THE STIGLER-LUCKOCK MODEL FOR A LIMIT ORDER BOOK Abstract One of the types of modern-day markets are so-called order-driven markets whose core component is a database of all incoming buy and sell orders (order book). The main goal of this thesis is to extend the Stigler-Luckock model for order books to give a better insight into the price forming process and behaviour of the market participants themselves. The model introduced in this thesis focuses on a comparison of behaviour and various strategies of market makers who are sophisticated market participants profiting from extensive trading. The market is described using Markov chains, and the strategies are compared using Monte Carlo simulations and game theory. The results showed that market makers' orders should have small spread and large volumes. The final model compares two strategies in which market makers monitor their portfolio. In case of having more cash than asset (or vice versa), they shift prices of their orders to equalise the portfolio. The model recommends checking the market quite often, but acting conservatively, which means not changing prices that frequently and not jumping to conclusions just from a small imbalance in the portfolio.

Created in 2009, the digital currency of bitcoin is a relatively new phenomenon. During this short period of time, it has however displayed a strong development of both price and trade volume. This has led to increased media attention, but also regulators and researchers have developed an interest. At this moment, the amount of available research is however limited. With a focus on the price volatility of bitcoin and an aim of finding drivers of this volatility, this study is taking a unique position. The research has its basis in the philosophical position of positivism and objectivism. This has shaped the research question as well as the construction of the study. The result is a describing and explaining research with a deductive research approach, a quantitative research method and an archival research strategy. This has in turn stimulated an extensive literature review and information search. Areas of discussion are microstructure theory, the efficient market hypothesis, behavioural finance and informational structures. Due to the limited amount of previous bitcoin research within the area of price volatility, the study has drawn extensively on research performed on more classical assets such as stocks. Nevertheless, when available, bitcoin research has been used as a foundation/reference and an inspiration. Reviews of academic literature and economic theories, as well as public news helped to identify the variables for the empirical study. These variables are; information demand, trade volume, world market index, trend and six specified events, occurring during the chosen sample period and included in the study as dummy variables. The variables are all analysed and included in a GARCH (1,1) model, modified following a similar research by Vlastakis & Markellos (2012) on stocks. This GARCH (1,1) model is then fitted to the bitcoin volatility registered for the sample period and is able thereby able to generate data of if and how the variables affect the bitcoin volatility. The test result suggests that five of the ten variables are significant on a 5 %-level. More specifically it suggests that information demand is a significant variable with a positive influence on the bitcoin volatility, something that corresponds to the literature on information demand and price volatility. This also relates to the events found significant, as they generated bitcoin related information. The significant events of the Cypriot crisis and the failure of the bitcoin exchange MtGox are thus specific examples of how information affects price volatility. Another significant variable is trade volume, which also displays a positive influence on the volatility. The last significant variable turned out to be a constructed positive trend, suggesting that increasing acceptance of bitcoin decreases its volatility.

This thesis adopts an empirical approach to examine various market microstructure issues, using data from the National Stock Exchange of India (NSE). Whilst the respective empirical analyses may be considered as self-contained investigations, they are primarily linked through the common objective of understanding the mechanics of the pricing process as it occurs on actual markets, using the NSE as exemplar. The first major focus of the dissertation is non-synchronous trading: empirical evidence of nonsynchronicity is obtained by testing for predictability as between indices of different levels of liquidity. A simple test of the analysis of trading-break returns is proposed to infer whether predictability may be mainly attributable to non-synchronous trading or whether it constitutes a delayed adjustment of traders' expectations. The second question tackled in the thesis is whether volatility on the NSE may be considered as justified or excessive. Rathert han adopting the established methodology of comparing stock price changes to information about expected dividends, the research question is split up into two subsidiary ones. The first question is whether volatility is related to information flows, whilst the second related questionc oncernst he relationship betweenv olatility and returns. Three sources of excessive volatility are pin-pointed. Monday effects are found in index data but not in the underlying stocks-indicating index fluctuations which are not information-related. A second indicator of excessive price movements is the pronounced volatility which coincides with the fiscal year end of quoted companies but which is not accompanied by a similar increase in long-term returns. A third indication of unjustified price fluctuations is that volatility seems unrelated to returns when considering a long-term time series. The third topic of the thesis relates to the efficacy of opening and closing call auctions. This issue may be considered as the crux of the dissertation and it is tackled by analysing the effects of the suspension of a call auction system on NSE. Changes in volatility, efficiency and liquidity following the suspension are analysed, and an event study is presented. The relationship between call auctions and long-term volatility is also investigated. The findings suggest that the expected benefits of call auctions may not always materialise, possibly due to an inappropriately structured auction, or because a liquidity threshold for stocks must be surpassed for the expected benefits to accrue.

由於亞洲金融風暴源於資產價格崩盤，引發系統性風險，影響經濟穩定發展，各國央行開始正視資產價格變動所傳達之訊息，各個金融市場之資產價格也可能反映出投資人對物價之預期及經濟成長或衰退的訊息，似乎可作為央行執行貨幣政策指標或預測未來經濟發展之參考依據。 本研究利用Ederington and Lee(1993)模型，以台灣公債日資料(01/04/2001~12/29/2006)及日內資料(08/31/2005~12/20/2006)檢視經濟數據發佈對台灣十年期公債影響。以日資料實證結果發現僅經濟成長率及海關進出口貿易發佈對公債報酬率波動有較顯著衝擊；日內資料實證結果僅CPI-WPI及景氣對策訊號有顯著影響。台灣經濟數據發佈效果不如國外債券市場來的明顯，使得國內債券市場投資人反而尋求其他私人訊息納入交易策略考慮。

The three chapters of this thesis revolve around the trade in English stocks in the Amsterdam market during the 18th century. In the first chapter I use the primitive communication technology of that time to identify the impact of news on stock price volatility. I find that the arrival of news through sailing boats can explain between 30 and 50% of the price movements of the English stocks in Amsterdam. In the second chapter I provide evidence for the use and revelation of private information in the Amsterdam market. I show that price movements in Amsterdam and London are correlated, even when no information could be transmitted between the two markets. In the final chapter (joint with Hans-Joachim Voth) we study the impact of distressed trade in Amsterdam on stock prices. We show that prices responded immediately to news about the distress, but that actual distressed transactions were delayed. / Esta tesis estudia el negocio en acciones ingleses en el mercado de valores de Amsterdam durante el siglo XVIII. En capítulo uno aplico la comunicación primitiva de esa época para identificar el impacto de noticias sobre la volatilidad de cotizaciones de acciones. Observo que la llegada de noticias mediante barco a vela explica entre 30 y 50% de los movimientos en las cotizaciones de acciones ingleses en el mercado de Amsterdam. En capítulo dos enseño que en Amsterdam se utilizó información privada sobre las acciones ingleses. Muestro una correlación entre los movimientos en las cotizaciones de Amsterdam y Londres, incluso cuando no hay intercambio de información entre los dos mercados. En el último capítulo (junto con Hans-Joachim Voth) estudiamos el impacto de transacciones forzados en Amsterdam sobre las cotizaciones. Mostramos que las cotizaciones responden notablemente a noticias sobre la necesidad de negociar, pero que las transacciones forzados fueron aplazadas.

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

The presence of toxic order ow and predatory HFT strategies in a multi-market setting are scarcely researched in the academic world. This thesis studies the toxicity levels of a set of markets by examining unconsolidated quote data and firm specific trade data. A method for deducing the markets toxicity levels is presented along with proxies for toxic order ow, namely: changes in spread and quoted volume, following a trade in a given market. We find both signs of toxicity and different toxicity levels between the markets. However, the results are lacking in statistical significance but they show that this field is of great interest for further research. Also, the methods proposed for deducing the toxicity levels are rudimentary but could serve well as a premise for further development. / Närvaron av toxic order flow och predatoriska HFT-strategier i en flermarknadsmiljö är föga studerat i den akademiska världen. Denna avhandling studerar detta på en uppsättning marknader genom att undersöka okonsoliderad quote data och firma specifika trades, och på så vis ta fram marknadernas toxicity-nivåer. En metod för att fastställa marknadernas toxicity-nivåer presenteras tillsammans med proxys för toxic order flow, mer specifikt: förändringar i spread och quotad volym, efter en handel på en given marknad. Vi finner både tecken på toxicity och olika toxicityniv åer mellan marknaderna. Resultaten saknar dock statistisk signifikans men de visar ändå på att detta område är av stort intresse för ytterligare forskning. De metoder som föreslås för att fastställa toxicity-nivåerna är rudimentära, men kan tjäna som en utgångspunkt för vidare utveckling.

Thesis (MDevF (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: The importance of market microstructure in determining the success of a bond market in allocating financial resources depends on the degree to which the microstructure elements like liquidity, efficiency and volatility have been designed to determine the proper price at which matching of demand and supply in an efficient and effective manner is done. This research project analyzes some of the fundamental microstructure elements responsible for the current state of the Botswana bond market. The Botswana bond market is still in its infant stage hence there is little information on trades, which contributes to the liquidity problem. The purpose of the study was to investigate the liquidity and efficiency in Botswana’s bond market. The study also sought to compare the behaviour of the Botswana bond market to those of South Africa and further indicate what is behind the bond market emergence. Houweling, Mentink and Vorst‘s (2003) measure was used, in addition to a combination of simple regression and latent models. In the test of efficiency, a static model has been employed. Overall, it is established that the corporate bond market is less efficient and is illiquid. Furthermore, it is revealed that Botswana is still lagging behind South Africa when it comes to the level of development of the corporate bond market.