Volatility discovery in cryptocurrency markets

PurposeCryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market.Design/methodology/approachThe paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares.FindingsThe authors’ findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies.Research limitations/implicationsThe authors’ findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors’ estimates.Originality/valueTo date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).

Bootstrap Confidence Intervals and Hypothesis Testing for Market Information Shares*

Market information shares are widely used in empirical finance to measure one market’s contributions to price discovery. In contrast to common factor components, the literature on market information shares only provides rudimentary tools to test general hypotheses. Using Monte Carlo simulations, we show that bootstrap confidence bands proposed by Sapp (2002) perform well if markets have similar information shares but are too narrow if one market dominates price discovery. We design a new bootstrap-based method to test the “one-central-market” hypothesis and show that our tests have correct size and substantial power against the null hypothesis. Empirical results in the context of CDS and bonds markets complement the theoretical analysis.

Price Discovery in High Resolution*

U.S. equity market data are currently timestamped to nanosecond precision. This permits models of price dynamics at resolutions sufficient to capture the reactions of the fastest agents. Direct estimation of multivariate time series models at sub-millisecond frequencies nevertheless poses substantial challenges. To facilitate such analyses, this paper applies long distributed lag models, computations that take advantage of the inherent sparsity of price transitions, and bridged modeling. At resolutions ranging from 1 s down to 10 μs, I estimate representative models for two stocks (IBM and NVDA) bearing on three topics of current interest. The first analysis examines the extent to which the conventional source of market data (the consolidated tape) accurately reflects the prices observed by agents who subscribe (at additional cost) to direct exchange feeds. At a 1-s resolution, the information share of the direct feeds is indistinguishable from that of the consolidated tape. At resolutions of 100 and 10 μs, however, the direct feeds are totally dominant, and the consolidated share approaches zero. The second analysis examines the quotes from the primary listing exchange vs. the non-listing exchanges. Here, too, information shares that are essentially indeterminate at 1-s resolution become much more distinct at higher resolutions. Although listing exchanges execute about one-fifth of the trading volume, their information shares are slightly above one-half. The third analysis examines quotes, lit trades, and dark trades. At a 1-s resolution, dark trades appear to have a small, but discernible, information contribution. This vanishes at higher resolutions. Quotes and lit trades essentially account for all price discovery, with information shares of roughly 65% and 35%, respectively.

Steganography Based on Human Perception

Today internet has become a trusted factotum of everyone. Almost all payments like tax, insurance, bank transaction, healthcare payment, payment in e-commerce are done digitally through debit or credit card or through e-wallet. People share their personal information through social media like Facebook. Twitter, WhatsApp etc. The government of every developing country is going to embrace e-Governance system to interact with people more promptly. The information shares through these applications are the burning target to intruders. This paper utilized the imperceptibility as well as the robustness of steganography techniques which are increased by embedding multiple bits in a particular region selected either based on some image attributes or by Human Visual Perception.

Existence and Exploitability of Financial Analysts' Informational Leadership

This paper bridges two recent studies on the role of analysts to provide new and relevant information to investors. On the one hand, the contribution of analysts to long-term price discovery on the US market is rather low. Considering earnings per share forecasts as the main output of analysts’ reports, their information share amounts to only 4.6% on average. On the other hand, trading strategies set up on these EPS forecasts are quite profitable. Self-financing portfolios yield excess returns of more than 5% over the S&P 100 index for a time period of 36 years, which is persistent after controlling for the well-known risk factors. In this paper, we discuss the link between the low information shares and the high abnormal returns. We argue that information shares of analysts cannot be higher, because otherwise their forecasts would lead to excessively profitable trading strategies which are very unlikely to persist over such a long period of time.