Financial Markets
http://lists.repec.org/mailman/listinfo/nep-fmk
Financial Markets2014-08-25Kwang Soo CheongA Compound Multifractal Model for High-Frequency Asset Returns
http://d.repec.org/n?u=RePEc:arx:papers:1408.3650&r=fmk
This paper builds a model of high-frequency equity returns in clock time by separately modeling the dynamics of trade-time returns and trade arrivals. Our main contributions are threefold. First, we characterize the distributional behavior of high-frequency asset returns both in clock time and trade time and show that when controlling for pre-scheduled market news events, trade-time returns are well characterized by a Gaussian distribution at very fine time scales. Second, we develop a structured and parsimonious model of clock-time returns by subordinating a trade-time Gaussian distribution with a trade arrival process that is associated with a modified Markov-Switching Multifractal Duration (MSMD) model of Chen et al. (2013). Our modification of the MSMD model provides a much better characterization of high-frequency inter-trade durations than the original model of Chen et al. (2013). Over-dispersion in this distribution of inter-trade durations leads to leptokurtosis and volatility clustering in clock-time returns, even when trade-time returns are Gaussian. Finally, we use our model to extrapolate the empirical relationship between trade rate and volatility in an effort to understand conditions of market failure. Our model finds that physical separation of financial markets maintains a natural ceiling on systemic volatility and promotes market stability.Eric M. Aldrich, Indra Heckenbach, Gregory Laughlin2014-08Sector-Based Factor Models for Asset Returns
http://d.repec.org/n?u=RePEc:arx:papers:1408.2794&r=fmk
Factor analysis is a statistical technique employed to evaluate how observed variables correlate through common factors and unique variables. While it is often used to analyze price movement in the unstable stock market, it does not always yield easily interpretable results. In this study, we develop improved factor models by explicitly incorporating sector information on our studied stocks. We add eleven sectors of stocks as defined by the IBES, represented by respective sector-specific factors, to non-specific market factors to revise the factor model. We then develop an expectation maximization (EM) algorithm to compute our revised model with 15 years' worth of S&P 500 stocks' daily close prices. Our results in most sectors show that nearly all of these factor components have the same sign, consistent with the intuitive idea that stocks in the same sector tend to rise and fall in coordination over time. Results obtained by the classic factor model, in contrast, had a homogeneous blend of positive and negative components. We conclude that results produced by our sector-based factor model are more interpretable than those produced by the classic non-sector-based model for at least some stock sectors.Angela Gu, Patrick Zeng2014-08Capital gains taxes and asset prices: The impact of tax awareness and procrastination
http://d.repec.org/n?u=RePEc:zbw:fubsbe:201417&r=fmk
We argue that the impact of capital gains taxation on asset pricing depends on the tax awareness of market participants. While institutional investors should be generally well-informed about tax regulations, private investors have only limited tax knowledge and resources. As a result, market reactions on tax law changes may be delayed if a considerable fraction of market participants is not fully tax-aware. In line with our argument, we find evidence that the introduction of a previously announced German flat tax on private capital gains in 2009 resulted in a temporarily strong and significant increase of trading volumes, daily returns and asset prices. Our research implies that tax law changes provide an opportunity for well-informed investors to generate arbitrage benefits. Corresponding to our estimate, the capital gains tax resulted in an increase demand for shares of 160 % as well as in an price surplus of about 7.4 % within the last two trading days 2008. --Eichfelder, Sebastian, Lau, Mona2014capital gains tax,asset pricing,tax awareness,tax arbitrageOption Pricing in an Oligopolistic Setting
http://d.repec.org/n?u=RePEc:pra:mprapa:57978&r=fmk
Option valuation models are usually based on frictionless markets. This paper extends and complements the literature by developing a model of option pricing in which the derivative and/or the underlying asset have an oligopolistic market structure, which produces an expected return on these assets that exceeds (or goes below) their fundamental value, and hence affects the option valuation. Our formulation begins modeling a capital asset pricing model that takes into account an oligopolistic setting, and hence the standard option pricing formula is derived, but this time considering the level of market power into the model. Our results show that higher levels of market power will lower the required expected return, in comparison to the perfectly competitive CAPM model. Similarly, simulations show that higher levels of market power in the derivative markets tend to increase the call option values in comparison to those values given by the standard Black and Scholes formulation, while the impact of market power in the underlying asset market tends to lower the option price.Villena, Marcelo, Villena, Mauricio2011-03-01Capital Asset Pricing, Option Pricing, Oligopolistic Markets.A Tale of Two Option Markets: Pricing Kernels and Volatility Risk
http://d.repec.org/n?u=RePEc:fip:fedgfe:2014-58&r=fmk
Using prices of both S&P 500 options and recently introduced VIX options, we study asset pricing implications of volatility risk. While pointing out the joint pricing kernel is not identified nonparametrically, we propose model-free estimates of marginal pricing kernels of the market return and volatility conditional on the VIX. We find that the pricing kernel of market return exhibits a decreasing pattern given either a high or low VIX level, whereas the unconditional estimates present a U-shape. Hence, stochastic volatility is the key state variable responsible for the U-shape puzzle documented in the literature. Finally, our estimates of the volatility pricing kernel feature a U-shape, implying that investors have high marginal utility in both high and low volatility states.Song, Zhaogang, Xiu, Dacheng2014-01-30Pricing kernel; volatility risk; VIX option; state-price density