Heejoon Han

Sungkyunkwan University | SKKU · Department of Economics

This article investigates the estimation and inference of quantile impulse response functions. We propose a new estimation method using the idea of local projections by Jordà (2005). We establish consistency and asymptotic normality of the estimator, thereby enabling asymptotic inference. We also consider the confidence interval construction based...

This paper investigates the tail behavior of safe haven currencies using high-frequency data during both financially good and bad times over the period 2004–2017. The analysis uses the cross-quantilogram, recently developed by Han et al. (2016), to measure quantile dependence between currencies and foreign exchange (FX) volatility, and equity and b...

This paper nonparametrically estimates the distribution of world citizens’ income and investigates world income inequality for the period from 1970 to 2010. We consider 188 countries that account for 98.68% of the world population and almost 100% of the world GDP in the year 2010. Various income inequality indices such as the Gini coefficient revea...

This paper investigates the profitability of carry trades by taking into account the endogeneity of regime switching between low and high states of exchange rate volatility. The analysis uses an endogenous regime switching model with an autoregressive latent factor, in which the future transition between states depends on the current state as well...

This paper examines quantile dependence and directional predictability between the foreign exchange market and the stock market in Korea. Instead of adopting a multivariate model such as a vector autoregressive model, a multivariate GARCH model or a combination of both models, we apply the cross-quantilogram recently proposed by Han et al. (2016)....

This paper introduces and analyzes a new model for realized volatility that accommodates endogenous regime switching. The model is based on the heterogeneous autoregressive model and allows for two-state regime switching. Importantly, a current shock to the realized volatility affects the regime switching in the next period. We apply the model to t...

This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, which is a correlation statist...

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross-quantilogram and the corresponding test statistic. The limiting distributions d...

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross-quantilogram and the corresponding test statistic. The limiting distributions d...

To forecast realized volatility, this paper introduces a multiplicative error model that incorporates heterogeneous components: weekly and monthly realized volatility measures. While the model captures the long-memory property, estimation simply proceeds using quasi-maximum likelihood estimation. This paper investigates its forecasting ability usin...

This paper analyzes the effect of omitting a persistent covariate in the GARCH-X model. In particular, we show that if the relevant persistent covariate is omitted and the usual GARCH(1,1) model is fitted, the model will be estimated approximately as an IGARCH model. This may well explain the ubiquitous evidence of the IGARCH in empirical volatilit...

This article investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLE’s) of the GARCH model augmented by including an additional explanatory variable—the so-called GARCH-X model. The additional covariate is allowed to exhibit any degree of persistence as captured by its long-memory parameter dx; in particular...

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross quantilogram and the corresponding test statistic. The limiting distributions d...

This paper compares the information content of realized measures constructed from high-frequency data and implied volatilities from options in the context of forecasting volatility. The comparison is based on within-sample and out-of-sample (over horizons of 1–22 days) forecasts of daily S&P 500 index return volatility. The paper adds to the findin...

This article considers a GARCH process, generally named as GARCH-X, in which the additional covariate is specified as a positive fractionally integrated process. Recent work on MEM, HEAVY, and Realized GARCH models falls in this category. We investigate the asymptotic properties of this process and show how it explains stylized facts of financial t...

We investigate a new non-stationary non-parametric volatility model, in which the conditional variance of time series is modelled as a non-parametric function of an integrated or near-integrated covariate. Importantly, the model can generate the long memory property in volatility and allow the unconditional variance of time series to be time-varyin...

We consider a model called GARCH-NNH, which is a GARCH(1,1) process with a nonlinear function of a persistent, integrated or nearly integrated, variable. We derive the asymptotic distribution theory of the quasi-maximum likelihood estimator in the GARCH-NNH model. We establish the consistency and asymptotic mixed normality of the quasi-maximum like...

The paper considers a volatility model which introduces a persistent, integrated or near-integrated, covariate to the standard GARCH(1, 1) model. For such a model, we derive the asymptotic theory of the quasi-maximum likelihood estimator. In particular, we establish consistency and obtain limit distribution. The limit distribution is generally non-...

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a m...

The paper considers a volatility model which introduces a persistent, integrated or nearly integrated, covariate to the standard ARCH(1) model. For such a model, we derive asymptotic theory of quasi-maximum likelihood estimator. In particular, we establish consistency and obtain limit distribution. The limit distribution is generally non-Gaussian a...

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a m...

We consider the ARCH models with nonstationary covariates and provide theories how nonstationary covariates affect volatility persistence and leptokurtosis. In particular, we propose and study a volatility model that set the conditional heteroskedasticity as the lag of the squared underlying process combined with a nonlinear function of a nonstatio...