Volatility Forecasting: Downside Risk, Jumps and Leverage Effect

This study investigates the long-run and short-run movements of two emerging stock market volatilities using a volatility decomposition methodology. We studied the impact of 2007–2008 subprime mortgage crisis on the transitory and permanent volatility components in terms of two empirical stylized facts, the leverage effect and volatility persistence. In order to do so, the long spanning data are separated into three different periods. For the former stylized fact, the crisis impact on the leverage effect is mainly temporary with no long-run effect to the stock markets. This finding explains that the leverage effect is mostly difficult to adjust in the short-run transitory volatility during the crisis periods. However with proper risk management and long term strategies, most of the market participants are able to anticipate and handle this news impact in the long-run. For the latter stylized fact, the crisis has slightly increased the volatility persistence in all the markets. From the viewpoint of heterogeneous market hypothesis, the higher intensity of volatility persistence implies the stock markets are less informational efficient.

In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.

Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News

Return behaviour in Africa's emerging equity markets

The paper examines equilibrium models based on Epstein–Zin preferences in a framework in which exogenous state variables follow affine jump diffusion processes. A main insight is that the equilibrium asset prices can be computed using a standard machinery of affine asset pricing theory by imposing parametric restrictions on market prices of risk, determined inside the model by preference and model parameters. An appealing characteristic of the general equilibrium setup is that the state variables have an intuitive and testable interpretation as driving the consumption and dividend dynamics. We present a detailed example where large shocks (jumps) in consumption volatility translate into negative jumps in equilibrium prices of the assets as agents demand a higher premium to compensate for higher risks. This endogenous “leverage effect,” which is purely an equilibrium outcome in the economy, leads to significant premiums for out‐of‐the‐money put options. Our model is thus able to produce an equilibrium “volatility smirk,” which realistically mimics that observed for index options.

This study employs the daily data of the Stock Exchange of Thailand to test for the leverage and volatility feedback effects. The period of investigation is during January 4, 2005 to December 27, 2013, which includes the Subprime crisis period in the US that might affect the volatility of stock market return in emerging stock markets. The results from this study show that the US subprime crisis imposes a minimal positive impact on volatility. In addition, the estimations of the three parametric asymmetric volatility models give the results showing some evidence of the volatility feedback and leverage effects. The findings give implications for portfolio diversification and risk management.

Occupation density estimation for noisy high-frequency data

This paper proposes a multivariate VAR-BEKK-GJR-GARCH volatility model to assess the dynamic interdependence among stock, bond and money market returns and volatility of returns. The proposed model allows for market interaction which provides useful information for pricing securities, measuring value-at-risk (VaR), and asset allocation and diversification, assisting financial regulators for policy implementation. The model is estimated by the maximum likelihood method with Student-t innovation density. The asymptotic chi-square tests for volatility spillovers and leverage effects are constructed and provide predictions of volatility and time-varying correlations of returns. Application of the proposed model to the Australia’s domestic stock, bond, and money markets reveals that the domestic financial markets are interdependent and volatility is predictable. In general, volatility spillovers from stock market to bond and to money markets due to common news. The empirical findings of this paper quantify the association among the security markets which can be utilized for improving agents’ decision-making strategies for risk management, portfolio selection and diversification.

Chasing volatility: A persistent multiplicative error model with jumps

Stochastic Volatility and Jumps: Exponentially Affine Yes or No? An Empirical Analysis of S&P500 Dynamics

This paper uses ARCH models to examine if there is a leverage effect and also to test if A- and B-share holdings have different risks in Chinese stock markets before and after B-share markets open to domestic investors in February 2001. The empirical results suggest that leverage effect was not present and shocks have symmetric impact on the volatility of Chinese B-share stock returns in both periods and A-share returns in Period I. Thus GARCH model would be a better model to fit the Chinese B-share stock returns than EGARCH or GJR-GARCH model. But EGARCH or GJR-GARCH model fits recent (Period II) A-share markets data better than GARCH model. Another finding of this paper is that holding A- or B-share bears different risk in returns in the two Chinese markets. Furthermore, news or shocks have a larger impact on volatility of B-share returns in Period I than in Period II.

Aggregate stock market behavior and investors’ low risk aversion

We consider three sets of phenomena that feature prominently -and separately -in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence;

This paper investigates the daily exchange rate volatility of the Swedish krona (SEK) against the USD, EUR, GBP, and NOK over the period 2010–2023. Using asymmetric power ARCH (APARCH) models, the analysis uncovers significant differences in volatility dynamics across currency pairs and subperiods. A negative asymmetric return–volatility relationship is identified for SEK/EUR, indicating stronger reactions to negative shocks, while an inverted asymmetry is found for SEK/NOK—a pattern rarely documented in prior studies. No significant asymmetry is detected for SEK/USD or SEK/GBP. To address the limitations of conventional parametric models in small, open economies, a distribution-free support vector regression (SVR) approach with a wavelet kernel is integrated within the APARCH framework. In this study, the SVR-APARCH model demonstrates superior forecasting performance compared with standard APARCH models estimated via maximum likelihood estimation. Furthermore, it is shown to be generally more suitable for volatility forecasting than the hybrid ANN-APARCH and boosting-APARCH models. The results underscore the model’s enhanced capacity to capture the complex and nonlinear features of exchange rate volatility. The study contributes to the literature by providing robust evidence of asymmetric volatility patterns in SEK exchange rates and by introducing an effective hybrid modeling approach for improved volatility forecasting.

This paper discusses four GARCH-type models (A-GARCH, NA-GARCH, GJR-GARCH, and E-GARCH) in representing volatility of financial returns with leverage effect. In these models, errors are assumed to follow a Laplace distribution in order to deal with the typical leptokurtic feature of financial returns. The properties of these models are analyzed theoretically in terms of unconditional variance, kurtosis, autocorrelation function and news impact, and are further examined in the applications to real financial time series. Comparison is made with other choices of error distributions such as normal, Student-5, and Student-7 distributions, respectively. We also conduct residual analyses to justify the choice of error distributions and find that Laplace-E-GARCH model still performs very well. Our main purpose is to study and compare the proposed models’ relative adequacies and underlying limitations.