Download or read book Modeling and Forecasting the VIX Index written by Katja Ahoniemi and published by . This book was released on 2008 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper models the implied volatility of the Samp;P 500 index, with the aim of producing useful forecasts for option traders. Numerous time-series models of the VIX index are estimated, and daily out-of-sample forecasts are calculated from all relevant models. The directional accuracy of the forecasts is evaluated with market-timing tests. Option trades are simulated based on the forecasts, and their profitability is also used to rank the models. The results indicate that an ARIMA (1,1,1) model enhanced with exogenous regressors has predictive power regarding the directional change in the VIX index. GARCH terms are statistically significant, but do not improve forecasts. The best models predict the direction of change correctly for over 60 percent of the trading days. Out-of-sample option trading over a period of fifteen months yields positive returns when the forecasts from the best models are used as the basis for investment decisions.
Download or read book Modeling and Forecasting Volatility and Prices for SET50 Index Options written by Chanyapat Wiphatthanananthakul and published by LAP Lambert Academic Publishing. This book was released on 2018-06-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 2003, the Chicago Board Options Exchange (CBOE) made two key enhancements to the volatility index (VIX) methodology based on S&P options. The new VIX methodology seems to be based on a complicated formula to calculate expected volatility. In this book, with the use of Thailand's SET50 Index Options data, we modify the apparently complicated VIX formula to a simple relationship, which has a higher negative correlation between the VIX for Thailand (TVIX) and SET50 Index Options. We show that TVIX provides more accurate forecasts of option prices than the simple expected volatility (SEV) index, but the SEV index outperforms TVIX in forecasting expected volatility. Therefore, the SEV index would seem to be a superior tool as a hedging diversification tool because of the high negative correlation with the volatility index.
Download or read book Modeling and Forecasting Implied Volatility Indices and the Application in Risk Management and Option Trading written by and published by . This book was released on 2013 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Causal Relationship between the S P 500 and the VIX Index written by Florian Auinger and published by Springer. This book was released on 2015-02-13 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Florian Auinger highlights the core weaknesses and sources of criticism regarding the VIX Index as an indicator for the future development of financial market volatility. Furthermore, it is proven that there is no statistically significant causal relationship between the VIX and the S&P 500. As a consequence, the forecastability is not given in both directions. Obviously, there must be at least one additional variable that has a strong influence on market volatility such as emotions which, according to financial market experts, are considered to play a more and more important role in investment decisions.
Download or read book Forecasting the VIX to Improve VIX Derivatives Trading written by Chrilly Donninger and published by . This book was released on 2016 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Konstantinidi et. al. state in their broad survey of Volatility-Index forecasting: "The question whether the dynamics of implied volatility indices can be predicted has received little attention". The overall result of this and the quoted papers is: The VIX is too a very limited extend (R2 is typically 0.01) predictable, but the effect is economically not significant.This paper confirms this finding if (and only if) the forecast horizon is limited to one day. But there is no practical need to do so. One can - and usually does - hold a VIX Future or Option several trading days. It is shown that a simple model has a highly significant predictive power over a longer time horizon. The forecasts improve realistic trading strategies.
Download or read book Construction and Interpretation of Model free Implied Volatility written by Torben G. Andersen and published by . This book was released on 2007 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: The notion of model-free implied volatility (MFIV), constituting the basis for the highly publicized VIX volatility index, can be hard to measure with accuracy due to the lack of precise prices for options with strikes in the tails of the return distribution. This is reflected in practice as the VIX index is computed through a tail-truncation which renders it more compatible with the related concept of corridor implied volatility (CIV). We provide a comprehensive derivation of the CIV measure and relate it to MFIV under general assumptions. In addition, we price the various volatility contracts, and hence estimate the corresponding volatility measures, under the standard Black-Scholes model. Finally, we undertake the first empirical exploration of the CIV measures in the literature. Our results indicate that the measure can help us refine and systematize the information embedded in the derivatives markets. As such, the CIV measure may serve as a tool to facilitate empirical analysis of both volatility forecasting and volatility risk pricing across distinct future states of the world for diverse asset categories and time horizons.
Download or read book Essays on Volatility Forecasting written by Dimos S. Kambouroudis and published by . This book was released on 2012 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market volatility has been an important subject in the finance literature for which now an enormous body of research exists. Volatility modelling and forecasting have been in the epicentre of this line of research and although more than a few models have been proposed and key parameters on improving volatility forecasts have been considered, finance research has still to reach a consensus on this topic. This thesis enters the ongoing debate by carrying out empirical investigations by comparing models from the current pool of models as well as exploring and proposing the use of further key parameters in improving the accuracy of volatility modelling and forecasting. The importance of accurately forecasting volatility is paramount for the functioning of the economy and everyone involved in finance activities. For governments, the banking system, institutional and individual investors, researchers and academics, knowledge, understanding and the ability to forecast and proxy volatility accurately is a determining factor for making sound economic decisions. Four are the main contributions of this thesis. First, the findings of a volatility forecasting model comparison reveal that the GARCH genre of models are superior compared to the more 'simple' models and models preferred by practitioners. Second, with the use of backward recursion forecasts we identify the appropriate in-sample length for producing accurate volatility forecasts, a parameter considered for the first time in the finance literature. Third, further model comparisons are conducted within a Value-at-Risk setting between the RiskMetrics model preferred by practitioners, and the more complex GARCH type models, arriving to the conclusion that GARCH type models are dominant. Finally, two further parameters, the Volatility Index (VIX) and Trading Volume, are considered and their contribution is assessed in the modelling and forecasting process of a selection of GARCH type models. We discover that although accuracy is improved upon, GARCH type forecasts are still superior.
Download or read book Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility written by Eugenie M. J. H. Hol and published by . This book was released on 2000 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Volatility Modeling and Forecasting for NIFTY Stock Returns written by Gurmeet Singh and published by . This book was released on 2016 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, an attempt has been made to model the volatility of NIFTY index of National Stock Exchange (NSE) and forecast the NIFTY stock returns for short term by using daily data ranging from January, 2000, to December, 2014, which comprises 3736 data points for the analysis by using Box-Jenkins or ARIMA model. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. It is shown that ARCH family models outperform the conventional OLS models. ADF test and unit root testing is done to know the stationarity of the series, later the AR(p) and MA(q) orders are identified with the help of minimum information criterion as suggested by Hannan-Rissanen. As per the analysis, ARIMA (1,0,1) model was found to be the best fit to forecast the volatility of NIFTY stock returns. The model can be used by the investors to forecast the short run NIFTY stock returns and for making more profitable and less risky investments decision.
Download or read book Forecasting Stock Index Futures Price Volatility written by Mohammad Najand and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naive models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.
Download or read book Volatility Forecasting written by Timotheos Angelidis and published by . This book was released on 2005 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volatility prediction is the most important issue in finance, as it is the key ingredient variable in forecasting the prices of options, the VaR number and, in general, the risk that investors face. By estimating not only inter-day volatility models that capture the main characteristics of asset returns, such as the non-zero skewness, the excess kurtosis relative to that of the normal distribution and the fractional integration of the conditional variance, but also an intra-day model, we investigate their forecasting performance for three European equity indices. We find out that there is no consistent relation between the examined models and the specific purpose of volatility forecasts. Researchers cannot apply, not even for the same equity index, one model for all the forecasting purposes. However, if they want to choose one model, they must prefer an inter-day specification that accounts at least for volatility clustering and the leverage effect.
Download or read book Forecasting the Variablity of Stock Index Returns with Stochastic Volatility Models and Implied Volatility written by Eugenie Hol and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting VIX written by Stavros Antonios Degiannakis and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implied volatility index of the S&P500 is considered as a dependent variable in a fractionally integrated ARMA model, whereas volatility measures based on interday and intraday datasets are considered as explanatory variables. The next trading day's implied volatility forecasts provide positive average daily profits. All the forecasting information is provided by the VIX index itself. There is no incremental predictability from both realized volatility computed from intraday data and conditional volatility extracted from an Arch model. Hence, neither the interday volatility nor the use of intraday data yield any added value in forecasting the S&P 500 implied volatility index. However, an agent cannot utilize VIX predictions in creating abnormal returns in implied volatility futures market.
Download or read book Forecasting and Trading High Frequency Volatility on Large Indices written by Fei Liu and published by . This book was released on 2018 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present paper analyzes the forecastability and tradability of volatility on the large S&P500 index and the liquid SPY ETF, VIX index and VXX ETN. Even though there is already a huge array of literature on forecasting high frequency volatility, most publications only evaluate the forecast in terms of statistical errors. In practice, this kind of analysis is only a minor indication of the actual economic significance of the forecast that has been developed. For this reason, in our approach, we also include a test of our forecast through trading an appropriate volatility derivative. As a method we use parametric and artificial intelligence models. We also combine these models in order to achieve a hybrid forecast. We report that the results of all three model types are of similar quality. However, we observe that artificial intelligence models are able to achieve these results with a shorter input time frame and the errors are uniformly lower comparing with the parametric one. Similarly, the chosen models do not appear to differ much while the analysis of trading efficiency is performed. Finally, we notice that Sharpe ratios tend to improve for the longer forecast horizon.
Download or read book Generalized Correlation Measures of Causality and Forecasts of the VIX Using Non Linear Models written by David E. Allen and published by . This book was released on 2018 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper features an analysis of causal relations between the VIX, S&P500, and the realised volatility (RV) of the S&P500 sampled at 5 minute intervals, plus the application of an Artificial Neural Network (ANN) model to forecast the VIX. Causal relations are analysed using the recently developed concept of general correlation Zheng et al. (2012) and Vinod (2017). The neural network analysis is performed using the Group Method of Data Handling (GMDH) approach. The results suggest that causality runs from lagged daily RV and lagged continuously compounded return on the S&P500 index to the VIX. Out of sample tests suggest an ANN model can successfully predict the VIX using lagged RV and lagged S&P500 Index continuously compounded returns as inputs.
Download or read book The Ability of VIX Futures to Predict S P 500 Volatility written by Peter Williams and published by . This book was released on 2018 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the ability of futures on the CBOE Volatility Index (VIX) to predict realized S&P 500 volatility up to seven months into the future. These forecasts are found to be significantly biased. The imposition of a priori theoretically motivated restrictions can substantially improve forecast accuracy, especially when the VIX futures are augmented with the variance risk premium. When VIX futures are compared with out-of-sample forecasts from a GJR-GARCH model, the VIX-based forecasts are found to robustly outperform during periods of high volatility. In more normal states this out-performance is less significant but still present.
Download or read book Candlestick Forecasting for Investments written by Haibin Xie and published by Routledge. This book was released on 2021-03-11 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Candlestick charts are often used in speculative markets to describe and forecast asset price movements. This book is the first of its kind to investigate candlestick charts and their statistical properties. It provides an empirical evaluation of candlestick forecasting. The book proposes a novel technique to obtain the statistical properties of candlestick charts. The technique, which is known as the range decomposition technique, shows how security price is approximately logged into two ranges, i.e. technical range and Parkinson range. Through decomposition-based modeling techniques and empirical datasets, the book investigates the power of, and establishes the statistical foundation of, candlestick forecasting.
