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Volatility Forecasts (volatility + forecast)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	95%

Selected Abstracts

VOLATILITY FORECASTS, TRADING VOLUME, AND THE ARCH VERSUS OPTION-IMPLIED VOLATILITY TRADE-OFF

THE JOURNAL OF FINANCIAL RESEARCH, Issue 4 2005
R. Glen Donaldson
Abstract We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t,1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t,1 relative to the recent past, option-implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option-implied forward-looking estimate. [source]

Modeling and Forecasting Realized Volatility

ECONOMETRICA, Issue 2 2003
Torben G. Andersen
We provide a framework for integration of high,frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Building on the theory of continuous,time arbitrage,free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen/Dollar spot exchange rates, we find that forecasts from a simple long,memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal,normal mixture distribution produces well,calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation, and financial risk management applications. [source]

Optimal sampling frequency for volatility forecast models for the Indian stock markets

JOURNAL OF FORECASTING, Issue 1 2009
Malay Bhattacharyya
Abstract This paper evaluates the performance of conditional variance models using high-frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non-normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd. [source]

VOLATILITY FORECASTS, TRADING VOLUME, AND THE ARCH VERSUS OPTION-IMPLIED VOLATILITY TRADE-OFF

Neural network volatility forecasts

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 3-4 2007
José R. Aragonés
We analyse whether the use of neural networks can improve ,traditional' volatility forecasts from time-series models, as well as implied volatilities obtained from options on futures on the Spanish stock market index, the IBEX-35. One of our main contributions is to explore the predictive ability of neural networks that incorporate both implied volatility information and historical time-series information. Our results show that the general regression neural network forecasts improve the information content of implied volatilities and enhance the predictive ability of the models. Our analysis is also consistent with the results from prior research studies showing that implied volatility is an unbiased forecast of future volatility and that time-series models have lower explanatory power than implied volatility. Copyright © 2008 John Wiley & Sons, Ltd. [source]

The predictive value of temporally disaggregated volatility: evidence from index futures markets

JOURNAL OF FORECASTING, Issue 8 2008
Nicholas Taylor
Abstract This paper examines the benefits to forecasters of decomposing close-to-close return volatility into close-to-open (nighttime) and open-to-close (daytime) return volatility. Specifically, we consider whether close-to-close volatility forecasts based on the former type of (temporally aggregated) data are less accurate than corresponding forecasts based on the latter (temporally disaggregated) data. Results obtained from seven different US index futures markets reveal that significant increases in forecast accuracy are possible when using temporally disaggregated volatility data. This result is primarily driven by the fact that forecasts based on such data can be updated as more information becomes available (e.g., information flow from the preceding close-to-open/nighttime trading session). Finally, we demonstrate that the main findings of this paper are robust to the index futures market considered, the way in which return volatility is constructed, and the method used to assess forecast accuracy. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Forecasting volatility with noisy jumps: an application to the Dow Jones Industrial Average stocks

JOURNAL OF FORECASTING, Issue 3 2008
Basel M. A. Awartani
Abstract Empirical high-frequency data can be used to separate the continuous and the jump components of realized volatility. This may improve on the accuracy of out-of-sample realized volatility forecasts. A further improvement may be realized by disentangling the two components using a sampling frequency at which the market microstructure effect is negligible, and this is the objective of the paper. In particular, a significant improvement in the accuracy of volatility forecasts is obtained by deriving the jump information from time intervals at which the noise effect is weak. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Bias in the estimation of non-linear transformations of the integrated variance of returns

JOURNAL OF FORECASTING, Issue 7 2006
Richard D. F. Harris
Abstract Volatility models such as GARCH, although misspecified with respect to the data-generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non-linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non-linear transformations of these forecasts will be biased estimators of the same non-linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non-linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non-linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in-sample and out-of-sample forecasts of three non-linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial.,,Copyright © 2006 John Wiley & Sons, Ltd. [source]

Daily volatility forecasts: reassessing the performance of GARCH models

JOURNAL OF FORECASTING, Issue 6 2004
David G. McMillan
Abstract Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accurate measures and good forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility in-sample, they appear to provide relatively poor out-of-sample forecasts. Recent research has suggested that this relative failure of GARCH models arises not from a failure of the model but a failure to specify correctly the ,true volatility' measure against which forecasting performance is measured. It is argued that the standard approach of using ex post daily squared returns as the measure of ,true volatility' includes a large noisy component. An alternative measure for ,true volatility' has therefore been suggested, based upon the cumulative squared returns from intra-day data. This paper implements that technique and reports that, in a dataset of 17 daily exchange rate series, the GARCH model outperforms smoothing and moving average techniques which have been previously identified as providing superior volatility forecasts. Copyright © 2004 John Wiley & Sons, Ltd. [source]

An outlier robust GARCH model and forecasting volatility of exchange rate returns

JOURNAL OF FORECASTING, Issue 5 2002
Beum-Jo Park
Abstract Since volatility is perceived as an explicit measure of risk, financial economists have long been concerned with accurate measures and forecasts of future volatility and, undoubtedly, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used for doing so. It appears, however, from some empirical studies that the GARCH model tends to provide poor volatility forecasts in the presence of additive outliers. To overcome the forecasting limitation, this paper proposes a robust GARCH model (RGARCH) using least absolute deviation estimation and introduces a valuable estimation method from a practical point of view. Extensive Monte Carlo experiments substantiate our conjectures. As the magnitude of the outliers increases, the one-step-ahead forecasting performance of the RGARCH model has a more significant improvement in two forecast evaluation criteria over both the standard GARCH and random walk models. Strong evidence in favour of the RGARCH model over other competitive models is based on empirical application. By using a sample of two daily exchange rate series, we find that the out-of-sample volatility forecasts of the RGARCH model are apparently superior to those of other competitive models. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Forecasting high-frequency financial data with the ARFIMA,ARCH model

JOURNAL OF FORECASTING, Issue 7 2001
Michael A. Hauser
Abstract Financial data series are often described as exhibiting two non-standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA,ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid (1987). For daily data on the Swiss 1-month Euromarket interest rate during the period 1986,1989, the ARFIMA,ARCH (5,d,2/4) model with non-integer d is selected by AIC. Model-based out-of-sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (, > 40). Regarding volatility forecasts, however, the selected ARFIMA,ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd. [source]

VOLATILITY FORECASTS, TRADING VOLUME, AND THE ARCH VERSUS OPTION-IMPLIED VOLATILITY TRADE-OFF

Examination of long-term bond iShare option selling strategies

THE JOURNAL OF FUTURES MARKETS, Issue 5 2010
David P. SimonArticle first published online: 31 JUL 200
This article examines volatility trades in Lehman Brothers 20+ Year US Treasury Index iShare (TLT) options from July 2003 through May 2007. Unconditionally selling front contract strangles and straddles and holding for one month is highly profitable after transactions costs. Short-term option selling strategies are enhanced when implied volatility is high relative to time series volatility forecasts. Risk management strategies such as stop loss orders detract from profitability, while take profit orders have only modest favorable effects on profitability. Overall, the results demonstrate that TLT option selling strategies offered attractive risk-return tradeoffs over the sample period. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:465,489, 2010 [source]

Who knows more about future currency volatility?

THE JOURNAL OF FUTURES MARKETS, Issue 3 2009
Charlie Charoenwong
We use four currency pairs from October 1, 2001 to September 29, 2006 to compare the predictive power of the implied volatility derived from currency option prices that are traded on the Philadelphia Stock Exchange (PHLX), Chicago Mercantile Exchange (CME), and over-the-counter market (OTC). Among the competing implied volatility forecasts, OTC-implied volatility subsumes the information content of PHLX- and CME-implied volatility. Consistent with extant studies our result also shows that the implied volatility provides more information about future volatility,regardless of whether it is from the OTC, PHLX, or CME markets,than time series based volatility. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:270,295, 2009 [source]

Implied volatility forecasts in the grains complex

THE JOURNAL OF FUTURES MARKETS, Issue 10 2002
David P. Simon
This article finds that the implied volatilities of corn, soybean, and wheat futures options 4 weeks before option expiration have significant predictive power for the underlying futures contract return volatilities through option expiration from January 1988 through September 1999. These implied volatilities also encompass the information in out-of-sample seasonal Glosten, Jagannathan, and Runkle (GJR;1993) volatility forecasts. Evidence also demonstrates that when corn-implied volatility rises relative to out-of-sample seasonal GJR volatility forecasts, implied volatility substantially overpredicts realized volatility. However, simulations of trading rules that involve selling corn option straddles when corn-implied volatility is high relative to out-of-sample GJR volatility forecasts indicate that none of the trading rules would have been significantly profitable. This finding suggests that these options are not necessarily overpriced. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:959,981, 2002 [source]

Forecasting stock index volatility

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2001
Riccardo Bramante
Abstract Accurate volatility forecasting is the key to successful risk analysis. In fact, volatility forecasts lie at the centre of many financial systems, such as value at risk modelling and pricing of derivative securities. This paper is concerned with how to construct stock index volatility predictors using the returns histories of the stocks that define the Index. Specifically, our approach presupposes that the total volatility of the index returns can be explained by the volatility of the related components. Copyright © 2001 John Wiley & Sons, Ltd. [source]