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on Market Microstructure |
| By: | Ricardo Lagos; Shengxing Zhang |
| Abstract: | We develop a model of monetary exchange in over-the-counter markets to study the effects of monetary policy on asset prices and standard measures of financial liquidity, such as bid-ask spreads, trade volume, and the incentives of dealers to supply immediacy, both by participating in the market-making activity and by holding asset inventories on their own account. The theory predicts that asset prices carry a speculative premium that reflects the asset's marketability and depends on monetary policy as well as the microstructure of the market where it is traded. These liquidity considerations imply a positive correlation between the real yield on stocks and the nominal yield on Treasury bonds---an empirical observation long regarded anomalous. The theory also exhibits rational expectations equilibria with recurring belief driven events that resemble liquidity crises, i.e., times of sharp persistent declines in asset prices, trade volume, and dealer participation in market-making activity, accompanied by large increases in spreads and abnormally long trading delays. |
| JEL: | D83 E31 E52 E58 G12 |
| Date: | 2015–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:21528&r=all |
| By: | Krauss, Christopher |
| Abstract: | This survey reviews the growing literature on pairs trading frameworks, i.e., relative-value arbitrage strategies involving two or more securities. The available research is categorized into five groups: The distance approach uses nonparametric distance metrics to identify pairs trading opportunities. The cointegration approach relies on formal cointegration testing to unveil stationary spread time series. The time series approach focuses on finding optimal trading rules for mean-reverting spreads. The stochastic control approach aims at identifying optimal portfolio holdings in the legs of a pairs trade relative to other available securities. The category "other approaches" contains further relevant pairs trading frameworks with only a limited set of supporting literature. Drawing from this large set of research consisting of more than 90 papers, an in-depth assessment of each approach is performed, ultimately revealing strengths and weaknesses relevant for further research and for implementation. |
| Keywords: | statistical arbitrage,pairs trading,spread trading,relative-value arbitrage,meanreversion |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwqwdp:092015&r=all |
| By: | António A. F. Santos (Faculty of Economics, University of Coimbra, and GEMF, Portugal) |
| Abstract: | The measurement of the volatility is key in financial markets. This is true not only because decisions are made in an environment of uncertainty, but because sometimes the volatility element overpowers all the remaining aspects in the decision process. Huge movements in the prices of the assets (volatility) can lead to huge losses and also huge gains. There are models to establish the fair prices for certain kind of assets, in that the only parameter that is not directly observable is the parameter characterizing the volatility. However, it is well established in the literature that the evolution of the volatility can be forecasted. Several parametric models have been proposed for modeling the volatility evolution, for example, the Autoregressive Conditional Heteroscedastic (ARCH) and the Stochastic Volatility model (SV). Nowadays, we live in a “Big Data” world, and even for non-professionals of financial markets, it is possible to record data obtained at every second. Recently, measures of volatility have been developed using intraday data, for example, the measure of realized volatility. One of the main aspects to consider is that intraday data and measures of realized volatility are associated with unequal time-spaced observations. In this paper, we compare the forecasts of the volatility evolution using intradaily observations and daily observations, and by trying to conciliate both kind of forecasts, for the data obtained from US and European stock markets, we find out that the use of measures of realized volatility represent an important improvement in volatility forecasting, that can be added to the more well established models that are used in this context, ARCH and SV models. |
| Keywords: | ARCH models, Big data, Intraday data, Realized volatility, Stochastic volatility. |
| JEL: | C11 C15 C53 G17 |
| Date: | 2015–08 |
| URL: | http://d.repec.org/n?u=RePEc:gmf:wpaper:2015-17.&r=all |