles of variance distribution and, in case of non-normal errors, they have improved power due to their robustness against non-normality. A small Monte Carlo evidence suggests that the Wald and likelihood ratio (LR) tests out of ,-tests are reasonable, showing that they outperform the Lagrange multiplier (LM) test based on least squares residuals when the innovations exhibit heavy tail. Using the normalized residuals obtained from AR(1)-GARCH(1, 1) estimation, the test results demonstrated that only the TOPIX out of six stock-return series had asymmetry in volatility at moderate level, while all stock return series except the FAZ and FA100 had more significant asymmetry in volatility at higher levels. Interestingly, it is clear from the empirical findings that, like hypothesis of leverage effects, volatility of the TOPIX, CAC40, and, MIB tends to respond significantly to extremely negative shock at high level, but is not correlated with any positive shock. These might be valuable findings that have not been seriously considered in past research, which has focussed only on mean level of volatility. [source] Les hedge funds ont-ils leur place dans un portefeuille institutionnel canadien?CANADIAN JOURNAL OF ADMINISTRATIVE SCIENCES, Issue 3 2003Stéphanie Desrosiers This article examines the return and risk of hedge funds (HF), and their correlations with traditional asset classes for the 1990,2002 period. Efficient frontiers resulting from optimizations with and without constraints demonstrate that it is worthwhile to include HF in a Canadian institutional investor's portfolio. HF offer a high potential return relative to risk, while weaker correlations with traditional asset classes create a beneficial diversification effect. Non-directional HF provide protection in bear markets and are more suitable for lower risk portfolios, whereas directional HF are better suited to higher risk portfolios. Caveats are necessary due to the skew-ness and kurtosis of the return distributions, potential biases in the return series, the lower liquidity, and the complexity of the HF industry. Résumé Cet article examine le rendement, le risque et les correélations des hedge funds (HF) avec les catégories d'actif traditionnelles sur la période 1990,2002. Des optimisations avec et sans contraintes montrent qu'il est avantageux d'inclure les HF dans un portefeuille institutionnel canadien du fait d'un potentiel de rendement élevé par rapport au risque encouru et de faibles corrélations. Les HF non-directionnels offrent une meilleure protection en marché baissier et sont plus appropriés pour des portefeuilles moins risqués. Les HF directionnels conviennent davantage aux portefeuilles prksentant un risque plus élevé. Des réserves doivent toutefois étre émises en raison des coefficients d'asymétrie et d'aplatissement de la distribution des rendements, des biais potentiels des données, de la faible liquidité, et de la complexité de l'industrie des HF. [source] CONSTANT PROPORTION PORTFOLIO INSURANCE IN THE PRESENCE OF JUMPS IN ASSET PRICESMATHEMATICAL FINANCE, Issue 3 2009Rama Cont Constant proportion portfolio insurance (CPPI) allows an investor to limit downside risk while retaining some upside potential by maintaining an exposure to risky assets equal to a constant multiple of the cushion, the difference between the current portfolio value and the guaranteed amount. Whereas in diffusion models with continuous trading, this strategy has no downside risk, in real markets this risk is nonnegligible and grows with the multiplier value. We study the behavior of CPPI strategies in models where the price of the underlying portfolio may experience downward jumps. Our framework leads to analytically tractable expressions for the probability of hitting the floor, the expected loss, and the distribution of losses. This allows to measure the gap risk but also leads to a criterion for adjusting the multiplier based on the investor's risk aversion. Finally, we study the problem of hedging the downside risk of a CPPI strategy using options. The results are applied to a jump-diffusion model with parameters estimated from returns series of various assets and indices. [source] Asset Pricing Information in Vintage REIT Returns: An Information Subset TestREAL ESTATE ECONOMICS, Issue 1 2005David H. Downs REIT return data prior to the new REIT era offer important asset pricing information. At issue is whether empiricists should focus attention on returns series covering only the new period. We use a generalized asset pricing and information subset test to disentangle REIT information from information available in several benchmark series. Results indicate that REIT returns are informative about the discounting process during the pre,new-era period. Thus, the distribution of vintage REIT returns is not fully explained by either broad market indexes or from size-based anomalies. This study should be viewed as a useful empirical precedent for those studying REIT data preceding the new REIT era. [source] | ||||||||||