本文採用1998年九月2日到2003九月30日的台灣指數期貨每日收盤價，總共1304筆資料。我們希望能藉由濾嘴法則以收盤價及交易量和未平倉量來衡量台灣指數期貨的效率性。而實證結果也證實可以藉由濾嘴法則濾除掉市場上的小波動，並進而預測出主要的價格趨勢。 / This thesis adopts futures data, which are the daily closing prices of the Taiwan Stock Exchange Capitalization Weighted Stock Index futures contracts. The sample period is from September 2, 1998 to September 30, 2003, a total of 1304 transaction days. The goal we want to achieve is to test and verify the momentum by filter rules based on price and volume in the futures market in Taiwan. In addition, the open interest is substituted for the trading volume to exam its effect on the futures price. The empirical results show that we can predict the price trend as long as we employ an appropriate range value to filter out “the noise”.

This dissertation studies the cost of carry relationship and the international dynamics of mispricing, price and volatility in the three Nikkei futures markets - the Osaka Exchange (OSE), the Singapore Exchange (SGX) and the Chicago Mercantile Exchange (CME). Previous research does not fully consider the unique characteristics of the triple-listed Nikkei futures contracts, or the price and volatility dynamics in the three Nikkei futures exchanges at the same time. This dissertation makes a significant contribution to the existing literature. In particular, with a comprehensive new 19-year sample period, this dissertation helps deepen the understanding of the Nikkei spot-futures equilibrium and arbitrage behaviour, cross-border information transmission mechanism, and futures market integration. The first topic of the dissertation is to study the cost of carry relationship, mispricing and index arbitrage in the three Nikkei markets. The standard cost of carry model is adjusted for each Nikkei futures contract by allowing for the triple-listing nature and key institutional differences. Based on this, the economic significance of the Nikkei mispricing is explored in the presence of transaction costs. The static behaviour of the mispricing suggests that it is difficult especially for institutional investors to make arbitrage profits in the OSE and SGX, and that index arbitrage in the CME is not strictly risk-free due to the exchange rate effect. Smooth transition models are used to study the dynamic behaviour of the mispricing in the three markets. The results show that mean reversion in mispricing and limits to arbitrage are driven more by transaction costs than by heterogeneous arbitrageurs in the Nikkei markets. The second topic of the dissertation is to investigate the price discovery process in individual Nikkei markets and across the Nikkei futures markets. With smooth transition error correction models, this dissertation reports the leading role of the futures prices in the pre-crisis period and the leading role of the spot prices in the post-crisis period, in the first-moment information transmission process. Moreover, there is evidence of asymmetric adjustments in the Nikkei prices and volatilities. The cross-border dynamics suggest that the foreign Nikkei markets (the CME and SGX) act as the main price discovery vehicle, which implies the key functions of the equivalent, offshore markets in futures market globalisation. The third topic of the dissertation is to study the volatility transmission process in individual Nikkei markets and across the Nikkei futures markets, from the perspectives of the volatility interactions in and across the Nikkei markets and of the dynamic Nikkei market linkages. This dissertation finds bidirectional volatility spillover effects between the Nikkei spot and futures markets, and the information leadership of the foreign Nikkei markets (the CME and SGX) in the second-moment information transmission process across the border. It further examines the dynamic conditional correlations between the Nikkei markets. The results point to a dramatic integration process with strongly persistent and stable Nikkei market co-movements over time.

O objetivo desta tese foi avaliar algumas abordagens para utilizar os mercados futuros e de opções no Brasil e no exterior como ferramentas para gestão estratégica da produção de soja em Mato Grosso. Apresentam-se duas linhas de trabalho na pesquisa. Na primeira, a estrutura a termo das opções com vencimento futuro negociadas no CME Group foi obtida para efetuar previsões da volatilidade e do nível de preços realizados, no curto e longo prazo, para os preços a vista da soja negociada em Rondonópolis (MT). Através da extração da volatilidade implícita do modelo de Black (1976) para precificação de opções de commodities, decompôs-se a variância da volatilidade em intervalos conhecidos e não conhecidos, para os quais se fez previsões de curto e longo prazo. Usou-se também a volatilidade implícita como parâmetro numa equação de intervalos de confiança empíricos para a estimação do nível de preços, no curto e longo prazo. Os testes de eficiência preditiva indicaram que as previsões da volatilidade realizada com base na volatilidade implícita têm maior grau de eficiência no curto prazo, enquanto as previsões dos níveis de preço são mais eficientes no longo prazo. Pode-se atribuir os resultados às características intrínsecas da série de preços da soja, em particular a tendência de reversão à média e o agrupamento de volatilidades. Na segunda abordagem, a decisão de hedge simultâneo dos produtores de soja de Mato Grosso com contratos futuros de preço e taxa de câmbio da BOVESPA-BM&F foi analisada. Um modelo de hedge simultâneo do risco de preços e taxa de câmbio foi obtido e as eficiências de diferentes estratégias de hedge foram calculadas. As principais conclusões foram que o hedge simultâneo de risco de preços e taxa de câmbio reduz mais o risco da receita total do que apenas o hedge de preços. A mitigação do risco de taxa de câmbio em conjunto com o de preços é fundamental para uma gestão estratégica dos exportadores de commodities. / This dissertation objective was the evaluation of some approaches to use the Brazilian and foreign futures and options markets as a strategic management mechanism for the soybean production in Mato Grosso. Two research topics are presented. In the first, the term structure of options with future maturities traded at the CME Group was obtained to make realized volatility and price level short and long term forecasts of the soybeans spot prices traded in Rondonopolis (MT). By extracting the implied volatility using the Black (1976) model for commodities option pricing, the volatility variance is decomposed in known and unknown intervals, for which predictions of short and long term values were made. Also the implied volatility was used as a parameter in an equation of the empirical confidence intervals for the estimation of the price level in the short and long term. Predictive efficiency tests indicated that the forecasts of realized volatility based on implied volatility show a greater degree of efficiency in the short term, while estimates of price levels are more efficient in the long term. These results can be assigned to the intrinsic characteristics of the soybean price series, in particular its tendency for mean reversion and volatility clustering. In the second essay, the joint hedging decision of the soybean producers of Mato Grosso with price and exchange rate futures contracts of BOVESPA-BM&F was analyzed. A simultaneous price and exchange risk hedging model was obtained and the efficiencies of different hedging strategies was calculated. The main findings were that the simultaneous hedging of price and exchange rate risk reduce more revenue risk than hedging with price futures only. The exchange risk jointly with price risk offset is key for a strategic management of commodities exporters.

O problema estudado nesta tese foi a formação de preços de commodities agrícolas relacionadas com a produção de bioenergia. A possibilidade de substiuição de combustíveis fósseis, derivados do petróleo, por alternativas renováveis, como o etanol proveniente da cana-de-açúcar, inseriu um novo contexto no mercado de commodities. O objetivo principal desta tese foi propor um modelo de formação de preços que levasse em consideração o conceito de commodities agrícolas como componentes da matriz energética e aplicá-lo ao mercado brasileiro de açúcar e álcool. Para a especificação do modelo, foram elaboradas premissas que têm base na interdependência de preços com o petróleo, na Teoria de Estocagem, na sazonalidade das safras e na volatilidade do mercado. A volatilidade foi considerada no estudo porque, em períodos de turbulência econômica, os investidores buscam o mercado de commodities para proteger o valor real do capital. Para elaborar e testar o modelo, a pesquisa foi dividida em quatro partes interrelacionadas. A primeira consistiu na análise de outros modelos da literatura e dos processos estocásticos descritos pelas variáveis que compõem os preços das commodities. A segunda parte consistiu na elaboração das premissas, dispostas na forma de hipóteses, que foram testadas com dados do mercado futuro de açúcar e álcool da BM&FBOVESPA e com preços do mercado agrícola divulgados pelo CEPEA. A amostra analisada compreendeu o período de 2 de janeiro de 2002 a 30 de junho de 2008. Nesta etapa foram utilizados, entre outros métodos, testes estatísticos de significância de coeficientes de regressões multivaridadas pelo Método dos Mínimos Quadrados. Os resultados dos testes indicaram que o açúcar Granger causa os preços do etanol, confirmaram a presença de backwardation forte nas séries de preços, confirmaram também que os preços do petróleo antecipam informações sobre a tendência dos preços do açúcar, com defasagem de um mês. Além desses resultados, confirmou-se a presença de sazonalidade e, verificou-se, de forma não conclusiva, uma relação positiva entre a volatilidade dos mercados e os preços do açúcar. Na terceira parte da pesquisa, o modelo foi especificado em um sistema de três equações na forma de espaço de estado, cujos parâmetros foram estimados por meio do filtro de Kalman. Na quarta e última parte da pesquisa, foram viii geradas séries de previsão n passos à frente utilizando os parâmetros estimados e os resultados confrontados com os preços observados do açúcar no mercado à vista. Medidas de erros de previsão foram calculadas e comparadas com as de um outro modelo na literatura, adequado ao mercado de brasileiro açúcar o modelo de dois fatores. Verificou-se que o modelo proposto é estatisticamente superior, em termos de previsão, ao modelo de dois fatores, no nível de 1% de significância. Verificou-se também que, quanto maior o horizonte de previsão, maior é o ganho de informação relativo do modelo proposto. A redução percentual de erros foi superior a 10%, quando analisada a previsão de três meses à frente. Portanto, foi possível concluir que o modelo, que incorpora a interdependência do petróleo na formação de preços de commodities relacionadas à produção de biocombustíveis, é melhor, em termos de previsão, do que um outro modelo sugerido na literatura que não leva essa premissa em consideração, quando aplicado ao mercado brasileiro de açúcar. Os resultados da pesquisa podem ter aplicação pragmática em Administração de Empresas do setor sucroalcooleiro e na formação de preços no mercado de derivativos de commodities agrícolas. / The problem addressed by this thesis is the formation of prices of agricultural commodities related to bioenergy production. The possibility of substituting renewable alternatives, such as ethanol derived from sugar cane, for fossil fuels derived from petroleum has brought a new dimension to commodity markets. The primary objective of this thesis is to propose a pricing model which takes into account the concept of agricultural commodities as components of the energy matrix and to apply it to the Brazilian sugar and ethanol markets. The bases of the premises used to build this model were oil-price interdependence, inventory theory, harvest seasonality, and market volatility. Volatility was considered in the study because, in times of economic turbulence, investors turn to the commodities market to protect the real value of their capital. Four interrelated research tasks were undertaken to develop and test the model. The first step consisted of a literature analysis of other models and the stochastic processes to which variables that influence commodity prices are subject. The second part consisted of the elaboration of presumptions in the form of hypotheses which were tested using data from the BM&FBOVEPSA sugar and ethanol futures markets and agricultural prices published by CEPEA, with the test period being from January 2, 2002, to June 30, 2008. This step employed, among other methods, tests of stastical significance with multivariate regressions using the method of least squares. The test results indicated that sugar Granger causes ethanol prices, that strong backwardation exists in the price series, and that petroleum prices are predictive of sugar price patterns, with a one-month lag. In addition, the results confirmed the influence of seasonality and pointed, albeit somewhat inconclusively, to a positive relationship between market volatility and sugar prices. In the third step of the study, a model was defined using a system of three equations in state space form with parameters estimated using Kalman filtering. The fourth and last stage of the research generated series projections n steps forward using the estimated parameters, and the results were compared to sugar prices observed in the market. Measures of predictive error were calculated and compared with those of another model cited in literature as applicable to the Brazilian sugar market the two-factor model. The proposed model proved itself to be statistically superior, in terms of predictiveness, to the two-factor model, at the level of 1% significance. Moreover, x its predictive superiority rose as the period of time analyzed increased. The error reduction in percentage terms was greater than 10% over a forward-looking period of three months. Therefore, it is possible to conclude, in the context of the Brazilian sugar market, that this model, which incorporates petroleum interdependence in the formation of prices of commodities related to biofuel production, is better, in terms of predictive power, than another model cited in literature that does not take this premise into consideration. The results of this research can be applied in the management of sugar-alcohol companies and in the formation of derivatives prices in the agricultural commodities market.

The presented work deals with analysis and implementation of algorithmic trading applications based on client requirements. Applications developed in this work are supposed to be used to collect and manage data from the stock exchange, to view information about active trading orders, and to send trading orders to the exchange via the API from Interactive Brokers. The first chapter gives an overview of selected books focused on developing applications for C # and analysis. Then the concepts of UML, OOAD, and UP are introduced. In the next chapter, requirements of the customer are defined. In the following chapter, based on the results of literature research and defined client requirements, the initial architectural design is created and cases of use with subsequent specifications are presented. This section is followed by finding analytical classes, creating a domain model, implementation of some use cases using sequence diagrams. The last two chapters describe the implementation details - the language used, the libraries, database schema, and user manual.

When a stock is added into the S&P 500 Index, it is automatically "cross-listed" in the index derivative markets (i.e., S&P 500 Index futures and Index options). I examined the effects of such cross-listing on the trading volume and return volatility of the underlying component stocks. Traditional finance theory asserts that futures and "cash" markets are connected by arbitrage mechanism that brings both markets to equilibrium. When arbitrage opportunities arise, arbitrageurs buy (sell) the index portfolio and take short (long) positions in the corresponding index derivative contracts until prices return to theoretical levels. Such mechanical arbitrage trading tends to create large order flows that could be difficult for the market to absorb, resulting in price changes. Utilizing a list of S&P 500 index composition changes occurring over the period September 1976 to December 2005, I investigated the market-adjusted volume turnover ratios and return variances of the stocks being added to and deleted from the S&P 500, surrounding the effective day of index membership changes. My primary finding is that, after the introduction of the S&P 500 index futures and options contracts, stocks added to the S&P 500 experience significant increase in both trading volume and return volatility. However, deleted stocks experience no significant change in either trading volume or return volatility. Both daily and monthly return variances increase following index inclusion, consistent with the hypothesis that derivative transactions "fundamentally" destabilize the underlying securities. I argue that the increase in trading volume and return volatility may be attributed to index arbitrage transactions as derivative markets provide more routes for index arbitrageurs to trade. Other index trading strategies such as portfolio insurance and program trading may also contribute to the results. On the other hand, a deleted stock is not associated with changes in trading volume and volatility since it represents an extremely small fraction of the market value-weighted index portfolio, and the influence of index trading strategies becomes slight for these shares. Furthermore, evidence is provided that trading volume and return volatility are positively related.

Machine learning models has gained traction as an effective tool for short-term electricity price forecasting, namely day ahead and hourly price forecasting. Efficient and accurate forecasting is crucial for demand and capacity planning to ensure stability and optimal use of resources. This project applies two proven machine learning models, LSTM and TCN, to electricity futures contracts in the Swedish pricing areas SE1 and SE3. Future contracts are used to secure the price of electricity in the future. A multivariate time series of fundamental data that correlates with electricity prices is used as input for the forecasting. Fur- thermore, a portfolio approach for hedging is evaluated based on the predictive performance of the models. The forecasting accuracy of the multivariate TCN model outperform the LSTM model. The optimal hedging strategy based on the TCN model indicated potential cost savings of 1.43% compared to a benchmark method. / Maskininlärnings modeller har vunnit mark som effektiva verktyg för att prognosticera kortsiktiga elpriser, för dagen före och timpriser. Effektiv och korrekt prognosticering är viktigt för att skatta behovs- och kapacitetsplanering för optimal resursanvändning. Det här projektet applicerar två välbeprövade modeller, LSTM och TCN, för att prognosticera terminskontrakt i de två svenska pris- områdena SE1 och SE3. Terminskontrakt används för att säkra elpriser i framtiden. En tidsserie, med flera variabler av fundamental data som korrelerar med elpriser, används för att prognosticera elpriser. Vidare utvärderas en portfölj approach för prissäkring baserat på prognoserna från modellerna. TCN modellen gav högre noggrannhet än LSTM modellen. Optimal prissäkringsstrategi baserad på TCN modeller resulterade i 1.43% lägre elpriser jämfört med bench- marks.

The aim of this thesis is to conduct a nuanced investigation of connectedness in the global futures market across time and market conditions through a Quantile Vector Autoregression (QVAR) model. Later, a linear regression is utilized to identify determinants of futures market connectedness across market conditions. The sample period consists of daily data from December 2017 to August 2023. Our dataset includes five uncertainties and 19 continuous futures contracts, making it the most comprehensive study of futures market connectedness after the Russian invasion of Ukraine to our knowledge.  The results highlight heterogeneous effects across time and market conditions for all assets, with the futures market connectedness increasing during times of uncertainty. US equity, German Equity, Japanese equity, British equity, gold, silver, USD and EUR are identified as net transmitters of spillovers, whereas the rest of the futures are identified as net receivers. These findings are interesting in the concept of theory as they highlight potential periods of flight-to-safety and safe haven properties for certain futures. When including uncertainties in the QVAR model, financial uncertainty is identified as the only net transmitter, whereas the other uncertainties are net receivers.  Drivers of futures market connectedness depend on market conditions and time, with energy uncertainty being significant for normal markets and the world equity index being significant for bearish markets in both the full sample and a Covid-19 subsample. For the full sample only, financialization is identified as a driver during bullish markets. More variables are significant for the Covid-19 subsample. The commodity index and US dollar index becomes significant in bearish markets and monetary uncertainty in bullish markets.  Our findings are relevant for both investors and policymakers. The results suggest that investors should monitor market conditions when investing in the futures market to suitably optimize, diversify, and hedge their portfolios. For policymakers, monitoring spillover from the futures market is important as it can impact the overall economy by using the industrial sector as a transmission channel. This can aid in early decision-making and minimize the impact of economic downturns. / Das Ziel dieser Arbeit ist, eine nuancierte Untersuchung der Verbundenheit im globalen Terminmarkt über Zeit und Marktbedingungen durch ein Quantile Vector Autoregression (QVAR) Modell durchzuführen. Später benutzen wir eine lineare Regression, um Determinanten der Terminmarktverbundenheit unter verschiedene Marktbedingungen zu identifizieren. Der Zeitraum dieser Untersuchung besteht aus täglichen Daten von Dezember 2017 bis August 2023. Die Daten umfasst fünf Unsicherheitsmaße und 19 kontinuierliche Terminkontrakte, damit ist es nach unserem Wissen die umfassendste Untersuchung über die Verbundenheit des Terminmarkts nach der russischen Invasion die Ukraine.  Die Ergebnisse hervorheben heterogene Effekte über Zeit und Marktbedingungen für alle Variablen, wobei die Verbundenheit des Terminmarkts während unsicherer Perioden verstärkt ist. Der amerikanische Aktienindex, deutsche Aktienindex, japanische Aktienindex, britische Aktienindex, Gold, Silber, US-Dollar und Euro werden als Nettoübermittler von Spillovern identifiziert, während die andere Terminkontrakte als Nettoempfänger identifiziert werden. Die Ergebnisse sind interessant im Kontext der Theorie, da sie sowohl potenzielle Perioden von Flight-to-Safety als auch Safe Haven-Eigenschaften für die Terminkontrakte hinweisen. Bei der Einbeziehung von Unsicherheitsmaßen in das QVAR-Modell wird die finanzielle Unsicherheit als einziger Nettoübermittler identifiziert, während die anderen Unsicherheiten Nettoempfänger sind.  Die Determinanten der Verbundenheit an den Terminmarkt sind von Zeit und Marktbedingungen abhängig, wobei die Energieunsicherheit für normale Marktbedingungen und die Weltaktienindex für bärische Marktbedingungen während sowohl des ganzen Zeitraums als auch des Covid-19 Zeitraums signifikant ist. Finanzialisierung ist nur während des ganzen Zeitraums als Determinant für bullische Marktbedingungen signifikant. Im Covid-19 Zeitraum sind weitere Variablen signifikant. Der Rohstoffindex wird in bärische Marktbedingungen und die US-Dollar-Index wird in bullische Marktbedingungen signifikant.  Die Ergebnisse dieser Untersuchung sind sowohl für Investoren als auch für politische und finanzielle Entscheidungsträger relevant. Die Ergebnisse andeuten, dass Investoren die Marktbedingungen beobachten sollten, wenn sie in den Terminmarkt investieren, um ihre Portfolios zu optimieren, diversifizieren und abzusichern. Für politische und finanzielle Entscheidungsträger ist die Beobachtung von Spillover-Effekten vom Terminmarkt wichtig, da sie auf die Gesamtwirtschaft durch die Industriesektor auswirken können. Darum kann diese kontinuierliche Beobachtung zu früheren makroökonomischen Entscheidungen führen und damit ungünstige wirtschaftliche Auswirkungen minimieren.

Agriculture is of inestimable value to South Africa because it is a major source of job creation and plays a key role in earning foreign exchange. The most significant contribution of agriculture, and in particular maize, is its ability to provide food for the nation. For a number of decades government legislation determined prices, and as such the trade of grains on the futures exchange requires market participants to adapt to a volatile environment. The research focuses on the ability of market participants to effectively mitigate price volatility on the futures exchange through the use of derivative instruments, and the possibility of developing risk management strategies that will outperform the return offered by the market. The study shows that market participants are unable to use derivative instruments in such a way that price volatility is minimised. The findings of the study also indicate that the development of derivative risk management strategies could result in better returns than those offered by the market, mainly by exploiting trends on the futures market. / Financial Accounting / M. Comm. (Business Management)

本論文係研究期貨契約（Futures Contracts ）屏障（Hedge ）價格及利率風險之功 能，並對美國財務會計準則委員會所發布之第十八號會計準則，有關期貨契約會計處 理之規定，做一深入探討，以為國內未來發展期貨市場之參考。 全文共分五章，約六萬字。 第一章 緒論。說明本論文寫作之動機、目的、限制及研究方法。 第二章 探討期貨契約及期貨市場之發展、性質及期貨價格之習性，並對重要名辭加 以界定。 第三章 介紹屏障功能之性質、理論、風險及其有效性分析，並舉例說明。 第四章 介紹美國財務會計準則委員會第八十號會計準則發布始未，及期對期貨契約 會計處理之有關規定等。 第五章 結論。綜合以上各章之討論，做一總結，並提出適當建議。