This article presents a new approach for calculating sensitivities of European swaptions. The sensitivities are found by applying an adjoint method to a stochastic volatility model, namely the SABR/LIBOR market model. This market model predicts the volatility smile and follows the market fluctuations more accurately than earlier used deterministic volatility market models for complex derivatives. The new adjoint method involves not only sensitivity calculations, it also presents a way of estimating the time discretization error using an a posteriori approach. The error calculation is described in this document but not investigated further. The first step in order to calculate the sensitivities is to calibrate the SABR/LIBOR market model to some market data. In our calculations we used data from June 15 2011 with 6 month intervals between the maturity times. When this calibration is complete all of the parameters in the SABR/LIBOR market model are specified and we can continue with the sensitivity calculations using the new adjoint method. The results from these calculations show that the method is a good choice for estimating sensitivities if we consider a complex financial derivative like the European swaption. The method is quite computational so we recommend that it is only used on a small number of securities with respect to a large number of parameters. The method provides more market-driven price and sensitivity estimations than earlier used methods and can benefit hedging of portfolios.

Aircraft design has recently experienced a trend away from performance centric design towards a more balanced approach with increased emphasis on engineering an economically successful system. This approach focuses on bringing forward a comprehensive economic and life-cycle cost analysis, which can be addressed by the introduction of a dynamic method allowing the analysis of the future attractiveness of such a concept in the presence of uncertainty. One way of addressing this is through the use of a competitive market model. However, existing market models do not focus on the dynamics of the market, which results in poor predictive capabilities. The method proposed here focuses on a top-down approach that integrates a competitive model based on work in the field of system dynamics into the aircraft design process. The primary contribution is the demonstration of the feasibility of such integration. This integration is achieved through the use of surrogate models, which enabled not only the practical integration of analysis techniques, but also reduced the computational requirements so that interactive exploration as envisioned is actually possible. An example demonstration of this integration is built on the competition in the 250 seat large commercial aircraft market. Two aircraft models were calibrated to existing performance and certification data and then integrated into the system dynamics market model, which was then calibrated with historical market data. This calibration showed a much improved predictive capability as compared to the conventional logit regression models. The resulting market model was then integrated into a prediction profiler environment with a time variant Monte-Carlo analysis resulting in a unique trade-off environment. This environment was shown to allow interactive trade-off between aircraft design decisions and economic considerations while allowing the exploration potential market success in the light of varying external market conditions and scenarios. Another use of the existing outputs of the Monte-Carlo analysis was then realized by visualizing the model variables on a multivariate scatter plot. This enables the designer to define strategic market and return on investment goals for a number of scenarios and then directly see which specific aircraft designs meet these goals.

Analyzing different aspects of the supply chain aids in understanding how firms behave, interact and respond within an industry. Some concepts used to carry out this analysis include asymmetric price transmission, event study methodology and event costing analysis. Each of these topics is discussed in this dissertation, presented as a set of three separate papers. The first paper analyzes asymmetric price transmission and elasticities of price transmission at the farm-retail level for whole and two percent milk in selected cities in the United States. The theoretical core of this paper relies on a comparison between the traditional Houck approach and the error correction model proposed by von Cramon- Taubadel and Fahlbusch. We reject the null hypothesis of symmetry for each product and city under both approaches. We also find little evidence of statistical superiority between the classic Houck approach and the error correction model. The second paper uses financial market event study methodology to calculate the economic impact on the supply chain related to one of the worst disease outbreaks in the food industry in the United States. This event began on November 3, 2003, when the Associated Press reported a hepatitis advisory in the Beaver Valley, Pennsylvania. This outbreak directly involved two publicly traded companies: Prandium and Sysco. The market model is used as the main foundation of the economic analysis. There is no evidence of abnormal rates of return or spillover effects in relation to the outbreak. However, there is evidence that volatility of returns increases after the event. The third paper develops a general conceptual economic module to quantify the impact of an animal disease outbreak. This study develops a generic economic module, which estimates cost in the face of a simulated animal disease outbreak under different mitigation strategies. This model was subsequently applied in a case study: a hypothetical case of a foot-and-mouth (FMD) outbreak in the Texas Panhandle analyzed under five different ex-post mitigation strategies. The results show that the most effective strategy is to slaughter and not to vaccinate. We conclude that analyzing the supply chain is important in understanding how markets behave.

This thesis consists of three papers in the area of interest rate derivatives modelling. The pricing and hedging of (exotic) interest rate derivatives is one of the most demanding and complex problems in option pricing theory and is of great practical importance in the market. Models used in production at various banks can broadly be divided in three groups: 1- or 2-factor instantaneous short/forward rate models (such as Hull &amp; White (1990) or Cheyette (1996)), LIBOR/swap market models (introduced by Brace, Gatarek &amp; Musiela (1997), Miltersen, Sandmann &amp; Sondermannn (1997) and Jamshidian (1997)) and the one or two-dimensional Markov-functional models of Hunt, Kennedy &amp; Pelsser (2000)). In brief and general terms the main characters of the above mentioned three modelling frameworks can be summarised as follows. Short/forward rate models are by nature computationally efficient (implementations may be done using PDE or lattice methods) but less flexible in terms of fitting of implied volatility smiles and correlations between various rates. Calibration is hence typically performed in a ‘local’ (product by product based) sense. LIBOR market models on the other hand may be calibrated in a ‘global’ sense (i.e. fitting close to everything implying that one calibration may in principle be used for all products) but are of high dimension and an accurate implementation has to be done using the Monte Carlo method. Finally, Markov-functional models can be viewed as designed to combine the computational efficiency of short/forward rate models with flexible calibration properties. The defining property of a Markov-functional model is that each rate and discount factor at all times can be written as functionals of some (preferably computationally simple) Markovian driving process. While this is a property of most commonly used interest rate models Hunt et al. (2000) introduced a technique to numerically determine a set of functional forms consistent with market prices of vanilla options across strikes and expiries. The term a ‘Markov-functional model’ is typically referring to this type of model as opposed to the more general meaning, a terminology that is adopted also in this thesis. Although Markov-functional models are indeed a popular choice in practice there are a few outstanding points on the practitioners’ wish list. From a conceptual point of view there is still work to be done in order to fully understand the implications of various modelling choices and how to efficiently calibrate and use the model. Part of the reason for this is that while the properties of the short/forward rate and the LIBOR market models may be understood from their defining SDEs this is less clear for a Markov-functional model. To aid the understanding of the Markov-functional model Bennett &amp; Kennedy (2005) compares one-dimensional LIBOR and swap Markov-functional models with the one-factor separable LIBOR and swap market models and concludes that the models are similar distributionally across a wide range of viable market conditions. Although this provides good intuition there is still more work to be done in order to fully understand the implications of various modelling choices, in particular in a two or higher dimensional setting. The first two papers in this thesis treat extensions of the standard Markov-functional model to be able to use a higher dimensional driving process. This allows a more general understanding of the Markov-functional modelling framework and enables comparisons with multi-factor LIBOR market models. From a practical point of view it provides more powerful modelling of correlations among rates and hence a better examination and control of some types of exotic products. Another desire among practitioners is to develop an efficient way of using a process of stochastic volatility type as a driver in a Markov-functional model. A stochastic volatility Markov-functional model has the virtue of both being able to fit current market prices across strikes and to provide better control over the future evolution of rates and volatilities, something which is important both for pricing of certain products and for risk management. Although there are some technical challenges to be solved in order to develop an efficient stochastic volatility Markov-functional model there are also many (more practical) considerations to take into account when choosing which type of driver to use. To shed light on this the third paper in the thesis performs a data driven study in order to motivate and develop a suitable two-dimensional stochastic volatility process for the level of interest rates. While the main part of the paper is general and not directly linked to any complete interest rate model for exotic derivatives, particular care is taken to examine and equip the process with properties that will aid use as a driver for a stochastic volatility Markov-functional model. / <p>Diss. Stockholm :  Stockholm School of Economics, 2011. Introduction together with 3 papers</p>

This DPhil thesis is a collection of three theoretical and empirical papers studying labor markets in several advanced economies. Two chapters examine the relationship between the banking sector and the labor market in the US and the UK, while one evaluates a policy that has been proposed to help labor markets in the Euro Area adjust to economic shocks. In the first chapter, I develop a New Keynesian DSGE model that integrates a banking sector subject to moral hazard with a standard random search model of the labor market. I estimate the model using US data and study the role of the banking sector in determining labor market fluctuations. In the second chapter, I estimate a structural VAR model of the UK and US economies and identify bank lending shocks using a mix of sign and short-run exclusion restrictions. Consistent with the predictions of the DSGE model, an expansionary loan supply shock decreases job-destruction and increases job-creation, reducing the unemployment rate persistently. Bank lending shocks are also important drivers of labor market fluctuations, particularly during the Great Recession. Lastly, in the third chapter, I calibrate to the Euro Area a currency union DSGE model to evaluate the aggregate properties of European Unemployment Insurance (EUI). I find that EUI cannot contemporaneously stabilize the monetary union and achieve convergence in regional unemployment and inflation rates.

Instantaneous rate models, although theoretically satisfying, are less so in practice. Instantaneous rates are not observable and calibra- tion to market data is complicated. Hence, the need for a market model where one models LIBOR rates seems imperative. In this modeling process, we aim at regaining the Black-76 formula[7] for pricing caps and °oors since these are the ones used in the market. To regain the Black-76 formula we have to model the LIBOR rates as log-normal processes. The whole construction method means calibration by using market data for caps, °oors and swaptions is straightforward. Brace, Gatarek and Musiela[8] and, Miltersen, Sandmann and Sondermann[25] showed that it is possible to con- struct an arbitrage-free interest rate model in which the LIBOR rates follow a log-normal process leading to Black-type pricing for- mulae for caps and °oors. The key to their approach is to start directly with modeling observed market rates, LIBOR rates in this case, instead of instantaneous spot rates or forward rates. There- after, the market models, which are consistent and arbitrage-free[6], [22], [8], can be used to price more exotic instruments. This model is known as the LIBOR Market Model. In a similar fashion, Jamshidian[22] (1998) showed how to con- struct an arbitrage-free interest rate model that yields Black-type pricing formulae for a certain set of swaptions. In this particular case, one starts with modeling forward swap rates as log-normal processes. This model is known as the Swap Market Model. Some of the advantages of market models as compared to other traditional models are that market models imply pricing formulae for caplets, °oorlets or swaptions that correspond to market practice. Consequently, calibration of such models is relatively simple[8]. The plan of this work is as follows. Firstly, we present an em- pirical analysis of the standard risk-neutral valuation approach, the forward risk-adjusted valuation approach, and elaborate the pro- cess of computing the forward risk-adjusted measure. Secondly, we present the formulation of the LIBOR and Swap market models based on a ¯nite number of bond prices[6], [8]. The technique used will enable us to formulate and name a new model for the South African market, the SAFEX-JIBAR model. In [5], a new approach for the estimation of the volatility of the instantaneous short interest rate was proposed. A relationship between observed LIBOR rates and certain unobserved instantaneous forward rates was established. Since data are observed discretely in time, the stochastic dynamics for these rates were determined un- der the corresponding risk-neutral measure and a ¯ltering estimation algorithm for the time-discretised interest rate dynamics was pro- posed. Thirdly, the SAFEX-JIBAR market model is formulated based on the assumption that the forward JIBAR rates follow a log-normal process. Formulae of the Black-type are deduced and applied to the pricing of a Rand Merchant Bank cap/°oor. In addition, the corre- sponding formulae for the Greeks are deduced. The JIBAR is then compared to other well known models by numerical results. Lastly, we perform some computational analysis in the following manner. We generate bond and caplet prices using Hull's [19] stan- dard market model and calibrate the LIBOR model to the cap curve, i.e determine the implied volatilities ¾i's which can then be used to assess the volatility most appropriate for pricing the instrument under consideration. Having done that, we calibrate the Ho-Lee model to the bond curve obtained by our standard market model. We numerically compute caplet prices using the Black-76 formula for caplets and compare these prices to the ones obtained using the standard market model. Finally we compute and compare swaption prices obtained by our standard market model and by the LIBOR model. / Economics / D.Phil. (Operations Research)

This thesis focuses on the non-arbitrage (fair) pricing of interest rate derivatives, in particular caplets and swaptions using the LIBOR market model (LMM) developed by Brace, Gatarek, and Musiela (1997) and Swap market model (SMM) developed Jamshidan (1997), respectively. Today, in most financial markets, interest rate derivatives are priced using the renowned Black-Scholes formula developed by Black and Scholes (1973). We present new pricing models for caplets and swaptions, which can be implemented in the financial market other than the Black-Scholes model. We theoretically construct these "new market models" and then test their practical aspects. We show that the dynamics of the LMM imply a pricing formula for caplets that has the same structure as the Black-Scholes pricing formula for a caplet that is used by market practitioners. For the SMM we also theoretically construct an arbitrage-free interest rate model that implies a pricing formula for swaptions that has the same structure as the Black-Scholes pricing formula for swaptions. We empirically compare the pricing performance of the LMM against the Black-Scholes for pricing caplets using Monte Carlo methods.

In recent decades, many investors have abandoned hopes of achieving above market returns through active management, and consigned themselves to passive investing in the form of market capitalization based portfolios. Using Swedish stock exchange data from 2002-2016, this thesis investigates if there is a way to harmonize the strengths of active management, yielding potential above market returns, and passive index investing, implying lower fees and transparency. Based on observations from 275 companies, analysed through market model regressions, the results suggest that fundamentally invested value and quality portfolios create an alpha of 1-2 percent quarterly relative the market capitalization benchmark portfolio. Moreover, the results constitute basis for performing real investments, as they take into consideration the transaction costs implied by portfolio turnover. Furthermore, the findings of greater risk-adjusted returns through fundamentally weighted portfolios stand in opposition to the efficient market hypothesis.

There is an ongoing trade war between the two largest economies in the world. Since the trade war is still ongoing, few studies have been done to investigate how it affects the global economy. The purpose of this thesis is to analyze the trade war’s effect on the Swedish stock market between the 2nd of March 2018 when U.S. president Donald Trump first threatened to impose tariffs on Chinese imports to the 15th of January 2020 when the phase one deal was signed. Data is collected from Donald Trump’s official twitter account and by statements from the U.S. and Chinese governments. An event study is then made by using the market model to find abnormal returns for different sectors and stocks on OMXS large cap. The study shows that the sectors react differently to the announcements. Some sectors were not affected at all and others were heavily affected. Telecommunication is a sector that had an average cumulative abnormal return close to zero both when there was positive news and negative news about the trade war. Contrarily, a sector that seems to be highly correlated to the news about the trade war is the Technology sector. Basic Resources is the most affected sector in the study when bad news occurred. From our study, we can conclude that the Swedish stock market is affected by the trade war.

This study aims to examine the impact a released profit warning has on a company’s stock price. The effect will be examined using an event study. In addition, the market model will be applied to calculate the abnormal returns associated with the profit warning. Previous studies within the subject of profit warnings have shown that the abnormal return on the day of a published profit warning amounts to -14,72% (Jackson and Madura, 2003). Furthermore, the effect varies depending on the current state of the economy (Cox, Dayanandan, Donker and Nofsinger, 2017). The purpose of this study is to investigate the market response to a published profit warning on stocks associated with the Swedish all-share index OMXSPI. Calculations show that the abnormal returns on average totaled to about -8,3% during the first day of trading. In the longer perspective, up to 90 days following the profit warning, the study showed that stock prices recover the initial price fall. However, it should be noted that presented results are not statistically significant. / Studien avser att undersöka en negativ vinstvarnings effekt på aktuell aktiekurs. Effekten studeras genom en eventstudie som med hjälp av marknadsmodellen beräknar en akties överavkastning i samband med publicerad vinstvarning. Tidigare forskning inom området menar på att effekten är negativ om -14,72% (Jackson och Madura, 2003) samt att effekten är större ifall ekonomin befinner sig i en period präglad av tillväxt (Cox, Dayanandan, Donker och Nofsinger, 2017). Syftet med denna studie är att studera vinstvarningar hos företag som är en del av Stockholmsbörsens all-share index OMXSPI. Resultatet är att en vinstvarning i genomsnitt orsakade en överavkastning om ungefär -8,3% under den första handelsdagen efter offentliggörandet. På längre sikt, upp emot 90 dagar efter vinstvarning, har studien noterat att aktiekurserna återhämtar det initiala kursraset. Dock är resultaten inte statistiskt signifikanta.