Risk Management of Daily Tourist Tax Revenues for the Maldives

International tourism is the principal economic activity for Small Island Tourism Economies (SITEs). There is a strongly predictable component of international tourism, specifically the government revenue received from taxes on international tourists, but it is difficult to predict the number of international tourist arrivals which, in turn, determines the magnitude of tax revenue receipts. A framework is presented for risk management of daily tourist tax revenues for the Maldives, which is a unique SITE because it relies entirely on tourism for its economic and social development. As these receipts from international tourism are significant financial assets to the economies of SITEs, the time-varying volatility of international tourist arrivals and their growth rate is analogous to the volatility (or dynamic risk) in financial returns. In this paper, the volatility in the levels and growth rates of daily international tourist arrivals is investigated.Small Island Tourism Economies (SITEs), International tourist arrivals, Tourism tax, Volatility, Risk, Value-at-Risk (VaR), Sustainable Tourism-@-Risk (ST@R)

IMPACT OF ENVIRONMENTAL ANNOUNCEMENTS ON THE FINANCIAL MARKETS OF EUROPEAN CRUDE OIL EXPORTERS AND IMPORTERS

Climate change poses a severe threat to the world as we know it. Over the last decades, governments and intergovernmental organisations have introduced regulations and policies to limit greenhouse gas emissions. Several studies have found that announcements of environmental regulations and policies affect financial markets worldwide. Our thesis examines whether the main stock market indices of exporters of crude oil are affected differently than importers by environmental announcements. We consider the Russian MOEX, the Norwegian OSEBX and the British FTSE 100 as proxies for exporters, while the German DAX, Spanish IBEX 35 and Italian FTSE MIB represent importers. We employ the event study methodology to investigate whether indices display significant cumulative abnormal returns around environmental announcements. In addition, we estimate the same period volatility using a GJR-GARCH model, where we introduce dummy variables for the event period. We find significant negative cumulative abnormal returns for the Russian MOEX around the announcement of the Glasgow climate pact. We also find significant increases in volatility for both importers and exporters around two events. Overall, our findings do not indicate that the financial markets of exporters of crude oil behave differently from importers around environmental announcements

Climate risk and green investments : New evidence

The academic literature on green energy equity markets has increased extensively over the last decade due to growing concerns about climate change and the substantial flow of investments into alternative energy markets. This study contributes by investigating the effect of climate risk on the return and volatility of green energy assets. This is one of the first papers to assess such effects using the recently developed climate policy uncertainty index as an indicator of climate risk. In particular, we seek to answer the following research questions. Firstly, does rising climate risk lead to a significant increase in green energy asset returns? Secondly, does climate risk affect the volatility of green energy assets negatively? Employing various models, we provide statistical evidence in favour of our hypotheses. Rising climate risk seems to encourage investment in alternative energy, which leads to an upward demand for green energy, which in turn increases the prices of green energy investments and decreases their volatility levels. Our analysis further shows that when climate risk increases, the correlation between crude oil and green energy returns decreases. Furthermore, green energy assets are more effective than gold for hedging oil market risk, without ignoring the hedging ability of technology stock investment.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

IMPACT OF ENVIRONMENTAL ANNOUNCEMENTS ON THE FINANCIAL MARKETS OF EUROPEAN CRUDE OIL EXPORTERS AND IMPORTERS

Climate change poses a severe threat to the world as we know it. Over the last decades, governments and intergovernmental organisations have introduced regulations and policies to limit greenhouse gas emissions. Several studies have found that announcements of environmental regulations and policies affect financial markets worldwide. Our thesis examines whether the main stock market indices of exporters of crude oil are affected differently than importers by environmental announcements. We consider the Russian MOEX, the Norwegian OSEBX and the British FTSE 100 as proxies for exporters, while the German DAX, Spanish IBEX 35 and Italian FTSE MIB represent importers. We employ the event study methodology to investigate whether indices display significant cumulative abnormal returns around environmental announcements. In addition, we estimate the same period volatility using a GJR-GARCH model, where we introduce dummy variables for the event period. We find significant negative cumulative abnormal returns for the Russian MOEX around the announcement of the Glasgow climate pact. We also find significant increases in volatility for both importers and exporters around two events. Overall, our findings do not indicate that the financial markets of exporters of crude oil behave differently from importers around environmental announcements

Essays in financial econometrics

This dissertation consists of three chapters discussing issues in the field of financial econometrics. All three chapters are largely empirical, with some theoretical developments in second moments modelling in the second chapter. The first chapter of this thesis analyses the market neutrality of Pairs Trading, a statistical arbitrage trading technique, from a second moments perspective. In this study, I analyse how market and idiosyncratic news affect the profitability of this trading strategy. I propose a conditional covariance framework based on Kroner and Ng (1998) extension of the BEKK Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to analyse the dependence of second moments between different portfolios of pairs and the returns of the market. In contradiction to what is generally assumed about the market neutrality of this strategy, the results indicate the existence of significant spillovers from market news to different portfolios of pairs. In a second step of the study, I analyse the contribution of both idiosyncratic and market components to pairs volatility over time in an asynchronous panel of pairs. This analysis shows that the volatility of the pairs strategy has become more dependent on idiosyncratic rather than market shocks. In this sense, although Pairs Trading cannot be said to be market neutral from a second moments perspective if we look at the full sample from 1962 to 2018, the strategy has certainly become more market neutral as markets have evolved over the last two decades. In the second chapter of this thesis, Susana Campos-Martins (Oxford) and I propose an econometric framework that explains the Purchasing Power Parity (PPP) Puzzle as common volatility shocks. Most of the discussion about the PPP Puzzle of Rogoff (1996) has pertained to the reversion speed of deviations from PPP. Much less attention, however, has been given to the other component of the puzzle: the high volatilities of real exchange rates. In this paper, we provide a framework that is capable of explaining the econometric sources of these volatilities. First, we study the drivers of real exchange rate volatilities using a Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) panel framework and the conditional covariance matrices of the system with nominal exchange rates and price differentials. This analysis indicates that, for both emerging and developed markets, common factors are the main drivers of volatility. With this result in hand, we propose a novel econometric framework that explains the sources of these volatilities as common second moment shocks. This model allows us to gives structure to the origins of these high volatilities and propose an extension to study their macrofinancial drivers. The third and final chapter of this thesis is an adapted version of a current IMF working paper which introduces the IMF Soft Power Index. In this chapter, Serhan Cevik (IMF) and I introduce a new composite Global Soft Power Index (GSPI) composed of six dimensions for a broad sample of 72 countries across the world over the period 2007-2019. The proposed framework allows for comparisons not only at the “headline” level of the GSPI, but also at the level of the sub-indices, which allows us to identify and study how countries differ at a granular level of soft power. In a final step of the analysis, we present a possible macro-financial application to analyse the relationship between soft power and the volatility of Real Effective Exchange Rates (REER) across countries and over time. The results indicate that some dimensions of the GSPI play an important role in explaining real exchange rate volatility at almost all significance levels. Overall, our framework presents a systematic approach to measure soft power and its dimensions. By capturing the matrix of soft power characteristics, the GSPI offers significant advantages in comparative analysis of soft power across countries and over time

Forecasting volatility: Evidence from the Macedonian stock exchange

This paper investigates the behavior of stock returns in an emerging stock market namely, the Macedonian Stock Exchange, focusing on the relationship between returns and conditional volatility. The conditional mean follows a GARCH-M model, while for the conditional variance one symmetric (GARCH) and four asymmetric GARCH types of models (EGARCH, GJR, TARCH and PGARCH) were tested. We examine how accurately these GARCH models forecast volatility under various error distributions. Three distributions were assumed, i.e. Gaussian, Student-t and Generalized Error Distribution. The empirical results show the following: (i) the Macedonian stock returns time series display stylized facts such as volatility clustering, high kurtosis, and low starting and slow-decaying autocorrelation function of squared returns; (ii) the asymmetric models show a little evidence on the existence of leverage effect; (iii) the estimated mean equation provide only a weak evidence on the existence of risk premium; (iv) the results are quite robust across different error distributions; and (v) GARCH models with non-Gaussian error distributions are superior to their counterparts estimated under normality in terms of their in-sample and out-of-sample forecasting accuracy.Stock market; forecasting volatility; South-Eastern Europe; GARCH models; non-Gaussian error distribution; Macedonia

Estimating Dependences and Risk between Gold Prices and S&P500: New Evidences from ARCH,GARCH, Copula and ES-VaR models

This thesis examines the correlations and linkages between the stock and commodity in order to quantify the risk present for investors in financial market (stock and commodity) using the Value at Risk measure. The risk assessed in this thesis is losses on investments in stock (S&P500) and commodity (gold prices). The structure of this thesis is based on three empirical chapters. We emphasise the focus by acknowledging the risk factor which is the non-stop fluctuation in the prices of commodity and stock prices. The thesis starts by measuring volatility, then dependence which is the correlation and lastly measure the expected shortfalls and Value at risk (VaR). The research focuses on mitigating the risk using VaR measures and assessing the use of the volatility measures such as ARCH and GARCH and basic VaR calculations, we also measured the correlation using the Copula method. Since, the measures of volatility methods have limitations that they can measure single security at a time, the second empirical chapter measures the interdependence of stock and commodity (S&P500 and Gold Price Index) by investigating the risk transmission involved in investing in any of them and whether the ups and downs in the prices of one effect the prices of the other using the Time Varying copula method. Lastly, the third empirical chapter which is the last chapter, investigates the expected shortfalls and Value at Risk (VaR) between the S&P500 and Gold prices Index using the ES-VaR method proposed by Patton, Ziegel and Chen (2018). Volatility is considered to be the most popular and traditional measure of risk. For which we have used ARCH and GARCH model in our first empirical chapter. However, the problem with volatility is that it does not take into account the direction of an investments’ movement: volatility of stocks is that they suddenly jump higher and investors are not distressed with gains. When we talk about investors for them the risk is about the odds of losing money, after my research and findings VaR is based on the common-sense fact. Hence, investors care about the odds of big losses, VaR answers the question, what is my worst-case scenario? Or simply how much I could lose in a really bad month? The results of the thesis demonstrated that measuring volatility (ARCH GARCH) alone was not sufficient in measuring the risk involved in an investment therefore methodologies such as correlation and VAR demonstrates better results. In terms of measuring the interdependence, the Time Varying Copula is used since the dynamic structure of the de- pendence between the data can be modelled by allowing either the copula function or the dependence parameter to be time varying. Lastly, hybrid model further demonstrates the average return on a risky asset for which Expected Shortfall (ES) along with some quantile dependence and VaR (Value at risk) is utilised. Basel III Accord which is applied in coming years till 2019 focuses more on ES unlike VaR, hence there is little existing work on modelling ES. The thesis focused on the results from the model of Patton, Ziegel and Chen (2018) which is based on the statistical decision theory. Patton, Ziegel and Chen (2018), overcame the problem of elicitability for ES by using ES and VaR jointly and propose the new dynamic model of risk measure. This research adds to the contribution of knowledge that measuring risk by using volatility is not enough for measuring risk, interdependence helps in measuring the dependency of one variable over the other and estimations and inference methods proposed by Patton, Ziegel and Chen (2018) using simulations proposed in ES-VaR model further concludes that ARCH and GARCH or other rolling window models are not enough for determining the risk forecasts. The results suggest, in first empirical chapter we see volatility between Gold prices and S&P500. The second empirical chapter results suggest conditional dependence of the two indexes is strongly time varying. The correlation between the stock is high before 2008. The results further displayed slight stronger bivariate upper tail, which signifies that the conditional dependence of the indexes is influence by positive shocks. The last empirical chapter findings proposed that measuring forecasts using ES-Var model proposed by Patton, Ziegel and Chen (2018) does outer perform forecasts based on univariate GARCH model. Investors want to 10 protect themselves from high losses and ES-VaR model discussed in last chapter would certainly help them to manage their funds properly

Three essays on modeling energy prices with time-varying volatility and jumps

This thesis addresses the modeling of energy prices with time-varying volatility and jumps in three separate and self-contained papers: A. Modeling energy futures volatility through stochastic volatility processes with Markov chain Monte Carlo This paper studies the volatility dynamics of futures contracts on crude oil, natural gas and electricity. To accomplish this purpose, an appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models and their counterpart GARCH models is performed, with both classes of time-varying volatility processes being estimated through a Markov chain Monte Carlo technique. A comparison exercise for hedging purposes is also considered by computing the extreme risk measures (using the Conditional Value-at-Risk) of simulated returns from the SV model with the best performance - i.e., the SV model with a t-distribution - and the standard GARCH(1,1) model for the hedging of crude oil, natural gas and electricity positions. Overall, we find that: (i) volatility plays an important role in energy futures markets; (ii) SV models generally outperform their GARCH-family counterparts; (iii) a model with t-distributed innovations generally improves the fitting performance of both classes of time-varying volatility models; (iv) the maturity of futures contracts matters; and (v) the correct specification for the stochastic behavior of futures prices impacts the extreme market risk measures of hedged and unhedged positions. B. How does electrification under energy transition impact the portfolio management of energy firms? This paper presents a novel approach for structuring dependence between electricity and natural gas prices in the context of energy transition: a copula of meanreverting and jump-diffusion processes. Based on historical day-ahead prices of the Nord Pool electricity market and the Henry Hub natural gas market, a stochastic model is estimated via the maximum likelihood approach and considering the dependency structure between the innovations of these two-dimensional returns. Given the role of natural gas in the global policy for energy transition, different copula functions are fit to electricity and natural gas returns. Overall, we find that: (i) using an out-of-sample forecasting exercise, we show that it is important to consider both mean-reversion and jumps; (ii) modeling correlation between the returns of electricity and natural gas prices, assuring nonlinear dependencies are satisfied, leads us to the adoption of Gumbel and Student-t copulas; and (iii) without government incentive schemes in renewable electricity projects, the usual maximization of the risk-return trade-off tends to avoid a high exposure to electricity assets. C. Modeling commodity prices under alternative jump processes and fat tails dynamics The recent fluctuations in commodity prices affected significantly Oil Gas (O&G) companies’ returns. However, integrated O&G companies are not only exposed to the downturn of oil prices since a high level of integration allows these firms to obtain non-perfectly positive correlated portfolio. This paper aims to test several different stochastic processes to model the main strategic commodities in integrated O&G companies: brent, natural gas, jet fuel and diesel. The competing univariate models include the log-normal and double exponential jump-diffusion model, the Variance-Gamma process and the geometric Brownian motion with nonlinear GARCH volatility. Given the effect of correlation between these assets, we also estimate multivariate models, such as the Dynamic Conditional Correlation (DCC) GARCH, DCC-GJR-GARCH and the DCC-EGARCH models. Overall, we find that: (i) the asymmetric conditional heteroskedasticity model substantially improves the performance of the univariate jump-diffusion models; and (ii) the multivariate approaches are the best models for our strategic energy commodities, in particular the DCC-GJR-GARCH model