Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information

Most asset returns exhibit high volatility and its persistence. Heuristically, this paper focuses on the role of surprising information in high volatility processes and indicates that dismissing surprising information may lead to considerable loss in forecast accuracy. In response, this paper considers the corresponding extension of the modified MDH to surprising information, and proposes a bivariate stochastic volatility model incorporating surprising information in the volatility equations (BSV-SI), which is also designed to capture the dynamics of returns and trading volume. Using the South Korea stock index and trading volume series, it turns out that performance of the onestep- ahead forecasts of the BSV-SI model is apparently superior to those of other competitive models.Volatility forecasting, Bivariate stochastic volatility model with surprising information, Modified mixture of distribution hypothesis, Realized volatility models, Markov Chain Monte Carlo (MCMC)

A Dynamic Measure of Intentional Herd Behavior in Financial Markets

This paper suggests a dynamic measure of intentional herding, causing the excess volatility or even systemic risk in financial markets, which is based on a new concept of cumulative returns in the same direction as well as the collective behavior of all investors towards the market consensus. Differing from existing measures, the measure allows us to directly detect time-varying and market-wide intentional herding using the model of Dynamic Conditional Correlation (DCC) (Engle, 2002) between the financial market and its components that is partially free of spurious herding due to the inclusion of the variables of the number of economic news announcements as a proxy of market information. Strong evidence in favor of the dynamic measure over the other measures is based on empirical application in the U.S. markets (DJIA and S&P100), supporting the tendency to exhibit time-varying intentional herding. Much more important is a finding that the impact of intentional herding on market volatility tends to be stronger during the periods of turbulent markets like the degradation of U.S. sovereign credit rating by S&P, and be more significant in S&P 100 than DJIA

A Dynamic Measure of Intentional Herd Behavior in Financial Markets

This paper suggests a dynamic measure of intentional herding, causing the excess volatility or even systemic risk in financial markets, which is based on a new concept of cumulative returns in the same direction as well as the collective behavior of all investors towards the market consensus. Differing from existing measures, the measure allows us to directly detect time-varying and market-wide intentional herding using the model of Dynamic Conditional Correlation (DCC) (Engle, 2002) between the financial market and its components that is partially free of spurious herding due to the inclusion of the variables of the number of economic news announcements as a proxy of market information. Strong evidence in favor of the dynamic measure over the other measures is based on empirical application in the U.S. markets (DJIA and S&P100), supporting the tendency to exhibit time-varying intentional herding. Much more important is a finding that the impact of intentional herding on market volatility tends to be stronger during the periods of turbulent markets like the degradation of U.S. sovereign credit rating by S&P, and be more significant in S&P 100 than DJIA

Asymmetric herding as a source of asymmetric return volatility

As a considerable source of asymmetry in return volatility, this paper introduces asymmetric herding and extends the continuous beliefs system to account for its asymmetry and derive the asymmetric herding parameters that are easily estimated by using a maximum likelihood method based on the GARCH-type econometric model. This paper presents new empirical evidence for asymmetry in the exchange rates volatility of major currencies against the US dollar, which have bilateral nature. Interestingly, the asymmetry of Japanese yen is the opposite of that of others and the global financial crisis highlights the opposite asymmetry. Some of traditional hypotheses, such as the leverage effect and the volatility feedback effect, do not adequately explain these findings; however, a significant asymmetric herding effect is observed and appears to be time-varying. Further, the clear link between asymmetric herding and volatility strongly supports the hypothesis of the asymmetric herding effect.Asymmetry in return volatility Continuous beliefs system Asymmetric herding parameters Foreign exchange markets

Asymmetric Volatility of Exchange Rate Returns Under The EMS: Some Evidence From Quantile Regression Approach for Tgarch Models

This paper investigates the systematic impact of the European Monetary System EMS) on asymmetry in volatility of exchange rates vis-a-vis the Deutsche Mark. It seems plausible that the symmetric fluctuation band in the EMS affects asymmetric volatility and this id dominant at extreme returns. To examine the plausibility, this paper proposes quantile regression for threshold GARCH models (QRTGARCH), which allows an asymmetric reaction of conditional volatility to shocks without any rigid distributional assumptions. Further, it is well suited to precisely capture the asymmetric behaviors of conditional volatility over different levels of returns. The empirical finding suggests that the EMS seems to have some systematic effect on the asymmetry in volatility at moderate level of unpredictable returns. Especially, the estimation results of the QRTGARCH show that after the EMS conditional volatility for most of EMS currencies tends to grow more significantly in reaction to positive shock than negative shock at 0.1 quantile of returns distribution, so that as the unpredictable returns go down, the systematic effect of the EMS on asymmetry in volatility becomes more significant. Impressive as these results may be, the systematic effect can vary with levels of unpredictable returns. [F31, C22, or C51]

Quantile regression and the duration of unemployment

Powell (1986) proposed a quantile regression estimator for censored regression models on the basis of equivariance of quantiles to monotone transformations. In this thesis, censored quantile regression models are generalized using two-parameter Box-Cox transformation to relax the conventional linear specification of functional form, and the quantile regression estimator of the parameters of the transformed and censored regression models is presented. Both the N\sp{1/2}-consistency and the asymptotic normality of quantile estimator are derived for nonlinear regression models. The proof of asymptotic normality is based on the approach introduced by Pollard (1989) using maximal inequalities and quadratic approximation to the objective function, thus simplifying the argument and relaxing the need for convexity of the objective function in the parameter vector.From a practical point of view, this thesis proposes a new simple computational technique for nonlinear quantile regression estimation including Powell's piecewise linear censored regression quantile estimation. This algorithm is an extension of Meketon's (1985) idea for l\sb1 estimation of linear models using Karmarkar's interior point approach. It has remarkable advantages. It is computationally simple because the core of the algorithm is a standard least-squares computation. Numerical experience with a wide variety of test problems from the literature has shown it to perform quite satisfactorily.The final chapter of the thesis is devoted to applying the transformed and censored regression quantile model to the study of the duration of unemployment. The model is estimated using data from the Michigan Panel Study of Income Dynamics and Efron's (1979) bootstrap technique is taken to estimate the asymptotic covariance matrix. This empirical study clarifies the effect of education on unemployment duration particularly at the upper quantiles of the duration distribution. This is a new finding which has not been considered in past research, and is indicative of the value of the quantile regression approach.U of I OnlyETDs are only available to UIUC Users without author permissio

Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models

While most asset pricing models postulate a positive relationship between excess returns and risk, there is no consensus on the nature of the relationship due to conflicting empirical evidence. The relationship is particularly ambiguous within a GARCH-M framework. This paper demonstrates that such a conflict can be attributed primarily to the downward bias of standard estimators that neglect additive outliers (AO) commonly observed in financial returns, and proposes a feasible estimation method (RGMME) for the GARCH-M model based upon a robust variant of the GMM. Monte Carlo experiments demonstrate that AOs cause more serious bias in the ML and GMM estimates of the relationship coefficient than previously expected. Therefore, in the presence of AOs, the RGMME appears superior to other standard estimators in terms of the root mean square error criterion. There is strong evidence favouring the RGMME over standard estimators based on its empirical application. In particular, it is substantially evident from the results of the RGMME that there is support for a positive relationship between excess returns and conditional volatility for all three major equity markets.Risk-return relationship, Risk premium, Robust GMM estimation, GARCH-M model, Additive outliers, Finite-sample bias,

Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework

International audienc

Chatter Detection in Hot Strip Mill Process based on Modified Independent Component Analysis

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