Search results
1 – 10 of over 87000The purpose of this paper is to examine whether realized volatility can provide additional information on the volatility process to the GARCH and EGARCH model, based on the data…
Abstract
Purpose
The purpose of this paper is to examine whether realized volatility can provide additional information on the volatility process to the GARCH and EGARCH model, based on the data of Chinese stock market.
Design/methodology/approach
The realized volatility is defined as the squared overnight return plus the close to open squared return of the period between the morning and afternoon session, to plus the sum of the squared f-minute returns between the trading hours during the relevant trading day. The methodology is a GARCH (EGARCH) model with added explanation variables in the variance equation. The estimation methodology is exact maximum likelihood estimation, using the BHHH algorithms for optimization.
Findings
There are some stocks for which realized volatility measures add information in the volatility process, but there are still quite a number of stocks for which they do not contain any additional information. The 30 minutes realized volatility measures outperform measures constructed on other time intervals. The firm size, turnover rate, and amplitude also partially explain the difference in realized volatility ' s explanatory power across firms.
Research limitations/implications
When analyzing the factors determining the role of realized volatility, as the difficulty of getting the data, ownership structure and ultimately ownerships are not taken into account, except for the turnover ratio, amplitude and size.
Originality/value
This study extends firstly this line of inquiry of realized volatility into the emerging Chinese stock market. Due to the unique institutional setting in China, the results of this study have played an important role on pricing warrant for domestic investors in the Chinese market.
Details
Keywords
Emawtee Bissoondoyal-Bheenick, Robert Brooks, Sirimon Treepongkaruna and Marvin Wee
This chapter investigates the determinants of the volatility of spread in the over-the-counter foreign exchange market and examines whether the relationships differ in the crisis…
Abstract
This chapter investigates the determinants of the volatility of spread in the over-the-counter foreign exchange market and examines whether the relationships differ in the crisis periods. We compute the measures for the volatility of liquidity by using bid-ask spread data sampled at a high frequency of five minutes. By examining 11 currencies over a 13-year sample period, we utilize a balanced dynamic panel regression to investigate whether the risk associated with the currencies quoted or trading activity affects the variability of liquidity provision in the FX market and examine whether the crisis periods have any effect. We find that both the level of spread and volatility of spread increases during the crisis periods for the currencies of emerging countries. In addition, we find increases in risks associated with the currencies proxied by realized volatility during the crisis periods. We also show risks associated with the currency are the major determinants of the variability of liquidity and that these relationships strengthen during periods of uncertainty. First, we develop measures to capture the variability of liquidity. Our measures to capture the variability of liquidity are non-parametric and model-free variable. Second, we contribute to the debate of whether variability of liquidity is adverse to market participants by examining what drives the variability of liquidity. Finally, we analyze seven crisis periods, allowing us to document the effect of the crises on determinants of variability of liquidity over time.
Details
Keywords
Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Ginger Wu
A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying…
Abstract
A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.
David G. McMillan and Alan E.H. Speight
In this paper weekly volatility forecasts are considered with applications to risk management; in particular hedge ratios and VaR calculations, with the aim of identifying the…
Abstract
Purpose
In this paper weekly volatility forecasts are considered with applications to risk management; in particular hedge ratios and VaR calculations, with the aim of identifying the most appropriate model for risk management practice.
Design/methodology/approach
The study considers a variety of models, including those typically employed within the risk management industry, such as averaging and smoothing techniques, as well as those favored in academic circles, such as the GARCH genre of models, and a more recent realized volatility approach which incorporates both the simplicity in construction favored by the finance industry and the flexibility and theoretical underpinnings recommended by academics.
Findings
The results support the view that this realized volatility measure provides not only superior volatility forecasts per se, but also allows for improved hedge ratio and VaR calculations.
Practical implications
The research findings carry practical implications for the conduct of risk management, namely that volatility forecasts are best obtained using the realized volatility approach.
Originality/value
It is therefore proposed that a future direction for risk management practice may be to utilize such measures, while more generally it is hoped that such approaches may improve the cross‐fertilization of ideas and practice between the academic and practitioner communities.
Details
Keywords
Rahul Roy and Santhakumar Shijin
The purpose of the study is to examine the dynamics in the troika of asset pricing, volatility, and the business cycle in the US and Japan.
Abstract
Purpose
The purpose of the study is to examine the dynamics in the troika of asset pricing, volatility, and the business cycle in the US and Japan.
Design/methodology/approach
The study uses a six-factor asset pricing model to derive the realized volatility measure for the GARCH-type models.
Findings
The comprehensive empirical investigation led to the following conclusion. First, the results infer that the market portfolio and human capital are the primary discounting factors in asset return predictability during various phases of the subprime crisis phenomenon for the US and Japan. Second, the empirical estimates neither show any significant impact of past conditional volatility on the current conditional volatility nor any significant effect of subprime crisis episodes on the current conditional volatility in the US and Japan. Third, there is no asymmetric volatility effect during the subprime crisis phenomenon in the US and Japan except the asymmetric volatility effect during the post-subprime crisis period in the US and full period in Japan. Fourth, the volatility persistence is relatively higher during the subprime crisis period in the US, whereas during the subprime crisis transition period in Japan than the rest of the phases of the subprime crisis phenomenon.
Originality/value
The study argues that the empirical investigations that employed the autoregressive method to derive the realized volatility measure for the parameter estimation of GARCH-type models may result in incurring spurious estimates. Further, the empirical results of the study show that using the six-factor asset pricing model in an intertemporal framework to derive the realized volatility measure yields better estimation results while estimating the parameters of GARCH-type models.
Details
Keywords
Isao Ishida, Michael McAleer and Kosuke Oya
The purpose of this paper is to propose a new method for estimating continuous‐time stochastic volatility (SV) models for the S&P 500 stock index process using intraday…
Abstract
Purpose
The purpose of this paper is to propose a new method for estimating continuous‐time stochastic volatility (SV) models for the S&P 500 stock index process using intraday high‐frequency observations of both the S&P 500 index and the Chicago Board Options Exchange (CBOE) implied (or expected) volatility index (VIX).
Design/methodology/approach
A primary purpose of the paper is to provide a framework for using intraday high‐frequency data of both the indices' estimates, in particular, for improving the estimation accuracy of the leverage parameter, that is, the correlation between the two Brownian motions driving the diffusive components of the price process and its spot variance process, respectively.
Findings
Finite sample simulation results show that the proposed estimator delivers more accurate estimates of the leverage parameter than do existing methods.
Research limitations/implications
The focus of the paper is on the Heston and non‐Heston leverage parameters.
Practical implications
Finite sample simulation results show that the proposed estimator delivers more accurate estimates of the leverage parameter than do existing methods.
Social implications
The research findings are important for the analysis of ultra high‐frequency financial data.
Originality/value
The paper provides a framework for using intraday high‐frequency data of both indices' estimates, in particular, for improving the estimation accuracy of the leverage parameter, that is, the correlation between the two Brownian motions driving the diffusive components of the price process and its spot variance process, respectively.
Details
Keywords
Xuebiao Wang, Xi Wang, Bo Li and Zhiqi Bai
The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.
Abstract
Purpose
The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.
Design/methodology/approach
This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.
Findings
This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.
Research limitations/implications
This paper has research limitations in variable measurement and data selection.
Practical implications
This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.
Originality/value
The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.
Details
Keywords
Glenn Kit Foong Ho, Sirimon Treepongkaruna and Chaiyuth Padungsaksawasdi
This paper examines whether short sellers aggravate volatility in the Australian stock market by using five different realized volatility (RV) measures during a more stable period.
Abstract
Purpose
This paper examines whether short sellers aggravate volatility in the Australian stock market by using five different realized volatility (RV) measures during a more stable period.
Design/methodology/approach
The authors develop a measure to capture the abnormal level of short selling for each stock and examine the bivariate and trivariate dynamic relationships between abnormal short selling and five volatility measures: the RV, continuous and jump components of RV, upside and downside volatilities.
Findings
Overall, the findings indicate a weak association between abnormal short selling and volatility. Where the relationships are significant, the authors generally find that lagged abnormal short selling is negatively associated with both upside and downside volatilities. In general, short selling does not drive or amplify the decline in stock prices.
Originality/value
This paper contributes to existing literature in various aspects. First, the authors offer evidence on the relationship between abnormal short selling and volatility in a general market condition while existing studies often found mixed results of the effects of short selling on volatility around extreme events. Second, the authors add to the literature on the volume-volatility relation by introducing abnormal short selling. Although abnormal short volume does not supplant the number of trades in the volume-volatility relation, it has some incremental, albeit negative, effect on volatility. Finally, the study provides further evidence for the debate on the desirability of short sellers in financial markets.
Details
Keywords
Osvaldo Candido Silva Filho and Flavio Augusto Ziegelmann
The aim of this paper is to measure and evaluate the relationship between returns-volatility and trading volume and returns and volatility of financial market indexes using…
Abstract
Purpose
The aim of this paper is to measure and evaluate the relationship between returns-volatility and trading volume and returns and volatility of financial market indexes using time-varying copulas.
Design/methodology/approach
The time dynamic dependence parameter is allowed to evolve according to a restricted ARMA-type equation which includes a constant term that is driven by a hidden two-state first-order Markov chain.
Findings
In using this time dynamics in conjunction with non-elliptical distribution functions and tail dependence measure, the authors are allowing for (and focusing on) non-linearities in the returns-volume-volatility relationship. The results support the assumption that current trading volume provides information about future volatility as well as that there is a negative relationship between returns and their volatilities in financial market indexes.
Originality/value
The authors provide an interesting empirical interpretation for the regimes the authors have identified: in the high dependence regime the sequential information arrival hypothesis and/or noise trading hypothesis are valid, consequently future volatility prediction is possible and persistent but does not last indefinitely; in the low dependence regime, the future volatility prediction is more unlikely to occur, since both trading volume and return negatives have a low (near zero) relation with future volatility.
Details
Keywords
Peter U. Abel and Thomas von Woedtke
To overcome the problem of metabolic crashes as hypoglycaemic as well as hyperglycaemic episodes in diabetic patients the continuous or at least very frequent checking of the…
Abstract
To overcome the problem of metabolic crashes as hypoglycaemic as well as hyperglycaemic episodes in diabetic patients the continuous or at least very frequent checking of the circulating intracorporal glucose concentration is necessary. Biosensors measuring glucose in vivo are suitable for estimating the transient interstitial glucose concentration in human beings. Biologically and/or biochemically caused processes are responsible for limiting the functional stability of implanted sensors. It is now possible to advance beyond the current practice of hand making glucose sensors in the laboratory and produce these sensors as industrial products with reproducible characteristics. This gives us a real chance to avoid hypoglycaemic and hyperglycaemic metabolic attacks.
Details