In this thesis, we propose two continuous time stochastic volatility models with long memory that generalize two existing models. More importantly, we provide analytical formulae that allow us to study option prices numerically, rather than by means of simulation. We are not aware about analytical results in continuous time long memory case. In both models, we allow for the non-zero correlation between the stochastic volatility and stock price processes. We numerically study the effects of long memory on the option prices. We show that the fractional integration parameter has the opposite effect to that of volatility of volatility parameter in short memory models. We also find that long memory models have the potential to accommodate the short term options and the decay of volatility skew better than the corresponding short memory stochastic volatility models.

In this masterthesis I have rewied basic approaches to volatility estimating. These approaches are based on classical and Bayesian statistics. I have applied the volatility models for the purpose of volatility forecasting of a different foreign exchange (EURUSD, GBPUSD and CZKEUR) in the different period (from a second period to a day period). I formulate the models EWMA, GARCH, EGARCH, IGARCH, GJRGARCH, jump diffuison with constant volatility and jump diffusion model with stochastic volatility. I also proposed an MCMC algorithm in order to estimate the Bayesian models. All the models we estimated as univariate models. I compared the models according to Mincer Zarnowitz regression. The most successfull model is the jump diffusion model with a stochastic volatility. On the second place they were the GJR- GARCH model and the jump diffusion model with a constant volatility. But the jump diffusion model with a constat volatilit provided much more overvalued results.The rest of the models were even worse. From the rest the IGARCH model is the best but provided undervalued results. All these findings correspond with R squared coefficient.

Cette thèse étudie le marché d'électricité européen. Le premier sujet porte sur les interdépendances des prix de l'électricité dans les principaux marchés européens pour évaluer l'efficacité des réformes introduites par la commission européenne pour créer un marché unique de l'électricité. Nous utilisons des méthodes statistiques multidimensionnelles (modèle vectoriel autorégressif et le test de causalité de Granger). Le second sujet concerne l'impact de l'introduction du marché des dérivés sur la volatilité des prix du marché au comptant à travers une étude économétrique avec le modèle GARCH, l'objectif est d'examiner l'effet stabilisant ou déstabilisant des dérivés sur la volatilité des prix de l'électricité. Enfin, le troisième sujet analyse les effets potentiels de l'évolution des réformes réglementaires sur les prix et la sécurité d'approvisionnement ainsi que les interactions entre les réformes du Royaume-Uni et celles de l'Union Européenne. / This thesis examines the European electricity markets. It first evaluates the effectiveness of European commission reforms to create a single and unified electricity market in Europe against national policies measures, investigating the interdependencies of electricity prices in the main European markets including United Kingdom of Great Britain. To conduct the econometric analysis, Vector Autoregressive model and Granger causality test (Granger 1988) are used. It then studies the impact of the introduction of derivatives trading market on the price volatility of physical market through an econometric study with GARCH model. Finally, it examines the potential effect of changing reforms on electricity prices and security of supply and analyses the interaction between UK reforms and EU regulation.

Práce je zaměřena na modelování časových řad směnných kurzů pomocí modelů podmíněného rozptylu. V první části je vysvětlena teorie lineárních i nelineárních modelů volatility včetně popsání všech konkrétních modelů, které jsou v práci použity. Dále jsou zde podrobně popsány všechny testy, které jsou v práci použity. V druhé části jsou modelovány reálné časové řady směnných kurzů české koruny k amerického dolaru a k euru. Třetí část je zaměřena na ověření úrokové parity mezi Českou republikou a eurozónou. Tento vztah je ověřován pomocí Grangerovy kauzality a kointegrace časových řad.

This diploma thesis deals with the impact of real exchange rate volatility on real export of the Czech Republic. In the first part, theoretical aspects of this relationship are examined, explaining both - positive and negative ? effects on bilateral and aggregate trade flows. Further on, empirical data and econometric tools are employed to capture the relationship between real export and its main determinants for the case of Czech Republic in the past decade. After the brief theoretical introduction to time series econometrics, the particular export demand model is proposed and various cointegration techniques are explained and applied to examine the long-run equilibrium but also short-run dynamics.

The thesis describes and applies two parametric option pricing models which partially ease the well-known discrepancy between real world and Black-Scholes model. Stochastic volatility and jumps encompassed by Heston and SVJ models explain implied volatility smile and its heterogeneous term-structure. Both models are calibrated to market data observed for EURUSD currency options on January 23, 2015. While SVJ model provided a better fit for the market, especially for mid-term expiry smile curvature, its estimated risk-neutral parameters were unrealistic comparing with their counterparts under statistical measure. Estimations suggest zero long term price volatility and 2 jumps during the year with average magnitude of 6 \%. Both models failed to match curvature of short time to expiry smile and provided a good fit of term-structure and long-expiry smile. Analysing delta ratios adjusted for non-constant volatility as a possible alternatives the study considered minimum variance delta estimated with Heston model, delta ratio recommended by Nassim Taleb and two deltas adjusted for local volatility assuming sticky moneyness and sticky tree dynamics of implied volatility. On data set of EURUSD options from 1.1.2014 to 30.5.2015, our research did not find any alternative which would be more reliable than common Black-Scholes delta.

One of the essential features of financial time series data is volatility. It is often the case that, over time, structural changes occur in volatility, and an accurate estimation of the volatility of financial time series requires careful identification of the change-points. A common approach to modeling the volatility of time series data is based on the well-known Generalized Autoregressive Conditional Heteroscedastic (GARCH) model. Although the problem of change-point estimation of volatility dynamics derived from the GARCH model has been considered in the literature, these approaches rely on parametric assumptions of the conditional error distribution, which are frequently violated in financial time series. This misspecification of error distribution may lead to change-point detection inaccuracies, resulting in unreliable GARCH volatility estimates. In this dissertation, we introduce novel change-point detection algorithms based on a semiparametric GARCH model. The proposed semiparametric GARCH model retains the structural advantages of the GARCH process while incorporating the flexibility of nonparametric conditional error distribution. Consequently, the likelihood function and the corresponding volatility estimates obtained via this semiparametric approach are more accurate than the traditional Quasi-Maximum Likelihood Estimation (QMLE) method that relies on an assumed parametric error distribution. The main objective of the change-point estimation problem is to detect the exact number and locations of the change-points. This dissertation proposes an innovative semiparametric GARCH process in developing solutions for change-point estimation problems. Specifically, a penalized likelihood approach based on a semiparametric GARCH model and an efficient binary segmentation algorithm is developed to estimate the change points' locations. The results demonstrate that in terms of change-point identification and estimation accuracy for multiple GARCH process variations, the proposed semiparametric method outperforms the commonly used approaches to change-point analysis in financial data.

Includes bibliographical references / This thesis focuses on the effects and causes of exchange rate volatility in South Africa. These issues are analysed in three stand-alone but related papers. The first paper (Chapter 2) investigates the impact of real exchange rate volatility on employment growth in the manufacturing sector. The study contributes to the literature on the employment effects of exchange rate volatility in emerging markets given limited studies. This is done by using the Autoregressive Distributed Lag (ARDL) counteraction approach which is able to estimate an error correction form of the model for the variables under investigation. This enables one to analyse the relationship between exchange rate volatility and employment growth. The advantage of this approach is that it performs better in small samples and works well even when the underlying variables are integrated of different orders. Employing quarterly time series data for the period 1995 . 2010, the analysis shows that real exchange rate volatility has a significant contractionary effect on manufacturing employment growth. The study also provides evidence that exchange rate level, output, wages and interest rates have significant effects on manufacturing employment growth. The results suggest that the government can reduce the adverse effects of exchange rate volatility on manufacturing by adopting macroeconomic policies that minimise exchange rate volatility and policies that promote employment creation, for instance, less restrictive policies given that the results show that an increase in interest rates leads to a decline in employment. Coming up with macroeconomic policies that minimise exchange rate volatility requires the knowledge of the causes of exchange rate volatility. As a result, the second paper (Chapter 3) investigates the determinants of exchange rate volatility in South Africa. Few studies investigate the determinants of rand volatility (Arezki, Dumitrescu, Freytag & Quintyn 2014, Farrell 2001). This study contributes to the literature by finding the sources of rand volatility using output volatility, money supply volatility, foreign reverses volatility, commodity price volatility, openness and a dummy for capital account liberalisation as explanatory variables. This is done using GARCH models for the period 1986- 2013 employing monthly time series data. The advantage of GARCH models is that they are able to model and forecast time-varying variance given that the exchange rate behaves similarly to other asset prices, for example, stock prices. The study tests the hypothesis that economic openness leads to a reduction in exchange rate volatility following Hau's (2002) modifications of the New Open Macroeconomics model of Obstfeld & Rogoff (1995, 1996). South Africa is a good case study following the liberalisation of the capital account in March 1995. The results show that switching to a coating exchange rate regime has a significant positive effect on exchange rate volatility. That is, it increases exchange rate volatility. The results also show that trade openness reduces exchange rate volatility using the bilateral exchange rate. The results also show that output, commodity prices, money supply and foreign reserves volatilities significantly influences exchange rate volatility. The study also shows that real factors (commodity prices, output and openness) have relatively larger effects on exchange rate volatility compared to monetary factors. The third paper (Chapter 4) analyses the short run behaviour of the South African rand using daily data. The study contributes to the literature on the causes of exchange rate movements in several ways. First, it uses an event studies approach a la Campbell, Lo & MacKinlay (1997) to answer two research questions. First, what is the impact of South Africa's monetary policy announcements on the rand? Second, what is the impact of South African political events on the rand? The advantage of event studies is that they are able to quantify systematically the abnormal or unexpected impact of an economic or political event on asset prices like the exchange rate. Second, the study focuses on an emerging market given that most studies have mainly focused on developed economies. Third, few studies that use event studies in South Africa focus on stock market reaction to announcements. The results find 8 out of 12 significant cumulative abnormal returns for monetary policy announcements. This suggests that the rand is not only influenced by demand and supply flows but also by news. The study also finds significant cumulative abnormal returns for all the three exchange rates following the Marikana massacre on 16 August 2012 and the release of Nelson Mandela banknotes on 6 November 2012. The ANC elective conference only has significant cumulative abnormal returns using the Rand/US dollar in 2007 and 2012.

The study analyzes the effects of three maize market policies on strategic price volatility, oligopoly/oligopsony market power, distribution of market surplus and total welfare. Policies of interest are privatization, the Current Malawi government policy and a proposed policy. The study first develops a workable theory then tests the various government policies in laboratory market experiments. The laboratory results indicate that the proposed policy was the most effective in reducing strategic volatility, but resulted in high output and low input prices. In terms of welfare distribution, privatization had highest consumer surplus followed by the current policy. The same was the case with producers’ surplus. However, traders’ profits were substantially higher in the proposed policy treatment. Total welfare was highest in the proposed policy followed by the current policy. In all, there appears there can be significant policy tradeoffs between market volatility, market power, surplus distribution and total welfare.

Field studies conducted in Missouri and Mississippi, in 2017 and 2018, respectively, indicated no droplet size effect on PRE herbicide efficacy, regardless of the herbicide, weed, soil, crop residue and weather conditions during spraying. Nozzle type enhanced herbicide efficacy for one location and herbicide. The TTI60 dual fan nozzle increased pendimethalin weed control, up to 91%, in a high organic matter (OM) soil with large clods and substantial weed pressure. Pendimethalin efficacy was reduced under high OM soils (> 2%) while metribuzin efficacy was reduced under low OM (< 0.7%), low cation exchange capacity (<13.1%) soils and 12.2 mm of rain three days after application. The greenhouse studies indicated that increasing crop residue levels reduced velvetleaf control by 7%. Simulated rainfall eight days after herbicide application decreased johnsongrass dry weight reductions by 29% in comparison to two day rainfall.