Search results

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.

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.

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.

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.

It is shown in Chou (2005). Journal of Money, Credit and Banking, 37, 561–582that the range can be used as a measure of volatility and the conditional autoregressive range (CARR) model performs better than generalized auto regressive conditional heteroskedasticity (GARCH) in forecasting volatilities of S&P 500 stock index. In this paper, we allow separate dynamic structures for the upward and downward ranges of asset prices to account for asymmetric behaviors in the financial market. The types of asymmetry include the trending behavior, weekday seasonality, interaction of the first two conditional moments via leverage effects, risk premiums, and volatility feedbacks. The return of the open to the max of the period is used as a measure of the upward and the downward range is defined likewise. We use the quasi-maximum likelihood estimation (QMLE) for parameter estimation. Empirical results using S&P 500 daily and weekly frequencies provide consistent evidences supporting the asymmetry in the US stock market over the period 1962/01/01–2000/08/25. The asymmetric range model also provides sharper volatility forecasts than the symmetric range model.

In this chapter, we apply the new measure of speculative activities (hereafter, named the speculative ratio) in Chan, Nguyen, and Chan (2013) to study the relationship between those activities and volatility in the oil futures market. We document that the speculative ratio (trading volume divided by open interest) can isolate speculative elements from total trading activities. Using the oil futures data and dividing the data into two subperiods surrounding Hurricane Katrina, we find an increased speculative trades in the post-Hurricane Katrina period. Our results show that speculative activities create a more volatile oil futures market and they lower the information flow between volatility and speculative activities in the post-Hurricane Katrina period.

While numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely exist. In order to conceal this void, the purpose of this paper is to investigate whether including OVX in the realized volatility (RV) models improve the accuracy of predictions.

At the empirical stage, the authors employ several measures to frame the RV of crude oil futures returns. In particular, the authors use three different range-based RV estimators recommended by Parkinson (1980), Rogers and Satchell (1991) and Alizadeh et al. (2002), respectively.

The findings reveal that the information content of crude OVX helps to provide more accurate volatility predictions in comparison to the base-line RV model which contains only historical oil volatilities. Besides, the forecast encompassing test further suggests that the modified RV model (when OVX is introduced in the base-line RV model) forecast encompasses the conventional RV forecast in majority of the cases.

Since forecasting oil price volatility plays a vital role in portfolio optimization, derivatives pricing, optimum asset allocation decisions and risk management, the findings of this study thus carry important implications for energy economists, investors and policymakers.

This paper adds to the existing literature, since it is one of the initial studies to explore whether OVX is informative about the realized variance of the US oil market returns. The findings recommend that the information content of oil implied volatilities should be taken into account when modeling the US oil market volatility. In addition, range-based measures should be utilized while estimating the RV.

The purpose of this paper is to re-examine the effectiveness of price limits on stock volatilities in China over a more recent time period spanning from 2007 to 2012. The motivation stems from the fact that very high stock market volatilities are observed in China and we are sceptical of the volatility mitigating effect claimed by advocates of price limits.

The effectiveness of price limits on volatilities is examined using an event study methodology and within an expanded framework of volatility-volume relationships. The sample stocks include the 300 component stocks of the CSI300 Index.

Both event study and regression analysis suggest that price limits exaggerate market volatilities by causing volatility spillovers. The destabilising effect is much more pronounced for small firm stocks and when the market falls. In addition to the informational source of volatilities (represented by volume), price limits create another non-trivial frictional source of volatilities in China’s stock market.

This research is the first to re-examine the price limit effect in China’s stock market in an expanded framework of volatility-volume relationships. It identifies price limits, in addition to information, as another non-trivial frictional source of volatilities. The findings derived from a recent sample period confirm the conventional view of inefficiency of price limits raised by Fama (1989) and provide evidence in support of the pervasive trend of stock market deregulations.

This paper aims to propose a framework based on the unbiased extreme value volatility estimator (namely, the AddRS estimator) to compute and predict the long position and the short position value-at-risk (VaR) and stressed expected shortfall (ES). The precise prediction of VaR and ES measures has important implications toward financial institutions, fund managers, portfolio managers, regulators and business practitioners.

The proposed framework is based on the Giot and Laurent (2004) approach and incorporates characteristics like long memory, fat tails and skewness. The authors evaluate its VaR and ES forecasting performance using various backtesting approaches for both long and short positions on four global indices (S&P 500, CAC 40, Indice BOVESPA [IBOVESPA] and S&P CNX Nifty) and compare the results with that of various alternative models.

The findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR and stressed ES. The findings also indicate that the VaR forecasts based on the proposed framework provide the least total loss for various long and short position VaR, and this supports the superior properties of the proposed framework in forecasting VaR more accurately.

The study contributes by providing a framework to predict more accurate VaR and stressed ES measures based on the unbiased extreme value volatility estimator.

The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.

Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.

It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.

This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.

The purpose of this paper is to explore the forecasting effectiveness of Black-Scholes (BS) focussing parity analysis of time series econometric and implied volatility (IV) numerical techniques.

To analyze the comparative competitiveness of econometric time series and IV models this paper consolidated the study with their inter-relations leading toward multilayered moneyness-maturity correlation of model and market option prices, thoroughly determined the moneyness-maturity combinations of error metrics of Nifty index options.

Out of six models tested and critically examined here, the paper procures only a single model, IV, which best caters to the requirements of option traders and as a result the paper ended up that only IV supports to multifarious moneyness-maturity dimension of option pricing of Nifty index options. The analysis also confirms that the standard VIX is not a reliable tool for determining the base price of Nifty index options (via BS). As the IV landmarks during the most dynamic phase of Indian capital market which is a touchstone to justify the quality of any model, the paper can deduce that IV could continue to perform in hardships of financial contraction par smoothly and effectively.

The final outcome of this research which ended successfully in exploring a dominant model, guided successfully through the most volatile period of Indian economy can be used to safe guard investor's faith and to figure a design which could compete on the canvass of option pricing.

As equity market is always subject to highly unpredictable conditions and may keep on experiencing it through all times to come, the unified objective of research is to find out the most impeccable volatility model to meet out the requirements of option practitioners, specifically contributing upto the satisfaction and expected results during tumultuous period.

A major focus of the literature in financial economics is the predictability of excess stock returns. Variables such as interest rates and dividend yields to some degree appear to predict the variation of expected returns over time.

The purpose of this paper is to present the Greek value at risk (VaR) legislation framework and to highlight some of its major deficiencies, using not only theoretical scenarios but also empirical evidence. Moreover, this paper does not only highlight the VaR legislation’s framework deficiencies but also suggests legal interventions for its revision and a new-alternative, flexible and simple-to-be-applied filtered estimation method which improves the VaR evaluations.

The Greek legislation framework suggests that for the daily VaR to be estimated, a minimum data set of the previous year (250 observations) at the 99 per cent confidence level should be considered. This approach may lead to inaccurate VaR estimations, for example, when after a long-term growth period, there is a sudden recession period, because the data input is not representative to the current financial environment. Taking into serious consideration that high volatility periods are linked to a financial crisis, it is assumed that volatility could be an indicator for the financial environment representation. The conventional historical VaR back-tested results suggest that the specific methodology should be revised, especially during the high volatility period. For the newly suggested filtered VaR, the data sample is divided into several regimes depending on the volatility range. The filtered VaR estimation process applies the conventional historical methodology but uses different historical data input depending on the current volatility. This new approach improves the VaR estimation by reducing the VaR daily violations.

The findings regarding the current legislation framework suggest that the financial analysts in Greece have a motivation to adopt a relative VaR approach for risk asset class portfolios (e.g. Greek domestic equity mutual funds), which enables them to bear increased risk without presenting it to the investors. For lower risk portfolios, the absolute VaR may be useful for increased risk bearing strategies. The stricter VaR approaches are preferred to be adopted because stricter VaR estimations are linked to a reduced number of violations. The filtered volatility approach improves the VaR estimations (fewer violations are relative to the conventional approach).

This methodology is designed to be applied for the VaR estimation, but it could be partly applied in other fields of the financial analysis study.

The suggested methodology could present efficient VaR estimation without using sophisticated procedures or expensive VaR systems. Therefore, it could be easily applied by the risk analysts. Moreover, the overview of the Greek legislation’s framework could be useful not only for the Greek regulators but also for the authorities in countries with a similar regulation.

The newly proposed methodology is so accurate and simple to apply that it could have far-reaching impact on practitioners. Finally, this is the first paper that examines the Greek VaR legislation framework in detail.