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.

Stochastic optimal bid to electricity markets with environmental risk constraints

There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) in the current energy market framework. Environmental policy issues have become more and more important for fossil-fuelled power plants and they have to be considered in their management, giving rise to emission limitations. This work allows to investigate the influence of both the allowances and emission reduction plan, and the incorporation of the derivatives medium-term commitments in the optimal generation bidding strategy to the day-ahead electricity market. Two different technologies have been considered: the coal thermal units, high-emission technology, and the combined cycle gas turbine units, low-emission technology. The Iberian Electricity Market and the Spanish National Emissions and Allocation Plans are the framework to deal with the environmental issues in the day-ahead market bidding strategies. To address emission limitations, some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), have been extended. This study offers to electricity generation utilities a mathematical model to determinate the individual optimal generation bid to the wholesale electricity market, for each one of their generation units that maximizes the long-run profits of the utility abiding by the Iberian Electricity Market rules, the environmental restrictions set by the EU Emission Trading Scheme, as well as the restrictions set by the Spanish National Emissions Reduction Plan. The economic implications for a GenCo of including the environmental restrictions of these National Plans are analyzed and the most remarkable results will be presented.

Economic structure and key sectors analysis of greenhouse gas emissions in Uruguay

This paper identifies the key sectors in greenhouse gas emissions of the Uruguayan economy through input-output analysis. This allows to precisely determine the role played by the different productive sectors and their relationship with other sectors in the relation between the Uruguayan productive structure and atmospheric pollution. In order to guide policy design for GHG reduction, we decompose sectors liability between the pollution generated through their own production processes and the pollution indirectly generated in the production processes of other sectors. The results show that all the key polluting sectors for the different contaminants considered are relevant because of their own emissions, except for the sector Motor vehicles and oil retail trade, which is relevant in CO2 emissions because of its pure, both backward and forward, linkages. Finally, the best policy channels for controlling and reducing GHGs emissions are identified, and compared with the National Climate Change Response Plan (NCCRP) lines of action.

Characterization of lead-recycling facility emissions at various workplaces : major insights for sanitary risks assessment

Most available studies on lead smelter emissions deal with the environmental impact of outdoor particles, but only a few focus on air quality at workplaces. The objective of this study is to physically and chemically characterize the Pb-rich particles emitted at different workplaces in a lead recycling plant. A multi-scale characterization was conducted from bulk analysis to the level of individual particles, to assess the particles properties in relation with Pb speciation and availability. Process PM from various origins were sampled and then compared; namely Furnace and Refining PM respectively present in the smelter and at refinery workplaces, Emissions PM present in channeled emissions.These particles first differed by their morphology and size distribution, with finer particles found in emissions. Differences observed in chemical composition could be explained by the industrial processes. All PM contained the same major phases (Pb, PbS, PbO, PbSO4 and PbO·PbSO4) but differed on the nature and amount of minor phases. Due to high content in PM, Pb concentrations in the CaCl2 extractant reached relatively high values (40 mg.L-1). However, the ratios (soluble/total) of CaCl2 exchangeable Pb were relatively low (< 0.02%) in comparison with Cd (up to 18%). These results highlight the interest to assess the soluble fractions of all metals (minor and major) and discuss both total metal concentrations and ratios for risk evaluations. In most cases metal extractability increased with decreasing size of particles, in particular, lead exchangeability was highest for channeled emissions. Such type of study could help in the choice of targeted sanitary protection procedures and for further toxicological investigations. In the present context, particular attention is given to Emissions and Furnace PM. Moreover, exposure to other metals than Pb should be considered. [Authors]

Market Transparency, Market Quality and Sunshine Trading

This paper analyzes the implications of pre-trade transpareny on market performance. We find that transparency increases the precision held by agents, however we show that this increase in precision may not be due to prices themselves. In competitive markets, transparency increases market liquidity and reduces price volatility, whereas these results may not hold under imperfect competition. More importantly, market depth and volatility might be positively related with proper priors. Moreover, we study the incentives for liquidity traders to engage in sunshine trading. We obtain that the choice of sunshine/dark trading for a noise trader is independent of his order size, being the traders with higher liquidity needs more interested in sunshine trading, as long as this practice is desirable. Key words: Market Microstructure, Transparency, Prior Information, Market Quality, Sunshine Trading

CO2 emissions embodied in international trade: A multiregional Inputoutput model for Spain

As a result of globalization and free trade agreements, international trade is enormously growing and inevitably putting more pressure on the environment over the last few decades. This has drawn the attention of both environmentalist and economist in response to the ever growing concerns of climate change and urgent need of international action for its mitigation. In this work we aim at analyzing the implication of international trade in terms of CO2 between Spain and its important partners using a multi-regional input-output (MRIO) model. A fully integrated 13 regions MRIO model is constructed to examine the pollution responsibility of Spain both from production and consumption perspectives. The empirical results show that Spain is a net importer of CO2 emissions which is equivalent to 29% of its emission due to production. Even though the leading partner with regard to import values are countries such as Germany, France, Italy and Great Britain, the CO2 embodied due to trade with China takes the largest share. This is mainly due to the importation of energy intensive products from China coupled with Chinese poor energy mix which is dominated by coal-power plant. The largest portion (67%) of the global imported CO2 emissions is due to intermediate demand requirements by production sectors. Products such as Motor vehicles, chemicals, a variety of machineries and equipments, textile and leather products, construction materials are the key imports that drive the emissions due to their production in the respective exporting countries. Being at its peak in 2005, the Construction sector is the most responsible activity behind both domestic and imported emissions.

Survival and Sensemaking: Organizational Resilience in a Wall Street Trading Room After 9/11

How do organizations cope with extreme uncertainty? The existing literature is divided on this issue: some argue that organizations deal best with uncertainty in the environment by reproducing it in the organization, whereas others contend that the orga nization should be protected from the environment. In this paper we study the case of a Wall Street investment bank that lost its entire office and trading technology in the terrorist attack of September 11 th. The traders survived, but were forced to relocate to a makeshift trading room in New Jersey. During the six months the traders spent outside New York City, they had to deal with fears and insecurities inside the company as well as outside it: anxiety about additional attacks, questions of professional identity, doubts about the future of the firm, and ambiguities about the future re-location of the trading room. The firm overcame these uncertainties by protecting the traders' identities and their ability to engage in sensemaking. The organization held together through a leadership style that managed ambiguities and created the conditions for new solutions to emerge.

Ecologies of Value in a Wall Street Trading Room

Our task in this paper is to analyze the organization of trading in the era of quantitative finance. To do so, we conduct an ethnography of arbitrage, the trading strategy that best exemplifies finance in the wake of the quantitative revolution. In contrast to value and momentum investing, we argue, arbitrage involves an art of association-the construction of equivalence (comparability) of properties across different assets. In place of essential or relational characteristics, the peculiar valuation that takes place in arbitrage is based on an operation that makes something the measure of something else-associating securities to each other. The process of recognizing opportunities and the practices of making novel associations are shaped by the specific socio-spatial and socio-technical configurations of the trading room. Calculation is distributed across persons and instruments as the trading room organizes interaction among diverse principles of valuation.

On the impact of leverage constraints on asset prices and trading volume

Researchers have used stylized facts on asset prices and trading volumein stock markets (in particular, the mean reversion of asset returnsand the correlations between trading volume, price changes and pricelevels) to support theories where agents are not rational expected utilitymaximizers. This paper shows that this empirical evidence is in factconsistent with a standard infite horizon perfect information expectedutility economy where some agents face leverage constraints similar tothose found in todays financial markets. In addition, and in sharpcontrast to the theories above, we explain some qualitative differencesthat are observed in the price-volume relation on stock and on futuresmarkets. We consider a continuous-time economy where agents maximize theintegral of their discounted utility from consumption under both budgetand leverage con-straints. Building on the work by Vila and Zariphopoulou(1997), we find a closed form solution, up to a negative constant, for theequilibrium prices and demands in the region of the state space where theconstraint is non-binding. We show that, at the equilibrium, stock holdingsvolatility as well as its ratio to stock price volatility are increasingfunctions of the stock price and interpret this finding in terms of theprice-volume relation.

Insider Trading and Real Investment

In this paper I analyze the effects of insider trading on real investmentand the insurance role of financial markets. There is a single entrepreneurwho, at a first stage, chooses the level of investment in a risky business.At the second stage, an asset with random payoff is issued and then the entrepreneurreceives some privileged information on the likely realization of productionreturn. At the third stage, trading occurs on the asset market, where theentrepreneur faces the aggregate demand coming from a continuum of rationaluniformed traders and some noise traders. I compare the equilibrium withinsider trading (when the entrepreneur trades on her inside information in theasset market) with the equilibrium in the same market without insider trading. Ifind that permitting insider trading tends to decrease the level of realinvestment. Moreover, the asset market is thinner and the entrepreneur's netsupply of the asset and the hedge ratio are lower, although the asset priceis more informative and volatile.

Resolving identities: Successive crises in a trading room after 9/11

How do organizations cope with extreme uncertainty? The existing literatureis divided on this issue: some argue that organizations deal best withuncertainty in the environment by reproducing it in the organization, whereasothers contend that the orga nization should be protected from theenvironment. In this paper we study the case of a Wall Street investment bankthat lost its entire office and trading technology in the terrorist attack ofSeptember 11 th. The traders survived, but were forced to relocate to amakeshift trading room in New Jersey. During the six months the traders spentoutside New York City, they had to deal with fears and insecurities insidethe company as well as outside it: anxiety about additional attacks,questions of professional identity, doubts about the future of the firm, andambiguities about the future re-location of the trading room. The firmovercame these uncertainties by protecting the traders identities and theirability to engage in sensemaking. The organization held together through aleadership style that managed ambiguities and created the conditions for newsolutions to emerge.

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paperexplores the consistency of some of its standard techniques. We focus in particular on prevailingwholesale electricity trading simulation methods. We include different supply and demandrepresentations and propose the Experience-Weighted Attractions method to include severalbehavioural algorithms. We compare the results across assumptions and to economic theorypredictions. The match is good under best-response and reinforcement learning but not underfictitious play. The simulations perform well under flat and upward-slopping supply bidding,and also for plausible demand elasticity assumptions. Learning is influenced by the number ofbids per plant and the initial conditions. The overall conclusion is that agent-based simulationassumptions are far from innocuous. We link their performance to underlying features, andidentify those that are better suited to model wholesale electricity markets.

The dog that did not bark: Insider trading and crashes

This paper documents that at the individual stock level insiders sales peak many months before a large drop in the stock price, while insiders purchases peak only the month before a large jump. We provide a theoretical explanation for this phenomenon based on trading constraints and asymmetric information. We test our hypothesis against competing stories such as patterns of insider trading driven by earnings announcement dates, or insiders timing their trades to evade prosecution. Finally we provide new evidence regarding crashes and the degree of information asymmetry.