Derivatives have a large and significant role on the financial markets today and the popularity of options has increased. This has also increased the demand of finding a suitable option pricing model, since the ground-breaking model developed by Black & Scholes (1973) have poor pricing performance. Practitioners and academics have over the years developed different models with the assumption of non-constant volatility, without reaching any conclusions regarding which model is more suitable to use. This thesis examines four different models, the first model is the Practitioners Black & Scholes model proposed by Christoffersen & Jacobs (2004b). The second model is the Heston´s (1993) continuous time stochastic volatility model, a modification of the model is also included, which is called the Strike Vector Computation suggested by Kilin (2011). The last model is the Heston & Nandi (2000) Generalized Autoregressive Conditional Heteroscedasticity type discrete model. From a practical point of view the models are evaluated, with the goal of finding the model with the best pricing performance and the most practical usage. The model´s robustness is also tested to see how the models perform in out-of-sample during a high respectively low implied volatility market. All the models are effected in the robustness test, the out-sample ability is negatively affected by a high implied volatility market. The results show that both of the stochastic volatility models have superior performances in the in-sample and out-sample analysis. The Generalized Autoregressive Conditional Heteroscedasticity type discrete model shows surprisingly poor results both in the in-sample and out-sample analysis. The results indicate that option data should be used instead of historical return data to estimate the model’s parameters. This thesis also provides an insight on why overnight-index-swap (OIS) rates should be used instead of LIBOR rates as a proxy for the risk-free rate.

The purpose of this thesis is to investigate day-of-the-week effects for stock index returns. The investigations include analysis of means and variances as well as return-distribution properties such as skewness and tail behavior. Moreover, the existences of conditional day-of-the-week effects, depending on the outcome of returns from the previous week, are analyzed. Particular emphasis is put on determining useful testing procedures for differences in variance in return data from different weekdays. Two time series models, AR and GARCH(1,1), are used to find out if any weekday's mean return is different from other days. The investigations are repeated for two-day re- turns and for returns of diversified portfolios made up of several stock index returns.

The purpose of this paper is to select the best GARCH-type model for modelling the volatility of Bitcoin, Bitcoin Cash, Litecoin, Dogecoin and Ethereum. GARCH (1,1), IGARCH(1,1), EGARCH(1,1), TGARCH(1,1) and CGARCH(1,1) are used on the cryptocurrencies closing day return. We select the model with the highest Maximum Likelihood and run an OLS regression on the conditional volatility to measure the day-of-the-week effect. The findings show that EGARCH(1,1) model best suits Bitcoin, Litecoin, Dogecoin and Ethereum data and that the GARCH(1,1) model suits best Bitcoin data. The results show a significant presence of day-of-the-week effects on the conditional volatility of some days for Bitcoin, Bitcoin Cash and Ethereum. Wednesday has a significant negative effect on Bitcoin conditional volatility. Friday, Saturday and Sunday are found to be significant and positive on Bitcoin Cash conditional volatility. Finally, Saturday is found to be significant and positive on Ethereum conditional volatility.

This thesis includes four essays on risk assessment with financial econometrics models. The first chapter provides Monte Carlo evidence on the efficiency gains obtained in GARCH-base estimations of VaR and ES by incorporating dependence information through copulas and subsequently using full maximum likelihood (FML) estimates. First, individual returns series are considered; in this case, the efficiency gain stems from exploiting the relationship with another returns series using a copula model. Second, portfolio returns series obtained as a linear combination of returns series related with a copula model, are considered; in this case, the efficiency gain stems from using FML estimates instead of two-stage maximum likelihood estimates. Our results show that, in these situations, using copula models and FML leads to a substantial reduction in the mean squared error of the VaR and ES estimates (around 50\% when there is a medium degree of dependence between returns) and a notable improvement in the performance of backtesting procedures. Then, chapter 2 analyzes the impact of the COVID-19 pandemic on the conditional variance of stock returns. In this work, we look at this effect from a global perspective, employing series of major stock market and sector indices. We use the Hansen’s Skewed-t distribution with EGARCH extended to control for sudden changes in volatility. We oversee the COVID-19 effect on the VaR. Our results show that there is a significant sudden shift up in the return distribution variance post the announcement of the pandemic, which must be explained properly to obtain reliable measures for financial risk management. In chapter 3, we assess VaR and ES estimates assuming different models for standardised returns such as Cornish-Fisher and Gram-Charlier polynomial expansions, and well-known parametric densities such as normal, skewed Student-t family of Zhu and Galbraith (2010), and Johnson. This paper aims to check whether models based on polynomial expansions outperform the parametric ones. We carry out the model performance comparison in two stages. First, a backtesting analysis for VaR and ES, and second, using the loss function approach. Our backtesting results in our empirical exercise suggest that all distributions, but the normal, perform quite well in VaR and ES estimations. Regarding the loss function analysis, we conclude that the Cornish-Fisher expansion usually outperforms the others in VaR estimation, but Johnson distribution is the one that provides the best ES estimates in most cases. Although the differences among all distributions (excluding the normal) are not great. Finally, chapter 4 assess whether accounting for asymmetry and tail-dependence in returns distributions may help to identify more profitable investment strategies in asset portfolios. Three copula models are used to parameterize the multivariate distribution of returns: Gaussian, C-Vine and R-Vine copulas. Using data from equities and ETFs from the US market, we find evidence that, for portfolios of 48 constituents or less, the R-Vine copula is able to produce more profitable portfolios with respect to both, the C-Vine and Gaussian copulas. However, for portfolios of 100 assets, performance of R- and C-Vine copulas is quite similar, being both better than the Gaussian copula.

京都大学 / 新制・課程博士 / 博士(経済学) / 甲第24164号 / 経博第658号 / 新制||経||302(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慎一, 准教授 高橋 修平, 准教授 安井 大真 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DFAM

No description available.

Background: With the tremendous growth of the Green Bond (GB) market, understanding the relationship of the GB market with other financial markets gains importance. The Covid19 pandemic causing a recession in most major economies creates an opportunity to see the co-movements of the GB market with other financial markets under a period of economic crisis. Purpose: This study aims to use the economic contraction catalyzed by the 2020’s Covid-19 pandemic as a means to investigate the co-movements between the GB and the treasury bond, corporate bond, stock, and carbon markets during an economic recession. Through this, we intend to find if co-movements of the GB market have changed, and if so, how. Method: As the collected data is time-series data, Augmented Dickey-Fuller and Ljung-Box tests are utilized for preliminary testing. Thereafter, a univariate-GARCH model is used for volatility modeling. Moreover, the DCC-GARCH model has been conducted to determine the co-movements between the markets. Conclusion: The results of the study show that in the case of GB, treasury, and corporate bond markets, no considerable changes were observed in the co-movement among the two different sample periods. Moving to the stock and GB markets, it was found that the co-movement increased at the beginning of the crisis. However, for the whole crisis period, no substantial changes can be seen in comparison to the pre-crisis period. Furthermore, the co-movement between the two markets was found to be weak in general. Moving on to the results obtained for GB and carbon markets, at the start of the crisis, a sharp fall can be observed. When compared to the pre-crisis period, the co-movement showed a slight increase, yet very weak. Furthermore, it was observed that the co-movement between the two markets has been weak during the whole sample period.

The study has examined the performance of six different specifications of Recurrent Neural Networks designed to predict Value at Risk at the one and five percent level. The models have been tested on the OMX30 stock index, the SEK/EUR exchange rate and the Class A Berkshire-Hathaway stock using a GARCH expanding window as baseline model. The proposed Neural Networks show decent predictive performance, serving as an indication of the potential use of Recurrent Neural Networks’ predictive capabilities of VaR. In three cases out of six does a proposed network outperform the baseline GARCH. However, when comparing the proposed models’ performance with the baseline GARCH, it is evident that GARCH on average is more precise and consistent in its predictions. Furthermore, the results show that the Neural Networks’ performance is very sensitive to the hyperparameter tuning, and that finding a model specification that performs well on both in-sample and out-of-sample data is rather difficult, as well as finding a single specification that performs acceptably on several data sets. Given the narrow selection of hyperparameters tuned, the fact that one of the proposed neural network models managed to beat the high performing GARCH in three out of six cases suggests that the subject could benefit from further studies. Future studies are recommended to extend the scope of hyperparameter tuning.

近年來，金融資產報酬波動的推估一直是重要的研究課題。然而，過去的波動不對稱研究均集中在股票市場，探討外匯市場波動不對稱性的實證研究並不多，但若忽略其不對稱效果將影響未來波動預測的正確性。因此，本研究利用近十六年來美元及日圓兌台幣匯率日資料，以傳統的波動不對稱性指數型GARCH模型(EGARCH Model)、門檻型GARCH模型(TGARCH, GJR GARCH Model)，亦延用異質自我相關迴歸模型(HAR-RV Model)及修正型異質自我相關迴歸模型(Modified HAR-RV Model)分別探討美元及日圓兌台幣匯率波動是否存在不對稱現象及其不對稱程度，並加以分析。實證研究中，上述四種模型均顯示美元及日圓兌台幣匯率波動的確具有不對稱效果；美元兌台幣匯率波動，與股票市場一致，報酬率與波動度間呈負向關係，當台幣相對美元升值時，波動度較高；而日圓兌台幣匯率波動，與美元匯率變動方向相反，報酬率與波動度間呈正向關係，當台幣相對日圓貶值時，波動度較高。此外，以異質自我相關迴歸模型實證分析中，日波動落後項的影響力明顯大於週、月、季波動落後項，與Muller, et al. (1997)、Corsi (2004)及Andersen, et al. (2005)實證研究結果類似。

Even though major advances in economic theory and modelling have in some cases furthered our understanding of how the economy works, the system as a whole has become more complex. If policymakers had perfect knowledge about the actual state of the economy, the various transmission mechanisms as well as the true underlying model, monetary intervention would be greatly simplified. In reality, however, the monetary authorities have to contend with considerable uncertainty in relation to the above-mentioned factors. This said, uncertainty has mostly been neglected in both the theoretical and empirical literature focusing on monetary policy analysis. Nonetheless, findings from a review of theoretical literature that does exist on this topic suggest that optimal central banks act more conservatively when faced with uncertainty. Similarly, empirical findings from the literature also favour conservatism. However, there is some evidence to suggest that this is not always the case. These results suggest that central banks do not always act optimally when faced with uncertainty. The limited number of industrial country cases examined prevents any generalised view from emerging. If anything, the literature findings suggest that central bank behaviour differs across countries. This thesis aims to contribute to the empirical literature by studying the effects of uncertainty on monetary policy in the developing country case of South Africa. In simplest terms, the thesis seeks to establish whether or not the South African Reserve Bank (SARB) responded optimally to uncertainty as suggested by theoretical models thereof. To this end, the thesis employs a theoretical model which resembles a structural rule-based approach. The optimal interest rate rule was derived given a set of structural equations relating to demand, the Phillips curve and the real exchange rate. To incorporate uncertainty, it is assumed that the coefficients are dependent on the variances of the exogenous variables, namely inflation, the output gap and the exchange rate. The uncertainty adjusted model allows us to investigate whether monetary policy is more aggressive or passive when uncertainty about the relevant exogenous variable increases. Inflation, output gap and exchange rate uncertainty estimates were derived through GARCH-model specifications related to the structural equations as defined in the theoretical model. The investigation considered both indirect and direct uncertainty effects with a sample period stretching from 1990 to 2011. The findings reported in this thesis provide strong evidence in support of the notion that uncertainty plays a significant role within the South African monetary policy landscape and contributes towards explaining the SARB’s actions. Furthermore, the results suggest that the SARB did in fact act optimally in responding more conservatively to target variable fluctuations on average. Also, the findings could potentially strengthen the case for inflation targeting as a monetary policy regime, as the results indicate a marked decline in the effects of uncertainty under inflation targeting than before. / Economics / D. Com. (Economics)