Realized volatility transmission: The role of jumps and leverage effects

This paper is the first to employ a multivariate extension of the LHAR–CJ model for realized volatility of Corsi and Renó (2012) considering continuous and jump volatility components and leverage effects. The model is applied to financial (S&P 500), commodity (WTI crude oil) and forex (US$/EUR) intraday futures data and allows new insights in the transmission mechanisms among these markets. Besides significant leverage effects, we find that the jump components of all considered assets do not contain incremental information for the one-step ahead realized volatility. The volatility of S&P 500 and US$/EUR exchange rate futures exhibits significant spillovers to the realized volatility of WTI. Moreover, decreasing equity prices appear to increase volatility in other markets, while strengthening of the US$ seems to calm down the crude oil market.     

Testing volatility persistence on Markov switching stochastic volatility models

In the literature, some researchers found that the high persistence of the volatility can be caused by Markov regime switching. This concern can be reflected as a unit root problem on the basis of Markov switching models. In this paper, our main purpose is to provide a Bayesian unit root testing approach for Markov switching stochastic volatility (MSSV) models. We illustrate the developed approach using S&P 500 daily return covering the subprime crisis started in 2008.     

Extracting portfolio management strategies from volatility transmission models in regime-changing environments: Evidence from GCC and global markets

Unlike previous studies, this paper uses the Multi-Chain Markov Switching model (MCMS) to examine portfolio management strategies based on volatility transmission between six domestic stock markets of Gulf Arab states (GCC) and global markets (i.e., the U.S. S&P 500 index and oil prices) and compares the results with those of the VAR model. Our volatility approach is range-based and not return-based which is traditionally used in estimating the optimal hedge ratios and portfolio weights. The results demonstrate the relative hedging effectiveness of the MCMS model compared to the VAR. We also highlight the time and regime dependency of the optimal hedge ratios and the portfolio weights for each selected pair of the considered markets conditional on the regime of the same market and the regimes of the other market. Policy implications on portfolio strategies under different states are also discussed.     

Modeling volatility with time-varying FIGARCH models

This paper puts the light on a new class of time-varying FIGARCH or TV-FIGARCH processes to model the volatility. This new model has the feature to account for the long memory and the structural change in the conditional variance process. The structural change is modeled by a logistic function allowing the intercept to vary over time. We also implement a modeling strategy for our TV-FIGARCH specification whose performance is examined by a Monte Carlo study. An empirical application to the crude oil price and the S&P 500 index is carried out to illustrate the usefulness of our techniques. The main result of this paper is that the long memory behavior of the absolute returns is not only explained by the existence of the long memory in the volatility but also by deterministic changes in the unconditional variance.     

Nonparametric estimation of stochastic volatility models

This letter introduces nonparametric estimators of the drift and diffusion coefficient of stochastic volatility models which exploit techniques for estimating integrated volatility with high-frequency data. The performance of the proposed estimators is assessed on simulations of two popular stochastic volatility models.     

Worker heterogeneity and labor market volatility in matching models

Shimer demonstrated that aggregate productivity shocks in a standard matching model cause fluctuations in key labor market statistics—such as the job-finding rate, the vacancy/unemployment ratio, and the unemployment rate—that are too small by an order of magnitude [Shimer, R., 2005. The cyclical behavior of equilibrium unemployment and vacancies. American Economic Review 95 (1) 25–49]. This paper shows that when the standard model is extended to allow for worker heterogeneity, it exhibits considerably greater volatility. In the model, marginal workers, whose productivity only slightly exceeds the value of their alternative use of time, constitute a disproportionate share of unemployment on average, and that share rises when aggregate conditions deteriorate. These composition effects cause firms to open fewer vacancies during downturns.     

Computing DSGE models with recursive preferences and stochastic volatility

This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin, 1989, Epstein and Zin, 1991 and stochastic volatility. Models with these two features have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.     

Estimating general equilibrium models with stochastic volatility and changing parameters

This paper explores the importance of specification in estimated general equilibrium models with changing monetary policy parameters and stochastic volatility. Simulated data is used to estimate models with incorrectly specified exogenous shocks (time-varying vs. constant variance) and models misspecifying the way Taylor rule parameters change over time (constant vs. drifting vs. regime-switching). The model correctly identifies some changes in monetary policy parameters, even when misspecified. The inclusion of stochastic volatility greatly improves model fit even when the data is generated using constant variance exogenous shocks; this relationship is stronger in data generated from models with changing policy parameters.     

Predictive ability of asymmetric volatility models at medium-term horizons

Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.     

High-low range in GARCH models of stock return volatility

We suggest a simple and general way to improve the GARCH volatility models using the intraday range between the highest and the lowest price to proxy volatility. We illustrate the method by modifying a GARCH(1,1) model to a range-GARCH(1,1) model. Our empirical analysis conducted on stocks, stock indices and simulated data shows that the range-GARCH(1,1) model performs significantly better than the standard GARCH(1,1) model both in terms of in-sample fit and out-of-sample forecasting ability.     

Regime-switching in volatility and correlation structure using range-based models with Markov-switching

This study examines latent shifts in the conditional volatility and correlation for the U.S. stock and T-bond data using the two-state Markov-switching range-based volatility and correlation models. This paper comes up with clear evidence of volatility regime-switching in stock indices and T-bond over the crisis period. As regards the process of correlation, we also find evidence of regime changes in correlations between stock indices and T-bond over several financial crises. We conclude that the phenomena of both volatility and correlation regime-switching are triggered by these financial crises. In addition, the range-based volatility and correlation model with regime-switching method could explicitly point out the true date of structure changes in the data generating process for volatility and correlation variables.     

Volatility forecasting: combinations of realized volatility measures and forecasting models

This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.     

Forecasting REIT volatility with high-frequency data: a comparison of alternative methods

Volatility is a crucial input for many financial applications, including asset allocation, risk management and option pricing. Over the last two decades the use of high-frequency data has greatly advanced the research on volatility modelling. This article makes the first attempt in the real estate literature to employ intraday data for volatility forecasting. We examine a wide range of commonly used methods and apply them to several major global REIT markets. Our findings suggest that the group of reduced form methods deliver the most accurate one-step-ahead forecast for daily REIT volatility. They outperform their GARCH-model-based counterparts and two methods using low-frequency data. We also show that exploiting intraday information through GARCH does not necessarily yield incremental precision for forecasting REIT volatility. Our results are relatively robust to the choice of realized measure of volatility and the length of evaluation period.     

Estimation and testing of alternative production function models

The paper deals with four different decision models for a firm operating with a constant elasticity of substitution production function. The assumptions underlying the different models and their consequences for estimation are carefully specified. Maximum likelihood estimates of the parameters are obtained of the full four-equation models, using data pertaining to the Dutch manufacturing sector. Finally likelihood ratio test procedures are developed in order to determine whether one or more of the decision models can be rejected on the basis of this data.     

Forecasting volatility in the Indian equity market using return and range-based models

In this article, we assess the time-varying volatility of the National Stock Exchange in the Indian equity market using unconditional estimators and asymmetric conditional econometric models. The volatility estimate and forecast is computed from the interday return and intraday range-based data of the exchange’s flagship index, CNX NIFTY, for the time period spanning 1 January 2009 through 31 December 2013. These are our findings: First, we determine that the time-varying volatility of the index is asymmetric with qualities of stationarity and leptokurtic distribution. Second, the one-step-ahead volatility forecast derived from the univariate time series parameters through the GJR-GARCH ?????process indicates that the model evaluation criteria of the autoregressive process tends towards range-based models vis-à-vis a return-based model. The validity of this methodology is further analysed with the superior predictive ability test, the outcome of which supports the use of range-based conditional models. Finally, among the evaluated range-based model variants, the model confidence set procedure favours the Yang–Zhang estimator as being better suited to forecast the exchange’s volatility than the ones by Parkinson, Garman–Klass and Rogers–Satchell.     

An alternative approach to simulating var models

This paper proposes a simple approach to the problem of handling contemporaneous correlation of the error terms when simulating VAR models. The approach is illustrated with an example using an estimated VAR model of the New Zealand economy.     

Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.     

Discrimination among alternative models of international capital markets

Most empirical capital flow studies have estimated individual models, many by assuming exogenous interest rates. None of these studies has examined interest rate interdependence from the perspective of the models. In this study, a simultaneous equation model of capital flows containing previous models as special cases is estimated using the cross spectral technique of model discrimination. Based on examination of both the flow-differential link and interest rate interdependence resulting from capital flows, the evidence 1) supports a simultaneous equation stock adjustment model, and 2) suggests that interest rate endogeneity may not imply serious simultaneous equation bias for those previous studies which assume exogenous interest rates.     

Workplace risks and wages: Canadian evidence from alternative models

Three alternative models of compensating wage premiums for risk are estimated: the conventional OLS wage regression; an endogenous risk model that accounts for the simultaneity that may occur if workers of high potential earnings prefer safer jobs; and a self-selection model to account for the possibility that workers sort into jobs based on unobserved tolerance for risk that affects their productivity in dangerous work environments. The results suggest that the existing Canadian estimates, which have been based on the basic model, may seriously underestimate the wage premium for risk and hence the implied cost of fatal and non-fatal injuries. JEL Classification: J28, J31
Risques au travail et salaires: résultats canadiens pour plusieurs modèles. Les auteurs calibrent trois modèles pour évaluer la compensation salariale pour les risques au travail: l'équation conventionnelle de régression des salaires estimée par la méthode des moindres carrés ordinaires; un modèle de risque endogène qui tient compte de la simultanéité qui peut se produire si les travailleurs dont les revenus potentiels sont élevés préfèrent les emplois où il y a moins de risques; un modèle d'auto-sélection où les travailleurs se répartissent entre les emplois sur la base d'une tolérance non-observée pour le risque qui affecte leur productivité dans des environnements de travail dangereux. Les résultats suggèrent que les évaluations canadiennes en vogue, qui sont fondées sur le modèle de base, peuvent sous-estimer sérieusement la prime salariale de risque et donc les coûts des blessures mortelles ou non que les accidents entraînent.