Volatility Discovery Across Stock Limit Order Book and Options Markets

Foucault [Journal of Financial Markets, 2, 99–134, 1999] provides a theoretical basis for how stock price volatility influences the aggressiveness of limit order traders. I investigate volatility discovery across stock limit order book and options markets using a broad panel of NYSE‐listed stocks from November 2007 to January 2008 and find strong evidence that, as predicted, the aggressiveness of the stock limit order book and option volatility trading Granger‐cause each other. Further, I find that the aggressiveness of the stock limit order book and option volatility trading are inversely related, which is both statistically and economically significant. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 34:934–956, 2014Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108316/1/fut21628.pd

The Market for Volatility Trading; VIX Futures

This paper analyses the new market for trading volatility; VIX futures. We first use market data to establish the relationship between VIX futures prices and the index itself. We observe that VIX futures and VIX are highly correlated; the term structure of VIX futures price is upward sloping while the term structure of VIX futures volatility is downward sloping. To establish a theoretical relationship between VIX futures and VIX, we model the instantaneous variance using a simple square root mean-reverting process. Using daily calibrated variance parameters and VIX, the model gives good predictions of VIX futures prices. These parameter estimates could be used to price VIX options

Delta-neutral volatility trading with intra-day prices: an application to options on the DAX

This paper evaluates the profitability of applying four different volatility forecasting models to the trading of straddles on the German stock market index DAX. Special care has been taken to use simultaneous intra-day prices and realistic transaction costs. Furthermore, straddle positions were evaluated on a daily basis to preserve delta neutrality. The four models applied in this paper are: historical volatility, two ARCH models, and an autoregressive model for the volatility index. VDAX. The ARCH models perform best in generating profits for market makers. Forecasts based on historical volatility also produce statistically and economically significant profits over the two-year simulation period of 1993 and 1994. In general, a filter1rule with a small filter of0.5 per cent produces the best results for both the ARCH models and historical volatility. However, the VDAX-AR model generates much lower and usually insignificant profits, and for some filter rules this model even has cumulative losses for market makers. For non-market-makers and non-members of exchange, however, larger transaction\costs imply that no significant profits can be gained with any model of volatility forecasts. --

Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets

I analyze company news from Reuters with the 'General Inquirer' and relate measures of positive sentiment, negative sentiment and disagreement to abnormal stock returns, stock and option trading volume, the volatility spread and the CDS spread. I test hypotheses derived from market microstructure models. Consistent with these models, sentiment and disagreement are strongly related to trading volume. Moreover, sentiment and disagreement might be used to predict stock returns, trading volume and volatility. Trading strategies based on positive and negative sentiment are profitable if the transaction costs are moderate, indicating that stock markets are not fully efficient.Content Analysis, Company News, Market Microstructure

Detrended cross-correlations between returns, volatility, trading activity, and volume traded for the stock market companies

We consider a few quantities that characterize trading on a stock market in a fixed time interval: logarithmic returns, volatility, trading activity (i.e., the number of transactions), and volume traded. We search for the power-law cross-correlations among these quantities aggregated over different time units from 1 min to 10 min. Our study is based on empirical data from the American stock market consisting of tick-by-tick recordings of 31 stocks listed in Dow Jones Industrial Average during the years 2008-2011. Since all the considered quantities except the returns show strong daily patterns related to the variable trading activity in different parts of a day, which are the best evident in the autocorrelation function, we remove these patterns by detrending before we proceed further with our study. We apply the multifractal detrended cross-correlation analysis with sign preserving (MFCCA) and show that the strongest power-law cross-correlations exist between trading activity and volume traded, while the weakest ones exist (or even do not exist) between the returns and the remaining quantities. We also show that the strongest cross-correlations are carried by those parts of the signals that are characterized by large and medium variance. Our observation that the most convincing power-law cross-correlations occur between trading activity and volume traded reveals the existence of strong fractal-like coupling between these quantities

Performativity and Financial Markets: Option Pricing in the Late 19th Century

The paper revisits the performativity thesis in economics stressing the plural character of knowledge, which includes not only scientific models but also every form of practical knowledge that systematizes the visible and the articulable experience of economic agents. To highlight the point, the paper examines the pricing of options in London in the late 19th century, long before the academic origin of modern option pricing models. The pamphlets of the time are valuable archives of existing option transactions performed on the basis of systematic practical techniques widely established among investors

Analyzing stock market movements using Twitter sentiment analysis

In this paper we investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed sentiments for more than 4 million tweets between June 2010 to July 2011 for DJIA, NASDAQ-100 and 13 other big cap technological stocks. Our results show high correlation (up to 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger's Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of Rsquare (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76% for Dow Jones Industrial Average (DJIA)

The dynamics of the volatility – trading volume relationship: New evidence from developed and emerging markets

This paper empirically investigates whether there is an evolution in the relation between stock market trading volume and volatility in 23 developed and 15 emerging markets. To answer this question, we develop a dynamic application of the TARCH (1, 1) model and first prove that the relationship is variable through time. Then, we focus our analysis on three major financial events, namely the Asian Crisis, the Dot Com bubble burst and the Subprime crisis. We find that the explanatory power of volume is greater during these periods. Finally, we show that the sign of the relationship cannot be clearly set for a specific country or sub group of developed or emerging markets.Mixture of distribution hypothesis, TARCH model, Conditional variance, Trading volume

Volatility Investing with Variance Swaps

Traditionally volatility is viewed as a measure of variability, or risk, of an underlying asset. However recently investors began to look at volatility from a different angle. It happened due to emergence of a market for new derivative instruments - variance swaps. In this paper first we introduse the general idea of the volatility trading using variance swaps. Then we describe valuation and hedging methodology for vanilla variance swaps as well as for the 3-rd generation volatility derivatives: gamma swaps, corridor variance swaps, conditional variance swaps. Finally we show the results of the performance investigation of one of the most popular volatility strategies - dispersion trading. The strategy was implemented using variance swaps on DAX and its constituents during the 5-years period from 2004 to 2008.Conditional Variance Swap, Corridor Variance Swap, Dispersion Trading, Gamma Swap, Variance Swap, Volatility Replication, Volatility Trading

Limit order books and trade informativeness

In the microstructure literature, information asymmetry is an important determinant of market liquidity. The classic setting is that uninformed dedicated liquidity suppliers charge price concessions when incoming market orders are likely to be informationally motivated. In limit order book markets, however, this relationship is less clear, as market participants can switch roles, and freely choose to immediately demand or patiently supply liquidity by submitting either market or limit orders. We study the importance of information asymmetry in limit order books based on a recent sample of thirty German DAX stocks. We find that Hasbrouck’s (1991) measure of trade informativeness Granger-causes book liquidity, in particular that required to fill large market orders. Picking-off risk due to public news induced volatility is more important for top-of-the book liquidity supply. In our multivariate analysis we control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. JEL Classification: G14 Keywords: Price Impact of Trades , Trading Intensity , Dynamic Duration Models, Spread Decomposition Models , Adverse Selection Ris