Conditional higher order moments in metal asset returns

This study examines the role of higher order moments in the returns of four important metals, aluminium, copper, gold and silver, using the asymmetric GARCH (AGARCH) model with a conditional skewed generalized-t (SGT) distribution. Implications of higher order moments in metal returns are evaluated by comparing the performances of conditional value-at-risk measures obtained from the AGARCH models with SGT distributions to those obtained from the AGARCH models with normal and student-t distributions. With the exception of gold, the AGARCH model with the SGT distribution appears to have the best fit for all metals examined.     

On Valuing Participating Life Insurance Contracts with Conditional Heteroscedasticity

In this paper, we consider a novel approach for the fair valuation of a participating life insurance policy when the dynamics of the reference portfolio underlying the policy are governed by an Asymmetric Power GARCH (APGARCH) model with innovations having a general parametric distribution. The APGARCH model provides a flexible way to incorporate the effect of conditional heteroscedasticity or time-varying conditional volatility and nests a number of important symmetric or asymmetric ARCH-type models in the literature. It also provides a flexible way to capture both the memory effect of the conditional volatility and the asymmetric effects of past positive and negative returns on the current conditional volatility, called the leverage effect. The key valuation tool here is the conditional Esscher transform of Bühlmann et al. (1996, 1998). The conditional Esscher transform provides a convenient and flexible way for the fair valuation under different specifications of the conditional heteroscedastic models. We illustrate the practical implementation of the model using the S&P 500 index as a proxy for the reference portfolio. We also conduct sensitivity analysis of the fair value of the policy with respect to the parameters in the APGARCH model to document the impacts of different conditional volatility models nested in the APGARCH model and the leverage effect on the fair value. The results of the analysis reveal that the memory effect of the conditional volatility has more significant impact on the fair value of the policy than the leverage effect.     

Multiple Regimes and Volatility Transmission in Securitized Real Estate Markets

We examine the dynamics and transmission of conditional volatilities with multiple structural changes in return volatility using Bai and Perron (2003)’s methodology, across five major securitized real estate markets as well as employing a multivariate regime-dependent asymmetric dynamic covariance methodology (MRDADC) that allows the conditional matrix to be both time- and state-varying. Our results imply that a multiple-regime time varying asymmetric variance and covariance approach is important in modeling real estate securities valuation and selection and portfolio optimization, and is consistent with popular beliefs that market volatility changes over time. Our MRDADC models detect the presence of significant mean-volatility linkages across the five major securitized real estate markets under different volatility regimes and would have implications for global investor in terms of estimating a dynamic risk-minimizing hedge ratio in international portfolio management.     

Generalised Rational Bias in Financial Forecasts

Laster et al. (Q J Econ 114(1):293–318, 1999) built an economic model in which forecasters have incentives to generate forecasts that differ form the consensus. It is shown that the dispersion of the equilibrium distribution of forecasters, depends on the relative importance given on the intensive forecast users’ loss versus the publicity gain from occasional users. These results depend heavily on the assumption of symmetry for the loss and density functions. In this paper we examine the effects of generalising loss preferences and probability densities to allow for asymmetries through the LinEx loss and the Skewed Normal density, respectively. We derive the generalised equilibrium distribution of forecasts which contains the results of Laster et al. as a special case. The presence of asymmetric preferences is shown to cause a movement of the distribution away from the conditional mean, towards the optimal forecast under loss asymmetry. Furthermore, forecasts now tend to cluster around this quantity in an asymmetric way. These effects tend to be further strengthened or partially offset by the presence of skewness in the distribution of data, a result consistent with the conclusions of Christodoulakis (Finan Res Lett 2:227–233, 2005).The author is grateful to an anonymous referee for helpful comments that have improved the paper. The views expressed in the paper are those of the author and should in no part be attributed to the Bank of Greece.     

Asymmetric correlations of equity portfolios

Correlations between U.S. stocks and the aggregate U.S. market are much greater for downside moves, especially for extreme downside moves, than for upside moves. We develop a new statistic for measuring, comparing, and testing asymmetries in conditional correlations. Conditional on the downside, correlations in the data differ from the conditional correlations implied by a normal distribution by 11.6%. We find that conditional asymmetric correlations are fundamentally different from other measures of asymmetries, such as skewness and co-skewness. We find that small stocks, value stocks, and past loser stocks have more asymmetric movements. Controlling for size, we find that stocks with lower betas exhibit greater correlation asymmetries, and we find no relationship between leverage and correlation asymmetries. Correlation asymmetries in the data reject the null hypothesis of multivariate normal distributions at daily, weekly, and monthly frequencies. However, several empirical models with greater flexibility, particularly regime-switching models, perform better at capturing correlation asymmetries.     

Hedging effectiveness under conditions of asymmetry

We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail-specific metrics, for example, value at risk, to compare the hedging effectiveness of short and long hedgers. Comparisons are applied to a number of hedging strategies including OLS and both symmetric and asymmetric generalised autoregressive conditional heteroskedastic models. We apply our analysis to a dataset consisting of S&P500 index cash and futures containing symmetric and asymmetric return distributions chosen ex post. Our findings show that asymmetry reduces out-of-sample hedging performance and that significant differences occur in hedging performance between short and long hedgers.     

Option pricing when correlations are stochastic: an analytical framework

In this paper we develop a novel market model where asset variances–covariances evolve stochastically. In addition shocks on asset return dynamics are assumed to be linearly correlated with shocks driving the variance–covariance matrix. Analytical tractability is preserved since the model is linear-affine and the conditional characteristic function can be determined explicitly. Quite remarkably, the model provides prices for vanilla options consistent with observed smile and skew effects, while making it possible to detect and quantify the correlation risk in multiple-asset derivatives like basket options. In particular, it can reproduce and quantify the asymmetric conditional correlations observed on historical data for equity markets. As an illustrative example, we provide explicit pricing formulas for rainbow “Best-of” options.     

Conditional conservatism and cost of capital

We empirically test the association between conditional conservatism and cost of equity capital. Conditional conservatism imposes stronger verification requirements for the recognition of economic gains than economic losses, resulting in earnings that reflect losses faster than gains. This asymmetric reporting of gains and losses is predicted to lower firm cost of equity capital by increasing bad news reporting precision, thereby reducing information uncertainty (Guay and Verrecchia 2007) and the volatility of future stock prices (Suijs 2008). Using standard asset-pricing tests, we find a significant negative relation between conditional conservatism and excess average stock returns over the period 1975–2003. This evidence is corroborated by further tests on the association between conditional conservatism and measures of implied cost of capital derived from analysts’ forecasts.     

Which parametric model for conditional skewness?

This paper addresses an existing gap in the developing literature on conditional skewness. We develop a simple procedure to evaluate parametric conditional skewness models. This procedure is based on regressing the realized skewness measures on model-implied conditional skewness values. We find that an asymmetric generalized autoregressive conditional heteroscedasticity specification on shape parameters with a skewed generalized error distribution provides the best in-sample fit for the data, as well as reasonable predictions of the realized skewness measure. Our empirical findings imply significant asymmetry with respect to positive and negative news in both conditional asymmetry and kurtosis processes.     

Assessing the Risk of Liquidity Suppliers on the Basis of Excess Demand Intensities

In this article we introduce the concept of excess volume durations,which are defined as the time until a given amount of buy orsell excess volume is traded on the market. Excess volume durationsindicate the one-sided intensity of liquidity demand and characterizethe risk of a market maker with respect to asymmetric informationand inventory problems. By modeling excess volume durationsbased on Box–Cox-type autoregressive conditional duration(ACD) models, it is shown that market microstructure variablesare predictors for the expected liquidity demand intensity.Moreover, the length of excess volume durations is found tobe positively correlated with the magnitude of the correspondingprice impact and thus the market depth.     

Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions

Financial returns typically display heavy tails and some degree of skewness, and conditional variance models with these features often outperform more limited models. The difference in performance may be especially important in estimating quantities that depend on tail features, including risk measures such as the expected shortfall. Here, using recent generalizations of the asymmetric Student-t and exponential power distributions to allow separate parameters to control skewness and the thickness of each tail, we fit daily financial return volatility and forecast expected shortfall for the S&P 500 index and a number of individual company stocks; the generalized distributions are used for the standardized innovations in a nonlinear, asymmetric GARCH-type model. The results provide evidence for the usefulness of the general distributions in improving fit and prediction of downside market risk of financial assets. Constrained versions, corresponding with distributions used in the previous literature, are also estimated in order to provide a comparison of the performance of different conditional distributions.     

Announcement Effects and Asymmetric Volatility in Industry Stock Returns: Evidence from Taiwan

This study employs financial econometric models to examine the asymmetric volatility of equity returns in response to monetary policy announcements in the Taiwanese stock market. The meetings of the board of directors at the Central Bank of the Republic of China (Taiwan) are considered for testing the announcement effects. The asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) model and the smooth transition autoregression with GARCH model are used to measure equity returns' asymmetric volatility. We conclude that the asymmetric volatility of countercyclical equity returns can be identified. Our findings support the leverage effect of stock price changes for most industry equity returns in Taiwan.     

On optimal portfolio diversification with respect to extreme risks

Extreme losses of portfolios with heavy-tailed components are studied in the framework of multivariate regular variation. Asymptotic distributions of extreme portfolio losses are characterized by a functional γ _ξ =γ _ξ (α,Ψ) of the tail index α, the spectral measure Ψ, and the vector ξ of portfolio weights. Existence, uniqueness, and location of the optimal portfolio are analysed and applied to the minimization of risk measures. It is shown that diversification effects are positive for α>1 and negative for α<1. Strong consistency and asymptotic normality are established for a semiparametric estimator of the mapping ξ ↦ γ _ξ . Strong consistency is also established for the estimated optimal portfolio.     

Evaluating the Forecast Accuracy of Emerging Market Stock Returns

This paper analyzes the forecast performance of emerging market stock returns using standard autoregressive moving average (ARMA) and more elaborated autoregressive conditional heteroskedasticity (ARCH) models. Our results indicate that the ARMA and ARCH specifications generally outperform random walk models. Models that allow for asymmetric shocks to volatility are better for in-sample estimation (threshold autoregressive conditional heteroskedasticity for daily returns and exponential generalized autoregressive conditional heteroskedasticity for longer periods), and ARMA models are better for out-of-sample forecasts. The results are valid using both U. S. dollar and domestic currencies. Overall, the forecast errors of each Latin American market can be explained by the forecasts of other Latin American markets and Asian markets. The forecast errors of each Asian market can be explained by the forecasts of other Asian markets, but not by Latin American markets. Our predictability results are economically significant and may be useful for portfolio managers to enter or leave the market.     

Valuación de opciones de tipo de cambio asumiendo distribuciones α-estables

This paper aims to present the valuation of options using the Black-Scholes method assuming α-stable distributions as an alternative option valuation in the Mexican market. The use of α-stable distributions for modelling financial series allows to overcome the classical valuation main weakness which assumes normality, by capturing the presence of heavy tails and asymmetry in financial time series. One of the main results is the price differential between the two models and the effect of alpha and beta parameters on prices; to show the difference valuation is made of a call option and a put option for the peso-dollar exchange rate. Likewise, basic sensitivity measurements of options (delta, gamma, and rho) were made and the effect of the stability parameter (α) was made on the implied volatility of options assuming the α-stable price as the market price.     

Backtesting extreme value theory models of expected shortfall

We use stock market data to analyze the quality of alternative models and procedures for forecasting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory (EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day horizon we combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES forecasts.     

Autoregressive Conditional Kurtosis

This article proposes a new model for autoregressive conditionalheteroscedasticity and kurtosis. Via a time-varying degreesof freedom parameter, the conditional variance and conditionalkurtosis are permitted to evolve separately. The model usesonly the standard Student’s t-density and consequentlycan be estimated simply using maximum likelihood. The methodis applied to a set of four daily financial asset return seriescomprising U.S. and U.K. stocks and bonds, and significant evidencein favor of the presence of autoregressive conditional kurtosisis observed. Various extensions to the basic model are proposed,and we show that the response of kurtosis to good and bad newsis not significantly asymmetric.     

Asymmetries of the intraday return-volatility relation

This study investigates the asymmetry of the intraday return-volatility relation at different return horizons ranging from 1, 5, 10, 15, up to 60 min and compares the empirical results with results for the daily return horizon. Using data on the S&P 500 (SPX) and the VIX from September 25, 2003 to December 30, 2011 and a Quantile-Regression approach, we observe strong negative return-volatility relation over all return horizons. However, this negative relation is asymmetric in three different aspects. First, the effects of positive and negative returns on volatility are different and more pronounced for negative returns. Second, for both positive and negative returns, the effect is conditional on the distribution of volatility changes. The absolute effect is up to five times larger in the extreme tails of the distribution. Third, at the intraday level, there is evidence of both autocorrelation in volatility changes and cross-autocorrelation with returns. This lead-lag relation with returns is also very asymmetric and more pronounced in the tails of the distribution. These effects are, however, not observed at the daily return horizon.     

Hourly index return autocorrelation and conditional volatility in an EAR–GJR-GARCH model with generalized error distribution

We study the autocorrelation and conditional volatility of the hourly Dow Jones Industrial Index return data from October 1974 to September 2002 using an exponential asymmetric AR–GARCH specification with a generalized error distribution. Our findings document a positive autocorrelation in hourly return data in the early years of the sampling period, but the autocorrelation turns negative after 1986 and the negative shock causes more impact on the conditional volatility. This latter period evidence stands in contrast to prior findings employing lower frequency and/or earlier year data. In addition, our results present some evidence of a negative relation between autocorrelation and conditional volatility before 1986 (albeit weaker than prior findings), but this negative relationship disappears after 1986.