A new generalized volatility proxy via the stochastic volatility model

This article proposes power transformation of absolute returns as a new proxy of latent volatility in the stochastic model. We generalize absolute returns as a proxy for volatility in that we place no restriction on the power of absolute returns. An empirical investigation on the bias, mean square error and relative bias is carried out for the proposed proxy. Simulation results show that the new estimator exhibiting negligible bias appears to be more efficient than the unbiased estimator with high variance.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

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.     

Semi-Parametric Comparison of Stochastic Volatility Models using Realized Measures

This paper proposes a procedure to test for the correct specification of the functional form of the volatility process within the class of eigenfunction stochastic volatility models. The procedure is based on the comparison of the moments of realized volatility measures with the corresponding ones of integrated volatility implied by the model under the null hypothesis.
We first provide primitive conditions on the measurement error associated with the realized measure, which allow to construct asymptotically valid specification tests.
Then we establish regularity conditions under which the considered realized measures, namely, realized volatility, bipower variation, and modified subsampled realized volatility, satisfy the given primitive assumptions.
Finally, we provide an empirical illustration based on three stocks from the Dow Jones Industrial Average.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

Shall One Sit “Longer” for a Free Lunch? Impact of Trading Durations on the Realized Variances and Volatility Spillovers

This study examines the effect of trading durations on the realized variance of rupee futures traded in national stock exchange (NSE), India and Dubai Gold & Commodities Exchange (DGCX), Dubai as there exists a difference in the trading durations at these exchanges, where DGCX has longer trading duration. The empirical results suggest that longer trading duration has significantly higher realized variance, and also non-trading durations at NSE account for higher overall realized variance of Rupee Futures. We model the impact of trading durations on intraday and overnight realized variance for rupee futures and estimate a reduced realized volatility of 40–70 bps due to shorter trading duration. We find that non-trading durations at National Stock Exchange account for 60–70% of the overall realized variance of rupee futures. Using MGARCH model with BEKK parameterization, we find evidence of bidirectional volatility spillover from Offshore to Onshore Rupee markets.

     

Volatility and firm growth

A growing body of macroeconomic evidence suggests that volatility is detrimental for economic growth. The channel through which this materializes, however, is less clear. Moreover, substantive evidence based on disaggregate data is scarce. In this paper, we provide empirical support for this relationship using a detailed cross-country firm-level dataset. We also provide additional evidence that institutional obstacles magnify the adverse effect of perceived volatility on firm growth.      

Estimation of Asymmetric Stochastic Volatility Models for Stock-Exchange Index Returns

The objective of this paper is to put forward a new autoregressive asymmetric stochastic volatility model for modeling volatility and to compare results obtained for this model with an autoregressive stochastic model and another asymmetric volatility model, such as, asymmetric generalized autoregressive conditional heteroskedasticity model. The results obtained from the estimation by maximum likelihood have shown the volatility behavior is asymmetric in the majority of cases. This fact is better shown by the ARSVA model, than the rest of alternative models. Moreover, the ARSVA model is able to reproduce other stylized facts of such series, such as high kurtosis, no autocorrelation of returns, slow decreasing of the autocorrelation function of the squared returns and high persistence.

Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models

A new literature has been recently devoted to the modeling of ultra-high-frequency (UHF) data. Our first aim is to develop an empirical application of UHF-GARCH models to forecast future volatilities on irregularly spaced data. We also compare the out-sample performance of these generalized autoregressive conditional heteroskedastic (GARCH) models with the realized volatility method. We propose a procedure to account for the time deformation problem and show how to use these models for computing daily Value at Risk (VaR).     

The Efficiency of the Realized Range Measure of Daily Volatility: Evidence from Greece

In this study we estimate and compare the realized range volatility, a novel efficient volatility estimator computed by summing high–low ranges for intra‐day intervals, to the recently popularized realized variance estimator obtained by summing squared intra‐day returns. Our results, derived from a Greek equity high‐frequency data set, show that realized range‐based measures improve upon the corresponding realized variance‐based ones in most cases, especially for the most actively traded stocks. The usefulness of high‐frequency data in measuring and forecasting financial volatility is apparent throughout the paper.     

A common factor of stochastic volatilities between oil and commodity prices

This article analyses the multivariate stochastic volatilities (SVs) with a common factor influencing volatilities in the prices of crude oil and agricultural commodities, used for both biofuel and nonbiofuel purposes. Modelling the volatility is crucial because the volatility is an important variable for asset allocation, risk management and derivative pricing. We develop a SV model comprising a latent common volatility factor with two asymptotic regimes with a smooth transition between them. In contrast to conventional volatility models, SVs are generated by the logistic transformation of latent factors, which comprise two components: the common volatility factor and an idiosyncratic component. We present a SV model with a common factor for oil, corn and wheat from 8 August 2005 to 10 October 2014, using a Markov chain Monte Carlo method to estimate the SVs and extract the common volatility factor. We find that the volatilities of oil and grain markets are persistent. According to the estimated common volatility factor, high volatility periods match the 2007–2009 recession and the 2007–2008 financial crisis quite well. Finally, the extracted common volatility factor exhibits a distinct pattern.     

Volatility transmission between Japan,UK and USA in daily stock returns

In the past, there are a lot of studies which conclude that the holiday, asymmetry and day-of-the-week effects influence stock price volatility. Most of the studies are based on a class of generalized auto-regressive conditional heteroskedasticity (GARCH) models. No one examines these effects simultaneously using stochastic volatility (SV) models. In this paper, using the SV model, we examine whether these effects play an important role in stock price volatilities. Furthermore, we consider spillover effects between Japan, UK and USA, where spillover effects in price level as well as volatility are taken into account. We are grateful to two anonymous referees for suggestions and comments. We also acknowledge Toshiaki Watanabe who gave us a lot of helpful suggestions and comments in the preliminary version of this paper. This research is partially supported from Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research (C) #18530158, 2006–2009, and Grant-in-Aid for COE Research) and the Zengin Foundation (Grant-in-Aid for Studies on Economics and Finance), which are acknowledged by H. Tanizaki.     

中国股票市场波动率的高频估计与特性分析

本文旨在应用高频数据估计中国股市的已实现波动率。我们发现股票指数与个股的高频交易数据中的微观摩擦影响正好相反 ,使用极高频的数据会大大增加个股的波动率估计值 ,相反却会大大降低指数的波动率估计值。在计算各种频率的已实现波动率的基础上 ,本文构造了一种较为精确的估计波动率的方法 ,可以更好地平衡测量误差与微观结构误差。基于已实现波动率 ,本文研究了中国股市波动率不对称性和长期记忆特性     

我国财政支出波动对居民消费波动的影响研究

利用1981—2010年我国27个省级地区的面板数据,在加入居民收入波动、金融发展水平、贸易开放度三个控制变量的基础上,综合运用固定效应模型及工具变量估计法,对我国财政支出波动与居民消费波动的关系进行探讨,结果表明:居民消费波动受财政支出波动的影响显著,两者呈正相关关系;金融发展水平及贸易开放度有助于减轻居民消费的波动程度;居民收入波动对消费波动的影响不显著。     

Volatility forecasting using related markets’ information for the Tokyo stock exchange

Due to lack of information, volatility cannot be estimated via a high-frequency approach when markets are non-trading. In this paper, we focus on volatility forecasting for the Tokyo Stock Exchange (TSE) using high-frequency data of related assets traded in international markets when TSE is closed. We use the heterogenous autoregressive model to identify an optimal approach of this additional information for the ten largest TSE-listed stocks, TOPIX and Nikkei 225. The usefulness of harnessing global and neighbour market information in forecasting the TSE market volatility is confirmed through in-depth empirical analysis. Our findings have important implications for investors and policy makers.     

中国股市收益、收益波动与投资者情绪

本文从我国股市的现实情况出发 ,构造理论模型证明 :投资者接受价格信号时表现出来的情绪是影响均衡价格的系统性因子。这一结论得到实际数据的支持 ,实证发现投资者情绪的变化不仅显著地影响沪深两市收益 ,而且显著地反向修正沪深两市收益波动 ,并通过风险奖励影响收益。研究结果表明 ,沪深两市不仅具有相同的投资者行为和风险收益特征 ,而且均未达到弱式有效 ,机构投资者是可能的噪声交易者风险源。     

Forecasting the oil futures price volatility: A new approach

This study provides a new perspective of modelling and forecasting realized range-based volatility (RRV) for crude oil futures. We are the first to improve the Heterogeneous Autoregressive model of Realized Range-based Volatility (HAR-RRV) model by considering the significant jump components, signed returns and volatility of realized range-based volatility. The empirical results show that the volatility of volatility significantly exists in the oil futures market. Moreover, our new proposed models with significant jump components, signed returns and volatility of volatility can gain higher forecast accuracy than HAR-RRV-type models. The results are robust to different forecasting windows and forecasting horizons. Our new findings are strategically important for investors making better decisions.     

The information content of implied volatility in developed versus developing FX markets

This article predicts the daily movement of monthly foreign exchange (FX) rate volatility using a linear combination of a time-series model and implied volatilities from options. The focus is on analysing the FX volatilities in three developing economies (the Brazilian real (BRL), the Indian rupee (INR) and the Russian ruble (RUB)) against the US dollar (USD). The empirical exercise utilizes two time-series models, mixed data sampling (MIDAS) and GARCH. The analysis indicates that for both developed and developing economies the predictive power of MIDAS and that of GARCH is comparable. Further on in this article, we will ascertain whether the relationship between realized and implied volatility is fundamentally different in the case of developing economies from that among developed economies. Thus, we compare the pairs USD/BRL, USD/INR and USD/RUB against EURO/USD and USD/Japanese yen to determine the information content and predictive power of implied volatilities. Plots of the MIDAS coefficients show that the volatility is more persistent in developing economies than in developed economies.     

Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.     

股市收益率与波动性长期记忆效应的实证研究

股票市场长期记忆效应问题是近来金融实证研究的一个热点.多数的研究集中在收益率长期相关性的考察上,较少有对波动率序列的研究.然而,波动率的长期记忆性不仅会导致金融市场上的波动持久性特征,而且将对波动率的预测与衍生证券定价产生重要的影响.基于此,本文通过修正的R/S分析与ARFIMA模型对我国股市收益率及其波动性的长期相关性进行了实证研究.结果表明:中国股市具有显著的非线性特征,虽然收益率序列的自相关性较弱,但波动性序列却表现出显著的长期记忆效应.这一结论将为研究股票价格行为特征与金融经济学理论提供新的方向.