OPTIMAL WEIGHT FOR REALIZED VARIANCE BASED ON INTERMITTENT HIGH‐FREQUENCY DATA*

Japanese stock markets have two types of breaks, overnight and lunch, during which no trading occurs, causing an inevitable increased variance in estimating daily volatility via a naive realized variance (RV). In order to perform a more stabilized estimation, we modify Hansen and Lunde's weighting technique. As an empirical study, we estimate optimal weights by using a particular approach for Japanese stock data listed on the Tokyo Stock Exchange, and then compare the forecast performance of weighted and non‐weighted RV through an autoregressive fractionally integrated moving average model. The empirical result indicates that the appropriate use of the optimally weighted RV can lead to remarkably smaller estimation variance compared with the naive RV, in many series. Therefore a more accurate forecasting of daily volatility data is obtained. Finally, we perform a Monte Carlo simulation to support the empirical result.     

QUANTILE FORECASTS OF FINANCIAL RETURNS USING REALIZED GARCH MODELS*

This article applies the realized generalized autoregressive conditional heteroskedasticity (GARCH) model, which incorporates the GARCH model with realized volatility, to quantile forecasts of financial returns, such as Value‐at‐Risk and expected shortfall. Student's t‐ and skewed Student's t‐distributions as well as normal distribution are used for the return distribution. The main results for the S&P 500 stock index are: (i) the realized GARCH model with the skewed Student's t‐distribution performs better than that with the normal and Student's t‐distributions and the exponential GARCH model using the daily returns only; and (ii) using the realized kernel to take account of microstructure noise does not improve the performance.     

An Improved Two‐step Regularization Scheme for Spot Volatility Estimation

We are concerned with the problem of spot volatility estimation in the presence of microstructure noise. We introduce an estimator based on the technique of multi‐step regularization. A preliminary form for such an estimator was proposed in Ogawa (2008) and was shown to work in a real‐time manner. However, the main drawback of this scheme is that it needs a lot of observation data. The aim of the present paper is to introduce an improvement to this scheme, such that the modified estimator can work more efficiently and with a data set of smaller size. The technical aspects of implementation of the proposed scheme and its performance on simulated data are analysed. The scheme is tested against other spot volatility estimators, namely a realized volatility type estimator, the Fourier estimator and three kernel estimators.     

Intraday market effects in electronic soybean futures market during non-trading and trading hour announcements

This article investigates market reactions to major United States Department of Agriculture announcements during non-trading and trading hours in the soybean futures market using microstructure data. Following report release, volume increases and remains elevated for up to 15 to 20 minutes. The volume spikes for the non-trading releases relative to the trading releases, but are identical after the first reaction. Report releases during non-trading hours cause a large spike in volatility at the onset of trading which subsides quickly. In contrast, releases during trading hours result in a smaller volatility spike, which extends for 5–6 min at a higher magnitude. Adjusting volatility by normal trading volatility indicates that volatility in trading hour release is higher in both immediate response and persistence. Return correlations provide little evidence to support systematic under- or overreaction in prices regardless of when the report is released reflecting the efficiency of the market.     

Forecasting the realized volatility in the Chinese stock market: further evidence

In this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model with the jump test and a decomposed signed jump variation have better out-of-sample forecasting performance. Finally, the use of the decomposed signed jump variations in predictive regressions can improve the economic value of realized volatility forecasts.     

ALLOWING FOR JUMP MEASUREMENTS IN VOLATILITY: A HIGH‐FREQUENCY FINANCIAL DATA ANALYSIS OF INDIVIDUAL STOCKS

Following recent advances in the non‐parametric realized volatility approach, we separately measure the discontinuous jump part of the quadratic variation process for individual stocks and incorporate it into heterogeneous autoregressive volatility models. We analyse the distributional properties of the jump measures vis‐à‐vis the corresponding realized volatility ones, and compare them to those of aggregate US market index series. We also demonstrate important gains in the forecasting accuracy of high‐frequency volatility models.     

Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market

Based on methods developed by Bollerslev et al. (2016), we explicitly accounted for the heteroskedasticity in the measurement errors and for the high volatility of Chinese stock prices; we proposed a new model, the LogHARQ model, as a way to appropriately forecast the realized volatility of the Chinese stock market. Out-of-sample findings suggest that the LogHARQ model performs better than existing logarithmic and linear forecast models, particularly when the realized quarticity is large. The better performance is also confirmed by the utility based economic value test through volatility timing.     

TRADER TYPE EFFECTS ON THE VOLATILITY‐VOLUME RELATIONSHIP EVIDENCE FROM THE KOSPI 200 INDEX FUTURES MARKET

We investigate whether the trading activity generated by investors with different access to information and trading motives has positive or negative impact on index futures volatility. Surprises in non‐member institutional, individual and foreign investors' trading volume are positively associated with volatility in most of the cases. For member institutional investors, unexpected trading volume is positively related to volatility. Long‐run changes in the trading activity also affect volatility differently across trader types. Finally, allowing for time‐to‐maturity effects, surprises in open interest are associated with more volatility towards contract expiration, contrary to the negative effect we find during normal times.     

Analysis of High Frequency Data in Finance: A Survey

This study examines the use of high frequency data in finance, including volatility estimation and jump tests. High frequency data allows the construction of model-free volatility measures for asset returns. Realized variance is a consistent estimator of quadratic variation under mild regularity conditions. Other variation concepts, such as power variation and bipower variation, are useful and important for analyzing high frequency data when jumps are present. High frequency data can also be used to test jumps in asset prices. We discuss three jump tests: bipower variation test, power variation test, and variance swap test in this study. The presence of market microstructure noise complicates the analysis of high frequency data. The survey introduces several robust methods of volatility estimation and jump tests in the presence of market microstructure noise. Finally, some applications of jump tests in asset pricing are discussed in this article.     

S&P500 volatility analysis using high-frequency multipower variation volatility proxies

The availability of ultra-high-frequency data has sparked enormous parametric and nonparametric volatility estimators in financial time series analysis. However, some high-frequency volatility estimators are suffering from biasness issues due to the abrupt jumps and microstructure effect that often observed in nowadays global financial markets. Hence, we motivate our studies with two long-memory time series models using various high-frequency multipower variation volatility proxies. The forecast evaluations are illustrated using the S&P500 data over the period from year 2008 to 2013. Our empirical studies found that higher-power variation volatility proxies provide better in-sample and out-of-sample performances as compared to the widely used realized volatility and fractionally integrated ARCH models. Finally, these empirical findings are used to estimate the one-day-ahead value-at-risk of S&P500.     

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.     

On the Use of the Moving Average Trading Rule to Test for Weak Form Efficiency in Capital Markets

The examination for the possible existence of predictive power in the moving average trading rule has been used extensively to test the hypothesis of weak form market efficiency in capital markets. This work focuses mainly on the study of the variation of the moving average (MA) trading rule performance as a function of the length of the longer MA. Empirical analysis of daily data from NYSE and the Athens Stock Exchange reveal high variability of the performance of the MA trading rule as a function of the MA length and on some occasions the series of successive trading rule total returns is non‐stationary. These findings have direct implications in weak form market efficiency testing. Indeed, given this high variability of the performance of the MA trading rule, by just finding out that trading rules with some specific combinations of MA lengths can or cannot beat the market, as is the case in most of the published work thus far, is not enough evidence for or against the existence of weak form market efficiency. Results also show that on average in about three out of four cases trading rule signals are false, a fact that leaves a lot of space for improved trading rule performance if trading rule signals are combined with other information (e.g. filters, or volume of trade). Finally, some evidence of enhanced trading rule performance for the shorter MA lengths was found. This enhanced performance is partly attributed to the higher probability that a trading rule signal is not a whipsaw, as well as to the larger number of days out‐of‐the‐market which are associated with shorter MA lengths.     

THE DYNAMIC RELATION BETWEEN RETURNS,TRADING VOLUME,AND VOLATILITY: LESSONS FROM SPILLOVERS BETWEEN ASIA AND THE UNITED STATES

This paper investigates the dynamic relationship between index returns, return volatility, and trading volume for eight Asian markets and the US. We find cross‐border spillovers in returns to be non‐existent, spillovers in absolute returns between Asia and the US to be strong in both directions, and spillovers in volatility to run from Asia to the US. Trading volume, especially on the Asian markets, depends on shocks in domestic and foreign returns as well as on volatility, especially those shocks originating in the US. However, only weak evidence is found for trading volume influencing other variables. In the light of the theoretical models, these results suggest sequential information arrivals, with investors being overconfident and applying positive feedback strategy. Furthermore, new information causes price volatility to rise due to differences in its interpretation among traders, but the subsequent market reaction takes the form of adjustment in price level, not volatility. Lastly, the intensity of cross‐border spillovers seems to have increased following the 1997 crisis, which we interpret as evidence of increased noisiness in prices and diversity in opinions about news originating abroad. Our findings might also help to understand the nature of financial crises, to predict their further developments and consequences.     

Pairs trading: does volatility timing matter?

Pairs trading is a dollar-neutral trading strategy. Using the components of two major stock indices, the S&P 500 and the Nikkei 225, this article deals with the performance of a pairs trading system based on various pairs selection methods (distance, stationarity, cointegration) over a 10-year period. On both markets, using a classical framework, cointegration appears superior and effective. On the U.S. market and also in Japan to a lower extent, pairs trading strategies exhibited an impressive performance during the 2008 financial crisis. Bearish periods are associated with a high level of the VIX index: the ‘investor fear gauge’. Using a modified trading system, this article examines the link between pairs trading performance and volatility/VIX timing. It is shown that for the best selection technique (cointegration), timing volatility has no economic value in a pairs trading context.     

Asymmetric effect of exchange rate volatility on India's cross‐border trade: Evidence from global financial crisis and multiple threshold nonlinear autoregressive distributed lag model

This paper examines the asymmetric effect of exchange rate volatility on India's cross‐border trade with its major trading partners: Japan, Germany, the United States, and China. We extend previous studies in two ways. First, we examine whether global financial crisis changes the asymmetric effect of exchange rate volatility on India's cross‐border trade. Next, we divide exchange rate volatility into quintiles and examine the effect of each quintile on cross‐border trade by using the multiple threshold nonlinear autoregressive distributed lag (MTNARDL) model. Our findings from standard nonlinear ARDL (NARDL) indicate that the asymmetric relationship between exchange rate volatility and cross‐border trade changes as a result of global financial crisis. In addition, findings from MTNARDL indicate that in short‐run, exchange rate volatility symmetrically affects India's cross‐border trade with all sample countries whereas in long‐run it asymmetrically affects cross‐border trade. Overall, these findings are very important for policy implications and open a new dimension to exchange rate volatility and trade flows.     

Modeling and forecasting return jumps using realized variation measures

This paper proposes a simple HAR-RV-based model to predict return jumps through a conditional density of jump size with time-varying moments. We model jump occurrences based on a version of the autoregressive conditional hazard model that relies on past continuous realized volatilities. Applying our methodology to seven equity indices on the U.S. and Chinese stock markets, we reach the following key findings: (i) jump occurrence and size are dependent on past realized volatility, (ii) the proposed model yields superior in- and out-of-sample jump size density forecasts compared to an ARMA(1,1)-GARCH(1,1) model, (iii) and the occurrence and sign of return jumps are predictable to some extent.     

BAYESIAN ANALYSIS OF GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY AND STOCHASTIC VOLATILITY: MODELING LEVERAGE,JUMPS AND HEAVY‐TAILS FOR FINANCIAL TIME SERIES*

This paper develops a Bayesian model comparison of two broad major classes of varying volatility model, the generalized autoregressive conditional heteroskedasticity and stochastic volatility models, on financial time series. The leverage effect, jumps and heavy‐tailed errors are incorporated into the two models. For estimation, the efficient Markov chain Monte Carlo methods are developed and the model comparisons are examined based on the marginal likelihood. The empirical analyses are illustrated using the daily return data of US stock indices, individual securities and exchange rates of UK sterling and Japanese yen against the US dollar. The estimation results indicate that the stochastic volatility model with leverage and Student‐t errors yield the best performance among the competing models.     

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.

     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

Small trades and volatility increases after stock splits

In this paper, we examine the trading activity and return volatility pattern before and after splits. Unlike previous studies, we employ high-frequency transaction data and more powerful asymptotical tests on the impact of split on volatility. Furthermore, we examine the relationship between volatility and volume using different volatility measures and controlling for the effects of autocorrelation and trading costs. We find that small trades increase significantly after stock splits and the increase in return volatility is strongly related to the increase in small trades after stock splits. The results support our contention that the post-split volatility increase is driven primarily by the trading activity of smaller noise investors. Test results are robust to different measures of trading activity and return volatility.