In this paper we model country-specific equity market return and association between country-specific equity market volatility and that of the world market in the downside and conventional asset pricing frameworks. For this a Factor- ARCH type process is adopted where world market risk (beta) is estimated in the mean equation and exposure of country-specific market volatility to world market volatility (volatility beta) is estimated in the variance equation. Generally, the beta is estimated higher for developed markets than for emerging markets and the reverse is observed in volatility beta. Even though the two types of betas are positive and significant, a cross-sectional analysis reveals that volatility beta is not priced. We observe these results when the analysis is carried out from an international investor perspective. When we repeat the analysis in sub-periods delineated via breakpoints in the world market return series and with alternative specifications of the variance equation our findings remain largely unchanged.

We present results of an experiment on expectation formation in an asset market. Participants to our experiment must provide forecasts of the stock future return to computerized utility-maximizing investors, and are rewarded according to how well their forecasts perform in the market. In the Baseline treatment participants must forecast the stock return one period ahead; in the Volatility treatment, we also elicit subjective confidence intervals of forecasts, which we take as a measure of perceived volatility. The realized asset price is derived from a Walrasian market equilibrium equation with non-linear feedback from individual forecasts. Our experimental markets exhibit high volatility, fat tails and other properties typical of real financial data. Eliciting confidence intervals for predictions has the effect of reducing price fluctuations and increasing subjects’ coordination on a common prediction strategy. -- Experimental economics ; Expectations ; Coordination ; Volatility ; Asset pricing

In Volatility Trading, Sinclair offers you a quantitative model for measuring volatility in order to gain an edge in your everyday option trading endeavors. With an accessible, straightforward approach. He guides traders through the basics of option pricing, volatility measurement, hedging, money management, and trade evaluation. In addition, Sinclair explains the often-overlooked psychological aspects of trading, revealing both how behavioral psychology can create market conditions traders can take advantage of-and how it can lead them astray. Psychological biases, he asserts, are probably the drivers behind most sources of edge available to a volatility trader. Your goal, Sinclair explains, must be clearly defined and easily expressed-if you cannot explain it in one sentence, you probably aren't completely clear about what it is. The same applies to your statistical edge. If you do not know exactly what your edge is, you shouldn't trade. He shows how, in addition to the numerical evaluation of a potential trade, you should be able to identify and evaluate the reason why implied volatility is priced where it is, that is, why an edge exists. This means it is also necessary to be on top of recent news stories, sector trends, and behavioral psychology. Finally, Sinclair underscores why trades need to be sized correctly, which means that each trade is evaluated according to its projected return and risk in the overall context of your goals. As the author concludes, while we also need to pay attention to seemingly mundane things like having good execution software, a comfortable office, and getting enough sleep, it is knowledge that is the ultimate source of edge. So, all else being equal, the trader with the greater knowledge will be the more successful. This book, and its companion CD-ROM, will provide that knowledge. The CD-ROM includes spreadsheets designed to help you forecast volatility and evaluate trades together with simulation engines.

Market Volatility proposes an innovative theory, backed by substantial statistical evidence, on the causes of price fluctuations in speculative markets. It challenges the standard efficient markets model for explaining asset prices by emphasizing the significant role that popular opinion or psychology can play in price volatility. Why does the stock market crash from time to time? Why does real estate go in and out of booms? Why do long term borrowing rates suddenly make surprising shifts? Market Volatility represents a culmination of Shiller's research on these questions over the last dozen years. It contains reprints of major papers with new interpretive material for those unfamiliar with the issues, new papers, new surveys of relevant literature, responses to critics, data sets, and reframing of basic conclusions. Included is work authored jointly with John Y. Campbell, Karl E. Case, Sanford J. Grossman, and Jeremy J. Siegel. Market Volatility sets out basic issues relevant to all markets in which prices make movements for speculative reasons and offers detailed analyses of the stock market, the bond market, and the real estate market. It pursues the relations of these speculative prices and extends the analysis of speculative markets to macroeconomic activity in general. In studies of the October 1987 stock market crash and boom and post-boom housing markets, Market Volatility reports on research directly aimed at collecting information about popular models and interpreting the consequences of belief in those models. Shiller asserts that popular models cause people to react incorrectly to economic data and believes that changing popular models themselves contribute significantly to price movements bearing no relation to fundamental shocks.

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

We investigate the time series behavior of idiosyncratic volatility and its role in asset pricing in China. We find no evidence of a long-term trend in the time series behavior of idiosyncratic volatility. Idiosyncratic volatility in China is best characterized by an autoregressive process with regime shifts that coincide with structural market reforms. We also document evidence of a negative idiosyncratic volatility effect in China with anecdotal evidence suggesting that it could be driven by investor preference for high idiosyncratic volatility stocks.