High frequency trading and standard asset pricing models

We present a pricing kernel that summarizes well the main features of the dynamics of interest rates and risk in postwar U.S. data and use it to uncover how the pricing kernel has moved with the short rate in this data. Our findings imply that standard monetary models miss an essential link between the central bank instrument and the economic activity that monetary policy is intended to affect, and thus we call for a new approach to monetary policy analysis. We sketch a new approach using an economic model based on our pricing kernel. The model incorporates the key relationships between policy and risk movements in an unconventional way: the central bank's policy changes are viewed as primarily intended to compensate for exogenous business cycle fluctuations in risk that threaten to push inflation off target. This model, while an improvement on standard models, is considered just a starting point for their revision. It leads to critical questions that researchers need to answer as they continue to revise their approach to monetary policy analysis. Copyright © 2009 by the National Bureau of Economic Research.

High Frequency Trading, Market Volatility and Trading Counterparty Performance

This study revisits a central assumption of standard trade models: constant marginal cost technology. The presence of increasing marginal costs for exporters introduces significant market interdependence across borders missing from traditional models of international trade that rely on constant marginal cost technology. Such market interdependence represents an additional channel through which local shocks are transmitted globally. To identify increasing marginal cost at the level of the firm, we build in flexible production assumptions that nest increasing, decreasing, and constant marginal cost technology to an otherwise standard international trade model. We derive an estimating equation that can be taken directly to the data. Our structural equation explicitly guides our inference on the shape of the marginal cost curve from estimated coefficients. The results suggest that increasing marginal cost is predominant at the firm level. Moreover, utilizing plant‐level information on physical and financial capacity constraints, we find that the degree of increasing marginal cost is significantly exacerbated by both types of constraints. The evidence suggests that access to larger markets through greater international integration may not have the expected welfare gains typically predicted in standard models. (JEL F12, F14)

Timescale betas and the cross section of equity returns: Framework, application, and implications for interpreting the Fama–French factors

The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.

PurposeThe purpose of this paper is to test a discrete time asset pricing model where a non‐marketable asset (human capital), along with other factors predicting stock returns, explain risk return relationship. The paper will add to the literature on risk return relationship with human capital by investigating the hypothesis that human capital is a significant factor affecting stock prices.Design/methodology/approachThe dynamic inter‐linkages of factors representing financial and human components of wealth in predicting stock returns is tested in the Indian market for the period of 1996:04 to 2005:06. The procedures employed include Granger causality tests, impulse response functions and seemingly unrelated regression estimates.FindingsEmpirical findings validate the model that including human capital as a proxy for aggregate wealth in the economy can better predict stock prices than the standard empirical capital asset pricing model. There is a Granger cause relationship between security prices and labor income and it is further concluded that labor and dividend are significant factors affecting security prices.Originality/valueThis is one of the first papers to study the human capital aspect in predicting stock returns in the Indian market. In addition, the paper provides important insights into the causal relationship of human capital and market return in explaining the risk return relationship.

High-Frequency Trading and Extreme Price Movements

This chapter reviews the efficient market hypothesis (EMH), proposed by Fama in 1970s. We discuss and exemplify the applications of the hypothesis that the equities are perfectly priced according to their characteristics, meaning that the market prices reflect the information made available to investors at any given time. The main focus of the chapter is to explore the relevance of the efficient market theory to the proximity-biased investors. We also review the standard asset pricing models used to test EMH.

Asset market equilibrium: A simulation

Investors can generate excess returns by implementing trading strategies based on publicly available equity analyst forecasts. This paper captures the information provided by analysts by the implied cost of capital (ICC), the internal rate of return that equates a firm’s share price to the present value of analysts’ earnings forecasts. We find that U.S. stocks with a high ICC outperform low ICC stocks on average by 6.0 % per year. This spread is significant when controlling the investment returns for their risk exposure as proxied by standard pricing models. Further analysis across the world’s largest equity markets validates these results.

In this article, futures and commodity options are analyzed in the context of Merton's (1987) model of capital market equilibrium with incomplete information. First, following Dusak (1973) and Black (1976), the conditions under which Merton's model can be applied to the valuation of forward and futures contracts are proposed. Then an application to futures markets is given. We provide a partial differential equation and the formulas for European commodity options, futures contracts, and American options in the same context. The models are simulated and compared to standard models with no information costs. We find that model prices are not significantly different from standard model prices. However, our models correct for some pricing biases in standard models. In particular, they reduce the overvaluation bias for European and American commodity options. It seems that the costs of gathering and processing information regarding the option and its underlying asset play a role in explaining the biases observed in standard models. This work can be applied to other futures markets. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 645–664, 1999

In today's markets where high frequency traders (HFTs) both provide and take liquidity, what influences HFTs' liquidity provision? I argue that information asymmetry induced by liquidity-taking HFTs' use of machine-readable information is important. Applying a novel statistical approach to measure HFT activity and using a natural experiment of index inclusion, I show that liquidity-providing HFTs supply less liquidity to stocks that suffer more from this information assymmetry problem. Moreover, when markets are volatile, this information asymmetry problem becomes more severe, and HFTs supply less liquidity. I discuss implications for market-making activity in times of market stess and for HFT regulations.

High Frequency Traders and Asset Prices

In the world of big data, cloud computing in trading and rapid development in computing hardware and software; cloud computing and high frequency trading (HFT) unconditionally turn to be tightly related as the way market is changing rapidly. We find the practicability of event trading strategy of HFT in the Finnish stock market based on auto regressive empirical test and comparative ratios insinuating the impression on positive recovers of event trading. A specialised version of cloud trading system is then architected after discovering and exploring the feasibility of HFT in event-based trading in the Finnish stock market so that afresh get going HFT firms who are speculating what tact to exercise with what holding period and not capable to get micro second favours of news feed earlier than their adversaries; can practice this discipline and the proposed cloud trading architect for alpha generation.

High Frequency Trading and Co-Movement in Financial Markets