Download or read book Modeling and Forecasting S P 500 Volatility written by Martin Martens and published by . This book was released on 2007 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model to realized volatilities of the Samp;P 500 stock index and three exchange rates produces forecasts that clearly improve upon the ones obtained from a linear ARFIMA model and from conventional time-series models based on daily returns, treating volatility as a latent variable.

Download or read book A Test of Efficiency for the S P 500 Index Option Market Using Variance Forecasts written by Jaesun Noh and published by . This book was released on 1993 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: To forecast future option prices, autoregressive models of implied volatility derived from observed option prices are commonly employed [see Day and Lewis (1990), and Harvey and Whaley (1992)]. In contrast, the ARCH model proposed by Engle (1982) models the dynamic behavior in volatility, forecasting future volatility using only the return series of an asset. We assess the performance of these two volatility prediction models from S&P 500 index options market data over the period from September 1986 to December 1991 by employing two agents who trade straddles, each using one of the two different methods of forecast. Straddle trading is employed since a straddle does not need to be hedged. Each agent prices options according to her chosen method of forecast, buying (selling) straddles when her forecast price for tomorrow is higher (lower) than today's market closing price, and at the end of each day the rates of return are computed. We find that the agent using the GARCH forecast method earns greater profit than the agent who uses the implied volatility regression (IVR) forecast model. In particular, the agent using the GARCH forecast method earns a profit in excess of a cost of $0.25 per straddle with the near-the-money straddle trading.

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 Modeling and Forecasting S P 500 Volatility written by Martin Prudentius Eleonora Martens and published by . This book was released on 2004 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting Volatility in the Financial Markets written by John L. Knight and published by Butterworth-Heinemann. This book was released on 2002 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modeling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.

Download or read book Forecasting the Volatility of S written by Charles Good and published by . This book was released on 2009 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study examines the ability of different models to predict the monthly volatility of the S & P 500 (SPX) index. Each model (ARIMA [2,0,2], GARCH [1,1] and EGARCH [1,1]) will forecast volatility with parameters that are initially estimated from an in-sample period (1/3/2000 to 12/30/2005) then re-estimate the parameters from an out-of-sample period (1/3/2006 to 12/31/2008) on a monthly-basis. The forecasted volatility is benclunarked against a historic volatility measure and the realized volatility--a value derived using the standard deviation of the log difference return data that serves as a proxy for the S & P 500 volatility. Overall, the EGARCH (1,1) model provided the most accurate forecasts during the forecast period of 1/3/2006 to 12/31/2008.

Download or read book ARCH Models and Financial Applications written by Christian Gourieroux and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classical ARMA models have limitations when applied to the field of financial and monetary economics. Financial time series present nonlinear dynamic characteristics and the ARCH models offer a more adaptive framework for this type of problem. This book surveys the recent work in this area from the perspective of statistical theory, financial models, and applications and will be of interest to theorists and practitioners. From the view point of statistical theory, ARCH models may be considered as specific nonlinear time series models which allow for an exhaustive study of the underlying dynamics. It is possible to reexamine a number of classical questions such as the random walk hypothesis, prediction interval building, presence of latent variables etc., and to test the validity of the previously studied results. There are two main categories of potential applications. One is testing several economic or financial theories concerning the stocks, bonds, and currencies markets, or studying the links between the short and long run. The second is related to the interventions of the banks on the markets, such as choice of optimal portfolios, hedging portfolios, values at risk, and the size and times of block trading.

Download or read book Forecasting Volatility in the Presence of Limits to Arbitrage written by Lu Hong and published by . This book was released on 2014 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop a novel model to forecast the volatility of S&P 500 futures returns by considering measures of limits to arbitrage. When arbitrageurs face constraints on their trading strategies, option prices can become disconnected from fundamentals, resulting in a distortion that reflects the limits to arbitrage. The corresponding market based implied volatility will therefore also contain these distortions. Our contributions are both conceptual and empirical. Conceptually, the limits to arbitrage framework can shed light on relative asset prices as exemplified by this particular study. Empirically, our volatility forecasting model explains 71% of the variation in realized volatility, a substantial improvement over a naive forecast based only on lagged realized volatility, which produces an R2 of 53%.

Download or read book Modelling and forecasting stock return volatility and the term structure of interest rates written by Michiel de Pooter and published by Rozenberg Publishers. This book was released on 2007 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.

Download or read book A Stochastic Volatility Model with Random Level Shifts written by Zhongjun Qu and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical findings related to the time series properties of stock returns volatility indicate autocorrelations that decay slowly at long lags. In light of this, several long-memory models have been proposed. However, the possibility of level shifts has been advanced as a possible explanation for the appearance of long-memory and there is growing evidence suggesting that it may be an important feature of stock returns volatility. Nevertheless, it remains a conjecture that a model incorporating random level shifts in variance can explain the data well and produce reasonable forecasts. We show that a very simple stochastic volatility model incorporating both a random level shift and a short-memory component indeed provides a better in-sample fit of the data and produces forecasts that are no worse, and sometimes better, than standard stationary short and long-memory models. We use a Bayesian method for inference and develop algorithms to obtain the posterior distributions of the parameters and the smoothed estimates of the two latent components. We apply the model to daily S&P 500 and NASDAQ returns over the period 1980.1-2005.12. Although the occurrence of a level shift is rare, about once every two years, the level shift component clearly contributes most to the total variation in the volatility process. The half-life of a typical shock from the short-memory component is very short, on average between 8 and 14 days. We also show that, unlike common stationary short or long-memory models, our model is able to replicate keys features of the data. For the NASDAQ series, it forecasts better than a standard stochastic volatility model, and for the S&P 500 index, it performs equally well.

Download or read book Forecasting Realized Volatility with Changes of Regimes written by Giampiero M. Gallo and published by . This book was released on 2014 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Realized volatility of financial time series generally shows a slow-moving average level from the early 2000s to recent times, with alternating periods of turmoil and quiet. Modeling such a pattern has been variously tackled in the literature with solutions spanning from long-memory, Markov switching and spline interpolation. In this paper, we explore the extension of Multiplicative Error Models to include a Markovian dynamics (MS-MEM). Such a model is able to capture some sudden changes in volatility following an abrupt crisis and to accommodate different dynamic responses within each regime. The model is applied to the realized volatility of the S&P500 index: next to an interesting interpretation of the regimes in terms of market events, the MS-MEM has better in-sample fitting capability and achieves good out-of-sample forecasting performances relative to alternative specifications.

Download or read book The Comparison of Forecasting Performance of Historical Volatility Versus Realized Volatility written by Linkai Huang and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: When forecasting stock market volatility with a standard volatility method (GARCH), it is common that the forecast evaluation criteria often suggests that the realized volatility (the sum of squared high-frequency returns) has a better prediction performance compared to the historical volatility (extracted from the close-to-close return). Since many extensions of the GARCH model have been developed, we follow the previous works to compare the historical volatility with many new GARCH family models (i.e., EGARCH, TGARCH, and APARCH model) and realized volatility with the ARMA model. Our analysis is based on the S&P 500 index from August 1st, 2018 to February 1st, 2019 (127 trading days), and the data has been separated into an estimation period (90 trading days) and an evaluation period (37 trading days). In the evaluation period, by taking realized volatility as the proxy of the true volatility, our empirical result shows that the realized volatility with ARMA model provides more accurate predictions, compared to the historical volatility with the GARCH family models.

Download or read book Forecasting Realized Volatility with Linear and Nonlinear Univariate Models written by Michael McAleer and published by . This book was released on 2010 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predictable Dynamics in the S P 500 Index Options Implied Volatility Surface written by Sílvia Gonçalves and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: One key stylized fact in the empirical option pricing literature is the existence of an implied volatility surface (IVS). The usual approach consists of fitting a linear model linking the implied volatility to the time to maturity and the moneyness, for each cross section of options data. However, recent empirical evidence suggests that the parameters characterizing the IVS change over time. In this paper, we study whether the resulting predictability patterns in the IVS coefficients may be exploited in practice. We propose a two-stage approach to modeling and forecasting the Samp;P 500 index options IVS. In the first stage, we model the surface along the cross-sectional moneyness and time-to-maturity dimensions, similarly to Dumas, et. al., (1998). In the second-stage, we model the dynamics of the cross-sectional first-stage implied volatility surface coefficients by means of vector autoregression models. We find that not only the Samp;P 500 implied volatility surface can be successfully modeled, but also that its movements over time are highly predictable in a statistical sense. We then examine the economic significance of this statistical predictability with mixed findings. Whereas profitable delta-hedged positions can be set up that exploit the dynamics captured by the model under moderate transaction costs and when trading rules are selective in terms of expected gains from the trades, most of this profitability disappears when we increase the level of transaction costs and trade multiple contracts off wide segments of the IVS. This suggests that predictability of the time-varying Samp;P 500 implied volatility surface may be not inconsistent with market efficiency.

Download or read book Forecasting Volatility and Option Prices of the S P 500 Index written by Jaesun Noh and published by . This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Forecasting in the Presence of Structural Breaks and Model Uncertainty written by David E. Rapach and published by Emerald Group Publishing. This book was released on 2008-02-29 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting in the presence of structural breaks and model uncertainty are active areas of research with implications for practical problems in forecasting. This book addresses forecasting variables from both Macroeconomics and Finance, and considers various methods of dealing with model instability and model uncertainty when forming forecasts.