This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models. The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons. The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.

Pricing options quickly and accurately is a well known problem in finance. Quantum computing is being researched with the hope that quantum computers will be able to price options more efficiently than classical computers. This research extends the quantum binomial option pricing model proposed by Zeqian Chen to European put options and to Barrier options and develops a quantum algorithm to price them. This research produced three key results. First, when Maxwell-Boltzmann statistics are assumed, the quantum binomial model option prices are equivalent to the classical binomial model. Second, options can be priced efficiently on a quantum computer after the circuit has been built. The time complexity is O((N − τ)log(N − τ)) and it is in the BQP quantum computational complexity class. Finally, challenges extending the quantum binomial model to American, Asian and Bermudan options exist as the quantum binomial model does not take early exercise into account. / May 2009

We give a review of classical and recent results on maximization of expected utility for an investor who has the possibility of trading in a financial market. Emphasis will be given to the duality theory related to this convex optimization problem. For expository reasons we first consider the classical case where the underlying probability space is finite. This setting has the advantage that the technical diffculties of the proofs are reduced to a minimum, which allows for a clearer insight into the basic ideas, in particular the crucial role played by the Legendre-transform. In this setting we state and prove an existence and uniqueness theorem for the optimal investment strategy, and its relation to the dual problem; the latter consists in finding an equivalent martingale measure optimal with respect to the conjugate of the utility function. We also discuss economic interpretations of these theorems. We then pass to the general case of an arbitrage-free financial market modeled by an R^d-valued semi-martingale. In this case some regularity conditions have to be imposed in order to obtain an existence result for the primal problem of finding the optimal investment, as well as for a proper duality theory. It turns out that one may give a necessary and sufficient condition, namely a mild condition on the asymptotic behavior of the utility function, its so-called reasonable asymptotic elasticity. This property allows for an economic interpretation motivating the term "reasonable". The remarkable fact is that this regularity condition only pertains to the behavior of the utility function, while we do not have to impose any regularity conditions on the stochastic process modeling the financial market (to be precise: of course, we have to require the arbitrage-freeness of this process in a proper sense; also we have to assume in one of the cases considered below that this process is locally bounded; but otherwise it may be an arbitrary R^d-valued semi-martingale). (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

http://orcid.org/0000-0002-1641-5388 / Pricing options quickly and accurately is a well known problem in finance. Quantum computing is being researched with the hope that quantum computers will be able to price options more efficiently than classical computers. This research extends the quantum binomial option pricing model proposed by Zeqian Chen to European put options and to Barrier options and develops a quantum algorithm to price them. This research produced three key results. First, when Maxwell-Boltzmann statistics are assumed, the quantum binomial model option prices are equivalent to the classical binomial model. Second, options can be priced efficiently on a quantum computer after the circuit has been built. The time complexity is O((N − τ)log(N − τ)) and it is in the BQP quantum computational complexity class. Finally, challenges extending the quantum binomial model to American, Asian and Bermudan options exist as the quantum binomial model does not take early exercise into account. / May 2009

Pricing options quickly and accurately is a well known problem in finance. Quantum computing is being researched with the hope that quantum computers will be able to price options more efficiently than classical computers. This research extends the quantum binomial option pricing model proposed by Zeqian Chen to European put options and to Barrier options and develops a quantum algorithm to price them. This research produced three key results. First, when Maxwell-Boltzmann statistics are assumed, the quantum binomial model option prices are equivalent to the classical binomial model. Second, options can be priced efficiently on a quantum computer after the circuit has been built. The time complexity is O((N − τ)log(N − τ)) and it is in the BQP quantum computational complexity class. Finally, challenges extending the quantum binomial model to American, Asian and Bermudan options exist as the quantum binomial model does not take early exercise into account.

This document is a Master Thesis report in financial mathematics for KTH. This Master thesis is the product of an internship conducted at Nexialog Consulting, in Paris. This document is about the innovative use of Constrained Gaussian process regression in order to build an arbitrage free swaption cube. The methodology introduced in the document is used on a data set of European Swaptions Out of the Money. / Det här dokumentet är en magisteruppsats i finansiel matematik på KTH. Detta examensarbete är resultatet av en praktik som ufördes på Nexialog Consulting i Paris.Detta dokument handlar om den innovativa användningen av regression för gaussiska processer med bivillkor för att bygga en arbitragefri swaption kub. Den metodik som introduceras i dokumentet används på en datamängd av europeiska swaptions som är "Out of the Money".

This thesis proposes a theory of inefficient markets that uses limited rational choice as a central trait and I call it the theory of Homo comperiens. The theory limits the alternatives and states that the subjects are aware of and only allow them to have rational preference relations on the limited action set and state set, i.e. limited rationality is introduced. With limited rational choice, I drive a wedge between the market price and the intrinsic value and thus create an arbitrage market. In the theory, the subjects are allowed to gain knowledge about something that they previously were unaware of. As the discovery proceeds, the arbitrage opportunities disappear, and the market prices regress towards the intrinsic values. The theory is applied to firms and market-pricing models for a Homo comperiens environment is a result. The application of the theory to firms also leads to testable propositions that I test on a uniquely comprehensive Swedish accounting database that cover the years 1978—1994. Hypotheses are tested which argues that risk-adjusted residual rates-of-returns exist. The null hypotheses argue that risk-adjusted residual rates-of-returns do not exist (since they assume a no-arbitrage market). The null hypotheses are rejected in favor of their alternatives at a 0.0 percent significance level. The tests use approximately 22,200 observations. I also test hypotheses which argue that risk-adjusted residual rates-of-returns regress to zero with time. The null hypotheses are randomly walking risk-adjusted residual rates-of-returns, which are rejected in favor of the alternative hypotheses. The hypotheses are tested using panel regression models and goodness-of-fit tests. I reject the null hypotheses of random walk at a 0.0 percent significance level. Finally, the results are validated using out-of-sample predictions where my models compete with random-walk predictions. It finds that the absolute prediction errors from my models are between 12 to 24 percent less than the errors from the random walk model. These results are significant at a 0.0 percent significance level.

In the first part of this thesis, we introduce a new methodology for stress-test exercises. Our approach allows to consider richer stress-test exercises, which assess the impact of a modification of the whole distribution of asset prices' factors, rather than focusing as the common practices on a single realization of these factors, and take into account the potential reaction to the shock of the portfolio manager. The second part of the thesis is devoted to the pricing of bonds with very long-term time-to-maturity (more than ten years). Modeling the volatility of very long-term rates is a challenge, due to the constraints put by no-arbitrage assumption. As a consequence, most of the no-arbitrage term structure models assume a constant limiting rate (of infinite maturity). The second chapter investigates the compatibility of the so-called "level" factor, whose variations have a uniform impact on the modeled yield curve, with the no-arbitrage assumptions. We introduce in the third chapter a new class of arbitrage-free term structure factor models, which allows the limiting rate to be stochastic, and present its empirical properties on a dataset of US T-Bonds.

Current economic situation is characterized for deflation and low inflation, low economic growth, and low or negative interest rates, which lead to phenomenon of issuing governments bonds with negative yield. The main goal of this work is to understand the valuation and behavior of bonds with condition of negative interest rates, analyze impacts of negative rates on volatility of bonds. This work also compares the behavior of negative yields of bonds in contrast with positive yields. The contribution of this work consists in the critical evaluation of limitations of the formula for calculating the bond price to fulfill its role if the values of negative interest rates are too low.

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