scholarly journals Equity Risk: Measuring Return Volatility Using Historical High-Frequency Data

2019 ◽  
Vol 14 (3) ◽  
pp. 60-71
Author(s):  
Chow Alan ◽  
Lahtinen Kyre
Keyword(s):  
High Frequency ◽  
Stock Price ◽  
Stock Return ◽  
Realized Volatility ◽  
Return Volatility ◽  
Time Intervals ◽  
Equity Risk ◽  
Tick Data ◽  
The Mean ◽  
Pitman Closeness

AbstractMarket Volatility has been investigated at great lengths, but the measure of historical volatility, referred to as the relative volatility, is inconsistent. Using historical return data to calculate the volatility of a stock return provides a measure of the realized volatility. Realized volatility is often measured using some method of calculating a deviation from the mean of the returns for the stock price, the summation of squared returns, or the summation of absolute returns. We look to the stocks that make up the DJIA, using tick-by-tick data from June 2015 - May 2016. This research helps to address the question of what is the better measure of realized volatility? Several measures of volatility are used as proxies and are compared at four estimation time intervals. We review these measures to determine a closer/better fit estimator to the true realized volatility, using MSE, MAD, Diebold-Mariano test, and Pitman Closeness. We find that when using a standard deviation based on transaction level returns, shorter increments of time, while containing some levels of noise, are better estimates of volatility than longer increments.

Does domestic institutional ownership increase return volatility? The French context

2019 ◽  
Vol 61 (2) ◽  
pp. 421-433
Author(s):  
Mouna Aloui ◽  
Anis Jarboui
Keyword(s):  
Institutional Investors ◽  
Stock Markets ◽  
Stock Price ◽  
Stock Return ◽  
Price Volatility ◽  
Return Volatility ◽  
Stock Price Volatility ◽  
Content Type ◽  
Stock Return Volatility ◽  
Empirical Results

Purpose The purpose of this study is to examine the impact of domestic ownership on the stock return volatility. The authors use a detailed panel data set of 89 French companies listed on the SBF 120 over the period 2006-2013. The empirical results show that the domestic institutional investors have low stock price volatility in the French stock market. This result implies the stabilizing factor of domestic investors in France stock markets, which can be considered as one of the potential favor of growing the exhibition of domestic stock markets to institutional investors. This study employs a variety of econometric models, including feedbacks, to test the robustness of our empirical results. Design/methodology/approach To explain the relation between stock return volatility and domestic institutional investors (DIIs), the authors used two complementary methods: two-step generalized method of moments analysis as well as panel vector autoregressive framework and two-stage least squares (2SLS) method. Findings The authors’ empirical results show that the proportion of DIIs with advanced local degrees stabilizes the stock price volatility. However, firm’s size and the turnover have a positive effect on the volatility of the stock returns. This result is consistent with the hypothesis that the firm’s size and the turnover will increase price volatility during a financial crisis as a result of the deterioration of the monitoring mechanism and the reduction of the investors’ confidence in firms. Originality/value This result also indicates that the variables (the firm’s size, total sales and debt ratio) are poor corporate governance and have a role in the increased the stock return volatility.

Effectiveness of filter trading as an intraday trading rule

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ling Xin ◽  
Kin Lam ◽  
Philip L.H. Yu
Keyword(s):  
High Frequency ◽  
Efficient Market Hypothesis ◽  
Realized Volatility ◽  
Optimal Filter ◽  
Trading Rule ◽  
Content Type ◽  
Empirical Results ◽  
Tick Data ◽  
Filter Size ◽  
Momentum Trading

Purpose Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings. Design/methodology/approach Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship. Findings The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility. Originality/value First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.

scholarly journals Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors

2021 ◽  
Vol 14 (4) ◽  
pp. 145
Author(s):  
Makoto Nakakita ◽  
Teruo Nakatsuma
Keyword(s):  
Stock Returns ◽  
Stochastic Volatility ◽  
High Frequency ◽  
Stock Price ◽  
Information Criterion ◽  
High Frequency Data ◽  
Return Volatility ◽  
Frequency Data ◽  
Volatility Clustering ◽  
Variance Gamma

Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper, we extend the stochastic volatility (SV) model for application with such intraday high-frequency data and develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm for Bayesian inference of the proposed model. Our modeling strategy is two-fold. First, we model the intraday seasonality of return volatility as a Bernstein polynomial and estimate it along with the stochastic volatility simultaneously. Second, we incorporate skewness and excess kurtosis of stock returns into the SV model by assuming that the error term follows a family of generalized hyperbolic distributions, including variance-gamma and Student’s t distributions. To improve efficiency of MCMC implementation, we apply an ancillarity-sufficiency interweaving strategy (ASIS) and generalized Gibbs sampling. As a demonstration of our new method, we estimate intraday SV models with 1 min return data of a stock price index (TOPIX) and conduct model selection among various specifications with the widely applicable information criterion (WAIC). The result shows that the SV model with the skew variance-gamma error is the best among the candidates.

Execution costs, investability, and actual foreign investment in emerging markets

2019 ◽  
Vol 10 (2) ◽  
pp. 143-167
Author(s):  
Hee-Joon Ahn ◽  
Jun Cai ◽  
Yan-Leung Cheung
Keyword(s):  
Foreign Investment ◽  
Stock Price ◽  
Financial Liberalization ◽  
Emerging Market ◽  
Return Volatility ◽  
Cross Sectional ◽  
Content Type ◽  
Execution Costs ◽  
The Mean ◽  
Emerging Market Firms

Purpose This paper focuses on execution costs as liquidity measure. Execution costs are related to volatility and are an important component of a firm’s cost of capital. The purpose of this paper is to examine whether emerging market firms have lower execution costs when they face less restrictions on foreign investment and when they have more foreign shareholders. Design/methodology/approach The authors begin by documenting the cross-sectional behavior of execution costs. The authors then obtain preliminary evidence on the interaction between execution costs, the investability index and actual foreign investment. These results foreshadow those the authors obtain with the regression analysis. The ordinary least square results show that more investable firms have lower execution costs after the authors control for firm size, stock price, return volatility, industry effects and country effects. This evidence is very robust and highly significant. Direct foreign ownership (FO) in emerging market firms also appear to be associated with lower execution costs. The economic benefit from lowering the investability index on trade execution costs is highly significant. Findings Using a large cross-sectional sample from 23 emerging markets, the authors show that firms with more ex ante restrictions on FO, measured by the investability index, have lower execution costs, such as quoted spreads (QS) and effective spreads (ES), after the authors control for firm size, stock price, return volatility, industry factors and country effects. In addition, direct FO in emerging market firms appears to be associated with lower execution costs. However, ex ante restrictions on FO dominate the influence of direct FO. For a 0.5 increase in the investability index in the range of 0–1, the QS will be reduced by 17 percent of the mean QS, and the ES will be reduced by 12 percent of the mean ES from the sample stocks. Originality/value There are important differences between the approach and most of the financial liberalization studies. First, whereas most of the earlier studies are conducted at the level of country or market analysis, the investigation is at the level of individual stocks. Second, the authors focus on a cross-sectional association that avoids a criticism leveled at time series analyses. Over-time studies often use specific time points to represent financial liberalization watersheds. This approach can be misleading when financial liberalizations are viewed as processes that unfold over time. Third, the proxies for financial openness are available not only for individual firms across markets, but the authors also make a distinction between potential and actual foreign investment. The authors further categorize actual foreign investment into direct and indirect FO.

Does Option Trading Have a Pervasive Impact on Underlying Stock Prices?

2020 ◽  
Author(s):  
Sophie X Ni ◽  
Neil D Pearson ◽  
Allen M Poteshman ◽  
Joshua White
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Return ◽  
Return Volatility ◽  
Equity Prices ◽  
Option Trading ◽  
Stock Return Volatility ◽  
Market Makers ◽  
Options Trading ◽  
Option Market

Abstract The question of whether and to what extent option trading affects underlying stock prices has been of interest to researchers since exchange-based options trading began in 1973. Recent research presents evidence of an informational channel through which option trading affects stock prices by showing that option market makers’ stock trades to hedge new options positions cause the information reflected in option trading to be impounded into underlying equity prices. This paper provides evidence of a noninformational channel through which option market maker hedge rebalancing affects stock return volatility and the probability of large stock price moves.

Stock Returns and Volatility: Evidence from Select Emerging Markets

2009 ◽  
Vol 12 (04) ◽  
pp. 567-592 ◽  
Author(s):  
Ravinder Kumar Arora ◽  
Himadri Das ◽  
Pramod Kumar Jain
Keyword(s):  
Emerging Markets ◽  
Stock Returns ◽  
Stock Return ◽  
Leverage Effect ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
Developed Markets ◽  
Return Distributions ◽  
The Mean ◽  
Return Data

This paper investigates the behavior of stock returns and volatility in 10 emerging markets and compares them with those of developed markets under different measures of frequency (daily, weekly, monthly and annual) over the period January 1, 2002 to December 31, 2006. The ratios of mean return to volatility for emerging markets are found to be higher than those of developed markets. Sample statistics for stock returns of all emerging and developed markets indicate that return distributions are not normal and return volatility shows clustering. In most cases, GARCH (1, 1) specification is adequate to describe the stock return volatility. The significant lag terms in the mean equation of GARCH specification depend on the frequency of the return data. The presence of leverage effect in volatility behavior is examined using the TAR-GARCH model and the evidence indicates that is not present across all markets under all measures of frequency. Its presence in different markets depends on the measure of frequency of stock return data.

scholarly journals IS TECHNICAL ANALYSIS PROFITABLE AND CAPABLE FOR STOCK PRICE PREDICTION? EVIDENCE FROM MALAYSIAN STOCK MARKET

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Kelvin Lee Yong MIng ◽  
Mohamad Jais ◽  
Bakri Abdul Karim
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Stock Return ◽  
Moving Average ◽  
Fibonacci Numbers ◽  
Technical Analysis ◽  
Least Square ◽  
Ordinary Least Square ◽  
Stock Price Prediction ◽  
The Mean

This study aims to test the ability of technical analysis in predicting the stock price and generating profits. This study employed two of the technical analysis indicators, which are (i) Variable Moving Average (VMA) rules and (ii) Elliot Wave Principle incorporated with Fibonacci numbers. Besides that, this study also examines the relationship between the signals emitted by VMA rules and the stock return by applying Ordinary Least Square (OLS) regression analysis. Among the 42 VMA rules tested, there were only 10 VMA rules shown that the mean returns generated from buy signals are significant higher than the unconditional return. While, the mean returns from sell signals are significant lesser than the unconditional return for all the VMA rules tested. As for Elliot Wave Principle incorporated with Fibonacci numbers indicator, the findings shows that impulsive wave is predictable, meanwhile the corrective wave is less predictable. Lastly, only the signals of 14 VMA rules had shown a significant relationship with the daily stock return. In conclusion, the VMA rules only able to generate profits for certain term of moving average, whereas the Elliot Wave Principle incorporated with Fibonacci numbers tools is useful in predicting the stock market trend.

Sign in / Sign up

Export Citation Format

Share Document