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Author: Stephen Satchell Publisher: Elsevier ISBN: 0080471420 Category : Business & Economics Languages : en Pages : 428
Book Description
Forecasting Volatility in the Financial Markets, Third Edition 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 modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey Leading thinkers present newest research on volatility forecasting International authors cover a broad array of subjects related to volatility forecasting Assumes basic knowledge of volatility, financial mathematics, and modelling
Author: Stephen Satchell Publisher: Elsevier ISBN: 0080471420 Category : Business & Economics Languages : en Pages : 428
Book Description
Forecasting Volatility in the Financial Markets, Third Edition 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 modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey Leading thinkers present newest research on volatility forecasting International authors cover a broad array of subjects related to volatility forecasting Assumes basic knowledge of volatility, financial mathematics, and modelling
Author: Ruojun Wu Publisher: ISBN: Category : Bayesian statistical decision theory Languages : en Pages : 137
Book Description
This dissertation studies the effect of parameter uncertainty on the return predictability and volatility of the stock market. The first two chapters focus on the decomposition of market volatility, and the third chapter studies the return predictability. When facing imperfect information, the investors tend to form a learning scheme that encompasses both historical data and prior beliefs. In the variance decomposition framework, the introducing of learning directly impacts the way that return forecasts are revised and consequently the relative component of market volatility based on these forecasts, namely the price movements from revision on future discount rates and those from future cash flows. According to the empirical study in Chapter 1, the former is not necessarily the major driving force of market volatility, which provides an alternative view on what moves stock prices. Learning is modeled and estimated by Bayesian method. Chapter 2 follows the topic in Chapter 1 and studies the role of persistent state variables in return decomposition in order to provide more robust inference on variance decomposition. In Chapter 3 we propose to utilize theoretical constraints to help predict market returns when in sample data is very noisy and creates model uncertainty for the investors. The constraints are also incorporated by Bayesian method. We show in the out-of-sample forecast experiment that models with theoretical constraints produce better forecasts.
Author: Florian Auinger Publisher: Springer ISBN: 3658089695 Category : Business & Economics Languages : en Pages : 102
Book Description
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.
Author: William T Ziemba Publisher: World Scientific ISBN: 9813223863 Category : Business & Economics Languages : en Pages : 309
Book Description
'Overall, the book provides an interesting and useful synthesis of the authors’ research on the predictions of stock market crashes. The book can be recommended to anyone interested in the Bond Stock Earnings Yield Differential model, and similar methods to predict crashes.'Quantitative FinanceThis book presents studies of stock market crashes big and small that occur from bubbles bursting or other reasons. By a bubble we mean that prices are rising just because they are rising and that prices exceed fundamental values. A bubble can be a large rise in prices followed by a steep fall. The focus is on determining if a bubble actually exists, on models to predict stock market declines in bubble-like markets and exit strategies from these bubble-like markets. We list historical great bubbles of various markets over hundreds of years.We present four models that have been successful in predicting large stock market declines of ten percent plus that average about minus twenty-five percent. The bond stock earnings yield difference model was based on the 1987 US crash where the S&P 500 futures fell 29% in one day. The model is based on earnings yields relative to interest rates. When interest rates become too high relative to earnings, there almost always is a decline in four to twelve months. The initial out of sample test was on the Japanese stock market from 1948-88. There all twelve danger signals produced correct decline signals. But there were eight other ten percent plus declines that occurred for other reasons. Then the model called the 1990 Japan huge -56% decline. We show various later applications of the model to US stock declines such as in 2000 and 2007 and to the Chinese stock market. We also compare the model with high price earnings decline predictions over a sixty year period in the US. We show that over twenty year periods that have high returns they all start with low price earnings ratios and end with high ratios. High price earnings models have predictive value and the BSEYD models predict even better. Other large decline prediction models are call option prices exceeding put prices, Warren Buffett's value of the stock market to the value of the economy adjusted using BSEYD ideas and the value of Sotheby's stock. Investors expect more declines than actually occur. We present research on the positive effects of FOMC meetings and small cap dominance with Democratic Presidents. Marty Zweig was a wall street legend while he was alive. We discuss his methods for stock market predictability using momentum and FED actions. These helped him become the leading analyst and we show that his ideas still give useful predictions in 2016-2017. We study small declines in the five to fifteen percent range that are either not expected or are expected but when is not clear. For these we present methods to deal with these situations.The last four January-February 2016, Brexit, Trump and French elections are analzyed using simple volatility-S&P 500 graphs. Another very important issue is can you exit bubble-like markets at favorable prices. We use a stopping rule model that gives very good exit results. This is applied successfully to Apple computer stock in 2012, the Nasdaq 100 in 2000, the Japanese stock and golf course membership prices, the US stock market in 1929 and 1987 and other markets. We also show how to incorporate predictive models into stochastic investment models.
Author: Kpate Adjaoute Publisher: ISBN: Category : Languages : en Pages :
Book Description
This study compares the performance of the ISD, the GARCH (1,1), the historical volatility estimates and of two lagged trading volume measures for predicting the Swiss Stock Market Index's (SMI) volatility. The ISD has a superior daily informational content than the GARCH(1,1) estimate and retains unbiased but decreasing explanatory power over up to 20 days ahead horizons. Mean and spread daily volume measures play a significant correcting role when forecasting stock market volatility over daily and longer intervals respectively and clearly dominate the GARCH (1,1) forecasts. Their significance emphasizes heterogeneous horizon traders' influence on the SMI volatility time series properties.
Author: Anthony J. Richards Publisher: International Monetary Fund ISBN: Category : Business & Economics Languages : en Pages : 52
Book Description
This paper examines the evidence for the common assertion that the volatility of emerging stock markets has increased as a result of the liberalization of markets. A range of measures suggests that there has been no generalized increase in volatility in recent years; indeed, it appears that volatility may have tended to fall rather than rise on average. The paper also tests for the predictability of long-horizon returns in emerging markets. While there is evidence for positive autocorrelation in returns at horizons of one or two quarters, the autocorrelations appear to turn negative at horizons of a year or more. However, the magnitude of the apparent return reversals is not that much larger than reversals in some mature markets. One interpretation of the results would be that emerging markets have not consistently been subject to fads or bubbles, or at least no more so than in some industrial countries. In general, the liberalization and broadening of emerging markets should lead to a reduction in return volatility as risk is spread among a larger number of investors.
Author: Greg N. Gregoriou Publisher: CRC Press ISBN: 1420099558 Category : Business & Economics Languages : en Pages : 654
Book Description
Up-to-Date Research Sheds New Light on This Area Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in devel
Author: Xingyi Li Publisher: ISBN: Category : Languages : en Pages : 35
Book Description
Recent literature on stock return predictability suggests that it varies substantially across economic states being strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state dependent. In particular, using a large data set of high-frequency data on individual stocks and a few popular time-series volatility models, in this paper we comprehensively examine how volatility forecastability varies across bull and bear states of the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state than when it is in a bull state. In addition, the volatility forecast accuracy is higher and forecast bias is lower when the market is in a bear state. Our study concludes that the stock volatility predictability is strongest during bad economic times proxied by bear market states.
Author: Robert A. Haugen Publisher: Pearson ISBN: Category : Business & Economics Languages : en Pages : 170
Book Description
It is now abundantly clear that stock volatility is a contagious disease that spreads virulently from market to market around the world. Price changes in one market drive subsequent price changes in that market as well as in others. In Beast, Haugen makes a compelling case for the fact that even under normal conditions, fully 80 percent of stock volatility is price driven. Moreover, this volatility is far from benign. It acts to reduce the level of investment spending and constitutes a significant and permanent drag on economic growth. Price-driven volatility is unstable. Dramatic and unpredictable explosions in price-driven volatility can send stock markets in a downward spiral and cause significant disruptions in economic activity. Haugen argues that this indeed happened in 1929 and 1930. If volatility in Asian markets persists, it can easily become the source of the problem rather than merely a symptom.
Author: Stephen Satchell
Author: Stephen Satchell
Author: Ruojun Wu
Author: Florian Auinger
Author: William T Ziemba
Author: Kpate Adjaoute
Author: Anthony J. Richards
Author: Greg N. Gregoriou
Author: Clive William John Granger
Author: Xingyi Li
Author: Robert A. Haugen