The volatility has been one of the cores of the financial theory research, in addition to the stock markets and the futures market are an important part of modern financial markets. Forecast volatility of the stock market and oil futures market is an important part of the theory of financial markets research.

As OPEC has loosened its grip over the past ten years, the oil market has been rocked by wild price swings, the likes of which haven't been seen for eight decades. Crafting an engrossing journey from the gushing Pennsylvania oil fields of the 1860s to today's fraught and fractious Middle East, Crude Volatility explains how past periods of stability and volatility in oil prices help us understand the new boom-bust era. Oil's notorious volatility has always been considered a scourge afflicting not only the oil industry but also the broader economy and geopolitical landscape; Robert McNally makes sense of how oil became so central to our world and why it is subject to such extreme price fluctuations. Tracing a history marked by conflict, intrigue, and extreme uncertainty, McNally shows how—even from the oil industry's first years—wild and harmful price volatility prompted industry leaders and officials to undertake extraordinary efforts to stabilize oil prices by controlling production. Herculean market interventions—first, by Rockefeller's Standard Oil, then, by U.S. state regulators in partnership with major international oil companies, and, finally, by OPEC—succeeded to varying degrees in taming the beast. McNally, a veteran oil market and policy expert, explains the consequences of the ebbing of OPEC's power, debunking myths and offering recommendations—including mistakes to avoid—as we confront the unwelcome return of boom and bust oil prices.

Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.

How much does speculation contribute to oil price volatility? We revisit this contentious question by estimating a sign-restricted structural vector autoregression (SVAR). First, using a simple storage model, we show that revisions to expectations regarding oil market fundamentals and the effect of mispricing in oil derivative markets can be observationally equivalent in a SVAR model of the world oil market à la Kilian and Murphy (2013), since both imply a positive co-movement of oil prices and inventories. Second, we impose additional restrictions on the set of admissible models embodying the assumption that the impact from noise trading shocks in oil derivative markets is temporary. Our additional restrictions effectively put a bound on the contribution of speculation to short-term oil price volatility (lying between 3 and 22 percent). This estimated short-run impact is smaller than that of flow demand shocks but possibly larger than that of flow supply shocks.

The relationship between the price of oil and the level of economic activity is a fundamental issue in macroeconomics. There is an ongoing debate in the literature about whether positive oil price shocks cause recessions in the United States (and other oil-importing countries), and although there exists a vast empirical literature that investigates the effects of oil price shocks, there are relatively few studies that investigate the direct effects of uncertainty about oil prices on the real economy. The book uses recent advances in macroeconomics and financial economics to investigate the effects of oil price shocks and uncertainty about the price of oil on the level of economic activity.

This book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.

Written by one of the leading authorities in market microstructure research, this book provides a comprehensive guide to the theoretical work in this important area of finance.

A volume that celebrates and develops the work of Nobel Laureate Robert Engle, it includes original contributions from some of the world's leading econometricians that further Engle's work in time series economics

This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.