Real equilibrium exchange rates. A panel data approach for advanced and emerging economies.

Based on an behavioral equilibrium exchange rate model, this paper examines the determinants of the real effective exchange rate and evaluates the degree of misalignment of a group of currencies since 1980. Within a panel cointegration setting, we estimate the relationship between exchange rate and a set of economic fundamentals, such as traded-nontraded productivity differentials and the stock of foreign assets. Having ascertained the variables are integrated and cointegrated, the long-run equilibrium value of the fundamentals are estimated and used to derive equilibrium exchange rates and misalignments. Although there is statistical homogeneity, some structural differences were found to exist between advanced and emerging economies.

General Equilibrium Long-Run Determinants for Spanish FDI: A Spatial Panel Data Approach

While general equilibrium theories of trade stress the role of third-country effects, little work has been done in the empirical foreign direct investment (FDI) literature to test such spatial linkages. This paper aims to provide further insights into long-run determinants of Spanish FDI by considering not only bilateral but also spatially weighted third-country determinants. The few studies carried out so far have focused on FDI flows in a limited number of countries. However, Spanish FDI outflows have risen dramatically since 1995 and today account for a substantial part of global FDI. Therefore, we estimate recently developed Spatial Panel Data models by Maximum Likelihood (ML) procedures for Spanish outflows (1993-2004) to top-50 host countries. After controlling for unobservable effects, we find that spatial interdependence matters and provide evidence consistent with New Economic Geography (NEG) theories of agglomeration, mainly due to complex (vertical) FDI motivations. Spatial Error Models estimations also provide illuminating results regarding the transmission mechanism of shocks.

Testing the bivariate distribution of daily equity returns using copulas: an application to the Spanish stock market

In this paper we deal with the identification of dependencies between time series of equity returns. Marginal distribution functions are assumed to be known, and a bivariate chi-square test of fit is applied in a fully parametric copula approach. Several families of copulas are fitted and compared with Spanish stock market data. The results show that the t-copula generally outperforms other dependence structures, and highlight the difficulty in adjusting a significant number of bivariate data series

Testing the bivariate distribution of daily equity returns using copulas: an application to the Spanish stock market

In this paper we deal with the identification of dependencies between time series of equity returns. Marginal distribution functions are assumed to be known, and a bivariate chi-square test of fit is applied in a fully parametric copula approach. Several families of copulas are fitted and compared with Spanish stock market data. The results show that the t-copula generally outperforms other dependence structures, and highlight the difficulty in adjusting a significant number of bivariate data series

Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

Monetary policy with signal extraction from the bond market

Monetary policy is conducted in an environment of uncertainty. This paper sets upa model where the central bank uses real-time data from the bond market togetherwith standard macroeconomic indicators to estimate the current state of theeconomy more efficiently, while taking into account that its own actions influencewhat it observes. The timeliness of bond market data allows for quicker responsesof monetary policy to disturbances compared to the case when the central bankhas to rely solely on collected aggregate data. The information content of theterm structure creates a link between the bond market and the macroeconomythat is novel to the literature. To quantify the importance of the bond market asa source of information, the model is estimated on data for the United Statesand Australia using Bayesian methods. The empirical exercise suggests that thereis some information in the US term structure that helps the Federal Reserve toidentify shocks to the economy on a timely basis. Australian bond prices seemto be less informative than their US counterparts, perhaps because Australia is arelatively small and open economy.

Is bank debt special for the transmission of monetary policy? Evidence from the stock market

We combine existing balance sheet and stock market data with two new datasets to studywhether, how much, and why bank lending to firms matters for the transmission of monetarypolicy. The first new dataset enables us to quantify the bank dependence of firms precisely,as the ratio of bank debt to total assets. We show that a two standard deviation increase inthe bank dependence of a firm makes its stock price about 25% more responsive to monetarypolicy shocks. We explore the channels through which this effect occurs, and find that thestock prices of bank-dependent firms that borrow from financially weaker banks display astronger sensitivity to monetary policy shocks. This finding is consistent with the banklending channel, a theory according to which the strength of bank balance sheets mattersfor monetary policy transmission. We construct a new database of hedging activities andshow that the stock prices of bank-dependent firms that hedge against interest rate riskdisplay a lower sensitivity to monetary policy shocks. This finding is consistent with aninterest rate pass-through channel that operates via the direct transmission of policy ratesto lending rates associated with the widespread use of floating-rates in bank loans and creditline agreements.

A Methodology to measure shareholder value orientation and shareholder value creation aimed at providing a research basis to investigate the link between both magnitudes

Does shareholder value orientation lead to shareholder value creation? This article proposes methods to quantify both, shareholder value orientation and shareholder value creation. Through the application of these models it is possible to quantify both dimensions and examine statistically in how far shareholder value orientation explains shareholder value creation. The scoring model developed in this paper allows quantifying the orientation of managers towards the objective to maximize wealth of shareholders. The method evaluates information that comes from the companies and scores the value orientation in a scale from 0 to 10 points. Analytically the variable value orientation is operationalized expressing it as the general attitude of managers toward the objective of value creation, investment policy and behavior, flexibility and further eight value drivers. The value creation model works with market data such as stock prices and dividend payments. Both methods where applied to a sample of 38 blue chip companies: 32 firms belonged to the share index IBEX 35 on July 1st, 1999, one company represents the “new economy” listed in the Spanish New Market as per July 1st, 2001, and 5 European multinational groups formed part of the EuroStoxx 50 index also on July 1st, 2001. The research period comprised the financial years 1998, 1999, and 2000. A regression analysis showed that between 15.9% and 23.4% of shareholder value creation can be explained by shareholder value orientation.

Selection bias and unobservable heterogeneity applied at the wage equation of European married women

This paper utilizes a panel data sample selection model to correct the selection in the analysis of longitudinal labor market data for married women in European countries. We estimate the female wage equation in a framework of unbalanced panel data models with sample selection. The wage equations of females have several potential sources of.

The hidden costs of hidden debt

We report evidence that salience may have economically signi.cant e¤ects on homeowners.borrowing behavior, through a bias in favour of less salient but more costly loans. Survey evidence corroborates the existence of such a bias. We outline a simple model in which some consumers are biased and show that under plausible assumptions this affects prices in equilibrium. Market data support the predictions of the model.

Honey, I shrunk the sample covariance matrix

The central message of this paper is that nobody should be using the samplecovariance matrix for the purpose of portfolio optimization. It containsestimation error of the kind most likely to perturb a mean-varianceoptimizer. In its place, we suggest using the matrix obtained from thesample covariance matrix through a transformation called shrinkage. Thistends to pull the most extreme coefficients towards more central values,thereby systematically reducing estimation error where it matters most.Statistically, the challenge is to know the optimal shrinkage intensity,and we give the formula for that. Without changing any other step in theportfolio optimization process, we show on actual stock market data thatshrinkage reduces tracking error relative to a benchmark index, andsubstantially increases the realized information ratio of the activeportfolio manager.

Industrial emissions abatement: Untangling the impact of the EU ETS and the economic crisis

In this study we use historical emission data from installations under the European Union Emissions Trading System, -EU ETS- to evaluate the impact of this policy on industrial greenhouse gas emissions during the first two trading phases, 2005-2012. As such the analysis seeks to disentangle two causes of emission abatement: that attributable to the EU ETS and that attributable to the economic crisis that hit the EU in 2008/09. Using a panel data approach the estimated emissions reduction attributable to the EU ETS is about 21 per cent of the total emission abatement during the observation period. These results suggest therefore that the lion’s share of abatement was attributable to the effects of the economic crisis, a finding that has serious implications for future policy adjustments affecting core elements of the EU ETS, including the distribution of EU emission allowances.

(No)competition in the Spanish retailing gasoline market: a variance filter approach

Various methodologies in economic literature have been used to analyse the international hydrocarbon retail sector. Nevertheless at a Spanish level these studies are much more recent and most conclude that generally there is no effective competition present in this market, regardless of the approach used. In this paper, in order to analyse the price levels in the Spanish petrol market, our starting hypothesis is that in uncompetitive markets the prices are higher and the standard deviation is lower. We use weekly retail petrol price data from the ten biggest Spanish cities, and apply Markov chains to fill the missing values for petrol 95 and diesel, and we also employ a variance filter. We conclude that this market demonstrates reduced price dispersion, regardless of brand or city.

A subsampling approach to estimating the distribution of diversing statistics with application to assessing financial market risks

In this paper we propose a subsampling estimator for the distribution ofstatistics diverging at either known rates when the underlying timeseries in strictly stationary abd strong mixing. Based on our results weprovide a detailed discussion how to estimate extreme order statisticswith dependent data and present two applications to assessing financialmarket risk. Our method performs well in estimating Value at Risk andprovides a superior alternative to Hill's estimator in operationalizingSafety First portofolio selection.

A territorial approach to firm entry: The effect of city size

This paper concerns the effects of territorial factors on the processes involved in the creation of manufacturing firms in Spanish cities. Most contributions have focused on regional factors rather than urban ones. Here we assume that it is possible to identify certain urban factors that attract new firms. We use data for the entry of firms in Spanish manufacturing industries between 1994 and 2002. This paper contributes to the existing literature on market entry. Key words: cities, regions, firm entry and Spanish economy. JEL: R0, R12, L60