Contextualización del Caso de Estudio 1. Dúrcal y el Valle de Lecrín

Market

The relationship between volume and asset returns in financial markets has generally been couched in the information dynamics framework amongst agents in the market, as well as in the market microstructure framework. It should be noted that these are not mutually exclusive frameworks since information asymmetry plays an important role in both systems. The information flow framework is essentially driven by the sequential information arrival models and the mixture of distributions (MDH) models.

The sequential information arrival models (Copeland 1976) starts off with agents in equilibrium, that is they are satisfied with their portfolio holdings, new information is then received by individual agents one at a time who then revise their beliefs and trades to arrive at a new temporary equilibrium. These actions lead to increased volumes and transaction price changes. This process continues until all agents have received the information and a new final equilibrium is reached. The fundamental insight of these models is that volume can forecast prices.

In contrast the MDH (Clarke, 1973; Tauchen and Pitt 1983) posits an underlying information flow that leads to the joint dependence of volumes and prices. They assume a random arrival of new information where information arrivals cause increased trading volume and price changes. Anderson (1996) modifies this basic model by including liquidity traders who do not trade in response to information arrivals and by assuming that information arrivals are serially correlated. The latter property leads to volatility persistence and therefore GARCH type models may be best at capturing the time series properties of the relationship between volume and prices in the MDH framework.

In terms of the market microstructure framework, private information is central to the analysis of the price-volume relationship. This relationship is, however, clouded by the fact that it is unclear what exact information volume provides to the market. The informational role of volume in the price process has been investigated by Pfleiderer (1984), Easley and O’Hara (1987), Blume, Easley and O’Hara (1994) and Wang (1994).

These studies can be separated into two categories, one which used the rational expectations framework to look at how volume is generated in an environment where agents with different information trade and another which looks at the information inherent in volume and the learning that occurs from observing volume. The first set of studies (Wang, 1994) provides links between volume and agents characteristics which allows one to make a connection between price changes and volume. In particular, volume is positively correlated with the absolute value of excess returns and the greater the asymmetry between agents information sets the greater the trading volume.

The second approach (Blume, Easley and O’Hara, 1994) looks at the learning that occurs when agents can condition their behavior on information contained in volume as well as price information in forming demand. The role of volume in the price adjustment process is to help agents to clear up the uncertainty of the quality of information from the direction of information which is derived from observing prices.

The signal from volume is different from prices because volume, unlike prices, is not normally distributed. Agents therefore get better information by observing both volumes and prices. In this learning by trading approach volume and price changes are positively correlated.

A review of the relationship between trading volumes and asset price dynamics by Karpoff (1987) also indicated that the vast majority of studies up to that period were devoted to examining this issue in the context of stock exchanges. This has since changed with a number of studies looking at these issues in the context of the foreign exchange markets (Bollerslev and and Domowitz, 1993; Jorion, 1996; Kim and Sheen, 2006; Galati, 2000; Melvin and Yin, 2000; Park, 2010). This trend has been caused by a number of factors. Some of the main reasons include the fact that foreign exchange markets are the largest in terms of daily turnover, the fact that exchange rate dynamics have such profound effects on economic conditions generally and on financial asset prices in particular and because more comprehensive information on the operations of

Many studies have looked at the impact of direct central bank intervention in the foreign exchange market in terms of its impact on price dynamics (Beine, 2004; Kim and Rigobon, 2005; Kim and sheen, 2006). The direct intervention policy reaction function of the central bank have also been looked at by a number of authors (Almekinders and Eijffinger, 1996; Ito, 2002; Ito and Yabu; 2007) and some studies have looked at the substitutability and relationship between direct intervention and monetary policy, particularly interest rate policy (kaminsky and Lewis, 1996; Bosner-Neal, Roley and Sellon, 1998, Sellon, 2003; Vitale, 2003; Egert and Komarek, 2006).

Moreover, many studies have looked at the impact of interest rate policy on exchange rate dynamics (Eichenbaum and Evens, 1995; Faust and Rogers, 2003; Chen 2006) and some have looked at the effect of central bank interventions on volume (Jorion, 1996;

Chaboud and Lebron, 2001).

We do not know of any study, however, that have looked at the impact of central bank policy (both direct intervention and interest rate policy) on trading volume and exchange rate returns at both the level and variance in the foreign exchange market.

The only study to our knowledge that have come closest to pulling these related strands of literature together in an integrated framework for the spot foreign exchange market is the Kim and Sheen (2006) study⁴¹. Kim and Sheen looked at the impact of central bank policy in the spot foreign exchange market in a multivariate GARCH framework that captured the many inherent links between volume, prices and policy variables in the foreign exchange market. However, Kim and Sheen (2006) by using a bivariate GARCH formulation with volume and returns together with a separate probit model for the policy reaction function of the central bank for direct intervention, was unable to capture the full dynamics of the linkages and endogeneity between volume, returns and direct intervention at levels and variances.

This is a significant weakness not only because one cannot trace the complete set of linkages and feedback effects but also because the estimated parameters would tend to be less efficient than if a more complete four-variable GARCH model was used to capture the relationship between volumes, returns, direct intervention and interest rates. Another weakness of the study by Kim and Sheen (2006) was that they only included policy interest rates as an exogenous variable in the separate policy reaction

function of the central bank. Modeling the effect of policy interest rates this way did not capture the inherent linkages and endogeneity between direct intervention and policy interest rates and indeed between policy interest rates, exchange rate returns and foreign exchange volumes (Chen, 2005).

The failure to look at the role volume plays in central bank policy effectiveness in the foreign exchange market in a fully integrated empirical framework is a huge lucuna in the literature since volume dynamics is central to the functioning of the market in terms of price dynamics and confidence. We address this issue in the Icelandic foreign exchange market in the following sections.

4.2 The Structure of the Icelandic Foreign Exchange Market and