Political uncertainty and FX volatility during US presidential elections: evidence from prediction market

This paper studies the effects of political uncertainty on the conditional volatility of the return on the Trade Weighted US Dollar Index during the homestretches of the last seven US presidential elections. Using daily probability data drawn from the Iowa Electronic Markets, I document that, first of all, higher uncertainty about the election outcome is attributed to a higher volatility of the US Dollar. Secondly, my empirical findings suggest that higher probabilities for a Republican candidate might decrease the volatility. Thirdly, I conclude that the volatility of the US Dollar is higher during the elections which lead to a change in political control between the parties, and in which there is no incumbent candidate running for the presidency. Overall, the findings of my thesis shed light on the connection between market anxiety and the uncertainty that surrounds political elections, and imply that the foreign exchange rates react to the changing betting odds of the US presidential elections

Financial Uncertainty from a Dual Shock at Global Level–Insights from Kuwait

Global stock markets experienced a dual shock in 2020 due to the impact of the global health crisis, parallel to a simultaneous shock derived from the Saudi Arabia and Russia oil price war. The dual shock fueled oil market volatility with lasting effects as the global economy is immersed in an energy crisis combined with high inflationary pressures exacerbated by heightened energy costs. This research paper implemented GARCH and FIGARCH models on daily returns from 31 December 2015, to 9 December 2021, to examine volatility persistence and long memory processes. The world’s most prominent economies are represented by the G7, E7 and the GCC stock markets. Particular attention was devoted to the case of Kuwait as an example of a small oil-dependent economy. The research findings suggest evidence of volatility persistence across the markets, as reported by the GARCH (1,1) model. The FIGARCH (1,1) did not offer significant evidence of long memory processes except for the cases of FTSE 100, BIST 100, IDEX, BSE 100 and Bahrain

Crude oil price forecasting based on the reconstruction of imfs of decomposition ensemble model with arima and ffnn models

The development of economic and industry depend upon how well the accuracy of crude oil price forecasting is managed. The study aims to reduce computation complexity and enhance forecasting accuracy of decomposition ensemble model. The propose model comprises four steps which are (i) decomposing the complex data into several IMFs using ensemble empirical mode decomposition (EEMD) method, (ii) reconstructing the decomposed IMFs through autocorrelation into stochastic and deterministic components, (iii) forecasting every reconstructed component, and (iv) ensemble all forecasted components for the final output. IMFs in the stochastic component are analysed separately. The findings confirm that the stochastic component contributed more variation as compared to deterministic component. For verification and illustration, Brent, West Texas Intermediate (WTI) daily, weekly, monthly and yearly, and Pakistan monthly spot crude oil prices were used as sample study. The empirical results indicated that the proposed model statistically outperformed all the considered benchmark models including the most popular auto-regressive integrated moving average (ARIMA) model, feed forward neural network (FFNN) model, decomposition ensemble model (EEMD-ARIMA and EEMD-FFNN), reconstruction decomposition ensemble model with stochastic and deterministic components (EEMD-(S+D)-ARIMA and EEMD- (S+D)-FFNN) and Rios and De Mello (RD) reconstruction decomposition ensemble model with stochastic and deterministic components (EEMD-RD-ARIMA and EEMD-RD-FFNN). To determine the performance, two descriptive statistical measures were applied, including the root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE of the proposed EEMD-individual stochastic and deterministic (ISD)-FFNN model for daily and weekly data of Brent and WTI are <1%, however, for monthly Brent, WTI and Pakistan data are <5% shows a good fit produce by EEMD-ISD-FFNN. The MAPE of the model EEMDISD- FFNN for yearly Brent data is <30% indicate a reasonable fit and for WTI <20% implies a good fit. Whereas the MAPE of the EEMD-(S+D)-FFNN model for Brent yearly data <20% display a good fit and for WTI data <10% indicate excellent fit. In nutshell, the recommended model for yearly data is EEMD-(S+D)-FFNN. In conclusion, the proposed method of reconstruction of IMFs based on autocorrelation enhanced the forecasting accuracy of the EEMD model

Constructing a global fear index for the COVID-19 pandemic

This paper offers two main innovations. First, we construct a global fear index (GFI) for the COVID-19 pandemic to support economic, financial, and policy analyses in this area. Second, we demonstrate the application of the index to stock return predictability using OECD data. The panel data predictability results reveal the significance of the index as a good predictor of stock returns during the pandemic. Also, we find that accounting for “asymmetry” effect and macro (common) factors improves the forecast performance of the GFI-based predictive model for stock returns. With regular updates and improvements of the index, several empirical analyses can be extended to other macroeconomic fundamentals in future research

Modelling and forecasting volatility in the fishing industry: a case study of Western Cape Fisheries

Dissertation submitted in partial fulfillment of the requirements for the degree of Masters of Management in Finance and Investments (MMFI) in the Graduate School of Business Administration University of the Witwatersrand 2017.The Western Cape Fishing industry has been a subject of discussion in numerous papers, in which the thrust has been to seek ways of sustaining the significantly fluctuating business. Common risk factors have been identified and strategies for managing the fishing business in turbulent periods have been proposed over the years. A closer examination of previous literature as well as empirical evidence indicate that the business has less to do to control or minimize the impact of most of its external factors, which include the Government imposed Total Allowable Catch (TAC) limit, the variability in natural marine populations, environmental factors and fuel price oscillations. In the interest of curbing the variability component which is borne by the internal factors, this study brings on board a quantitative dimension to the evaluation of the four commonly cited internal factors, namely; Earnings Per Share (EPS), Margin of Safety (MOS), Free Cash-Flow (FCF) and the Net-Worth (NW) on volatility of the fishing business. The performance of five large JSE-listed fishing firms: Brimstone, Oceana, Premier Fishing, Sea Harvest and Irvin & Johnson, is investigated with the view of modelling and forecasting their volatilities. Initially, the comparison of volatility forecasts from symmetric and asymmetric GARCH-family models is employed. The results of competing models are tested using cross-validation of mean error measures and the Superior Predictive Ability (SPA) and Model Confidence Set (MCS) tests. Later, a Vector Autoregressive (VAR) model is applied to assess the impact of the four commonly cited internal factors on volatility. The research analysis results reveal a generally high volatility of the Western Cape fishing sector stocks. When univariate GARCH models are applied, the asymmetric GARCH-family models (EGARCH and GJR), with fat tails, appear dominant in the sets of competing models for all stocks, which highlights evidence of the leverage effect in the sector. However, GARCH (1,1), outperformed its counterparts in modelling and forecasting Irvin & Johnson (AVI) and Oceana (OCE) stocks. In the VAR modelling process, the Granger-causality tests indicate limited causal-relationship between EPS, MOS, FCF and the company Net-worth with the companies’ volatility measures. The variance decomposition of the 10-year ahead forecast of volatility indicates that volatility lag, free cash flow and networth have the largest contribution on volatility in the long-run, followed by margin of safety. In view of the above observations, the research discusses recommendations to the Western Cape fishing business to improve business returns and sustainability.MT201

Essays in High Frequency Trading and Market Structure

High Frequency Trading (HFT) is the use of algorithmic trading technology to gain a speed advantage when operating in financial markets. The increasing gap between the fastest and the slowest players in financial markets raises questions around the efficiency of markets, the strategies players must use to trade effectively and the overall fairness of markets which regulators must maintain. This research explores markets affected by HFT activity from three perspectives. Firstly an updated microstructure model is proposed to allow for empirical exploration of current levels of noise in financial markets, this illustrates current noise levels are not disruptive to dominant trading strategies. Second, a ARCH type model is used to de-compose market data into a series of traders working price levels to demonstrate that in cases of suspected market abuse, regulators can assess the impact individual traders make on price even in fast markets. Finally, a review of various HFT control measures are examined in terms of effectiveness and in light of an ordoliberal benchmark of fairness. The work illustrates the extents to which HFT activity is not yet disruptive, but also shows where HFT can be a conduit for market abuse and provides a series of recommendations around use of circuit breakers, algorithmic governance standards and additional considerations where assets are dual listed in different countries

Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique

Previous studies revealed that Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) outperformed Vector Autoregression (VAR) when data exhibit heteroscedasticity. However, EGARCH estimation is not efficient when the data have leverage effect. Therefore, in this study the weaknesses of VAR and EGARCH were modelled using Combine White Noise (CWN). The CWN model was developed by integrating the white noise of VAR with EGARCH using Bayesian Model Averaging (BMA) for the improvement of VAR estimation. First, the standardized residuals of EGARCH errors (heteroscedastic variance) were decomposed into equal variances and defined as white noise series. Next, this series was transformed into CWN model through BMA. The CWN was validated using comparison study based on simulation and four countries real data sets of Gross Domestic Product (GDP). The data were simulated by incorporating three sample sizes with low, moderate and high values of leverages and skewness. The CWN model was compared with three existing models (VAR, EGARCH and Moving Average (MA)). Standard error, log-likelihood, information criteria and forecast error measures were used to evaluate the performance of the models. The simulation findings showed that CWN outperformed the three models when using sample size of 200 with high leverage and moderate skewness. Similar results were obtained for the real data sets where CWN outperformed the three models with high leverage and moderate skewness using France GDP. The CWN also outperformed the three models when using the other three countries GDP data sets. The CWN was the most accurate model of about 70 percent as compared with VAR, EGARCH and MA models. These simulated and real data findings indicate that CWN are more accurate and provide better alternative to model heteroscedastic data with leverage effect

Short Selling and Margin Trading in the Chinese Stock Market

Just as market regulators around the world adopt a more rigorous attitude towards short selling and margin trading, Chinese authorities at its first time approve trades on margin in the domestic stock market. With this introduction event, we conduct three empirical studies regarding short selling and margin trading in the A-share market. The first study examines the impact of the dual introduction on feedback trading behaviour and stock volatility dynamics of the underlying stocks. With a combination of the heterogeneous trader model and GARCH-type models, we highlight the conditional nature of return persistence stemming from feedback trading behaviour. Our findings indicate that the introduction of short selling and margin trading contribute to a moderated level of unconditional positive autocorrelation and conditional positive feedback trading. Besides, no evidence shows that the two mechanisms destabilise the stock market by increasing the volatility persistence in stock returns. Rather, the two mechanisms support the informational efficiency and contribute to the stabilisation of the stock market. With more precise data of each mechanism’s trading activity, the second study investigates the different impacts of short selling and margin trading on the degree of feedback trading and returns volatility at three levels, the individual stock level, the portfolio level, and the market level. Also, we study the impact differences between the trading activity of retail margin investors and that conducted by institutional margin investors. Our results indicate that neither short-selling activity nor margin-trading activity increases positive feedback trading among studied stocks. However, an increasing impact of short selling on negative feedback trading is observed. The strategy of negative feedback trading adopted by short sellers is not conducive to market stability since it does not involve evaluation of a security’s intrinsic value. We also find that margin-trading activity has a significant increasing impact on the level of volatility, while short-selling activity has a slightly decreasing impact. Besides, it reveals that retail investors who have a lower level of financial literacy are more prone to feedback trading strategies. During the stable and booming periods, trades on margin conducted by institutional investors are positively related to a lower level of returns volatility. During the bearish and crash periods, the participation of retail margin investors leads to a higher level of negative feedback trading. Our third study estimates the determinants of short selling and margin trading respectively with panel regressions of a hierarchical approach. We argue that short-selling (or margin-trading) activity is a function of various factors at both the firm and market level. Taking together with control variables, the firm-level factors considered include past short-selling/margin-trading activity, past stock returns, stock returns volatility, financial ratios, ex-dividend date event, industry classification, insider trading event, stock analyst recommendations, block trading event, whereas the market-level factors include past market performance and investor sentiment. We find that short-selling activity is significantly related to past short-selling activity (+), past stock returns (+), historical volatility (-), EPS (-), financial industry stocks (+), insider sale (+), analyst upgrade (+), block plus-tick order (+), past market performance (+) and CCI (-). While margin-trading activity is decided by past margin-trading activity (+), past stock returns (+), historical volatility (-), EPS (+), ex-dividend date event (-), financial stocks (+), insider purchase (+), analyst upgrade (-), block plus-tick order (+), past market performance (+) and market turnover (+). These results provide crucial insights into the nature of information advantages that lead to abnormal returns earned by short sellers and margin traders

Financial Risk Analysis in Polish Stock Market Using GARCH Models

Celem niniejszego artykułu jest odpowiedź na pytanie, czy możliwe jest skuteczne prognozowanie wartości ryzyka rynkowego w warunkach polskiego rynku kapitałowego. Do analizy tego zagadnienia wykorzystano szeregi dziennych stóp zwrotu spółek notowanych na Giełdzie Papierów Wartościowych w Warszawie w latach 2000-2015. W części badawczej pracy przyjęto założenie, iż analizowane szeregi czasowe są realizacją procesu GARCH, co pozwoliło na modelowanie charakterystycznych właściwości spotykanych w empirycznych szeregach czasowych stóp zwrotu akcji giełdowych. Pomiaru ryzyka dokonano posługując się popularnymi miarami zagrożenia. Została również podjęta próba wyboru optymalnej spośród najpopularniejszych metod estymacji ryzyka.The aim of this paper is to investigate whether it is possible to successfully forecast market risk in the Polish capital market. To answer this question, daily time series of the stock prices listed on the Warsaw Stock Exchange between 2000-2015 are analysed. In the research part of the paper, it is assumed that the analysed time series are the realisation of the GARCH process, which allows the author to model the characteristic properties among the empirical data. The risk is assessed with the use of popular quantile risk measures. Additionally, an attempt is made to establish the optimal method of risk [email protected]ł Informatyki i Komunikacji, Uniwersytet Ekonomiczny w KatowicachAcerbi C., 2002, Risk Aversion and Coherent Risk Measures: A Spectral Representation Theorem, “Journal of Banking and Finance”, no. 7.Andersen T. G., Davis R. A., Kreiß J.-P., Mikosch T., 2009, Handbook of Financial Time Series, Springer, Berlin.Ardia D., 2008, Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications, Springer, Berlin.Ardia D., Hoogerheide L. F., 2014, GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts, “Economic Letters”, vol. 123, iss. 2.Asteriou D., Hall S., G., 2007, Applied Econometrics: A modern Approach using EViews and Microfit Revised Edition, MacMillan, Palgrave.Basel Committee on Banking Supervision, 1995, An international model-based approach to market risk capital requirements, Bank for International Settlements, http://www.bis.org/publ/bcbs17.pdf (data wejścia: 04.07.2017).Basel Committee on Banking Supervision, 2013, Consultative Document: Fundamental review of the trading book: A revised market risk framework, Bank for International Settlements, http://www.bis.org/publ/bcbs265.pdf (data wejścia: 04.07.2017).Best P., 2000, Wartość narażona na ryzyko: obliczanie i wdrażanie modelu VaR, Oficyna Ekonomiczna, Kraków.Bollerslev T., 1986, Generalized Autoregressive Conditional Heteroscedasticity, “Journal of Econometrics”, no. 31.Bonferroni C.E., 1936, Teoria statistica delle classi a calcolo delle probabilita, “Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze”.Deacle S., Elyasiani E., 2014, Real estate investment by Bank Holding Companies and their risk and return: nonparametric and GARCH procedures, “Applied Financial Economics”, vol. 24, no. 13.Doman M., Doman R., 2004, Ekonometryczne modelowanie dynamiki polskiego rynku finansowego, Wydawnictwo Akademii Ekonomicznej w Poznaniu, Poznań.Doman M., Doman R., 2009, Modelowanie zmienności i ryzyka: metody ekonometrii finansowej, Wolters, Kraków.Engle R. F., 1982, Autoregressive Conditional Heteroscedasticity with Estimates of Variance of the United Kingdom Inflation, “Econometrica”, no. 50.Föllmer H., Shied A., 2002, Convex Measures of Risk and Trading Constraints, “Finance Stoch”, no. 6(4).Furió D., Climent F. J., 2013, Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation, “Quantitive Finance”, vol. 13, no. 1.Fritelli M., Rosazza G.E., 2002, Putting Order in Risk Measures, “Journal of Banking and Finance”, no. 26(7).Gouriéroux C., 1997, ARCH Models and Financial Applications, Springer, New York.J.P Morgan/Reuters, 1996, RiskMetrics – Technical Document, https://www.msci.com/documents/10199/5915b101-4206-4ba0-aee2-3449d5c7e95a (data wejścia: 04.07.2017).Melle A., Fornari F., 2000, Stochastic Volatility in Financial Markets: Crossing the bridge to continuous time, Springer Science + Business Media, miejsce?.Mayers R., A., 2009, Complex Systems in Finance and Econometrics, Springer, New York.Ng H., Lam K., 2006, How does Sample Size Affect GARCH Models?, “Information Sciences 2006”, Kaohsiung.Fiszeder P., 2009, Modele klasy GARCH w empirycznych badaniach finansowych, Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, Toruń.Francq C., Zakoïan J., 2010, GARCH Models: Structure, Statistical Inference and Financial Applications, WILEY, Hoboken.Hossain A., Nasser M., 2011, Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns, “Journal of Applied Statistics”, vol. 38, no. 3.Jajuga K., Jajuga T., 2004, Inwestycje: instrumenty finansowe, ryzyko finansowe, inżynieria finansowa, Wydawnictwo Naukowe PWN, Warszawa.Kou S., Peng X., Heyde C., 2013, External Risk Measures and Basel Accords, “Mathematics of Operations Research”, vol. 38, no. 3.Kupiec P., 1995, Techniques for Verifying the Accuracy of Risk Management Models, “Journal of Derivatives”, no. 2.Lama A., Jha G. K., Paul R. K., Gurung B., 2015, Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models, “Agricultural Economics Research Review”, vol. 28 (no. 1).McLeod A.I., Li W.K., 1983, Diagnostic Checking ARMA Time Series Model Using Squared Residual Autocorrelations, “Journal of Time Series Analysis, no. 4.Markowitz H., 1952, Portfolio Selection, ”The Journal of Finance, vol. 7, no. 1.Nadarajah, S., Zhang, B., & Chan, S., 2014, Estimation methods for expected shortfall, “Quantitative Finance, no. 14(2).Nyblom J., 1989, Testing for the Constancy of Parameters over Time, “Journal of the Statistical Society B 54”.Osińska M., 2006, Ekonometria finansowa, Polskie Wydawnictwo Ekonomiczne, Warszawa.Pajor A., 2003, Procesy zmienności stochastycznej SV w bayesowskiej analizie finansowych szeregów czasowych, Wydawnictwo Akademii Ekonomicznej w Krakowie, Kraków.Pajor A., 2010, Wielowymiarowe procesy wariancji stochastycznej w ekonometrii finansowej, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków.Pascual L., Romo J., Ruiz E., 2006, Bootstrap prediction for returns and volatilities in GARCH models, “Computational Statistics and Data Analysis”, vol. 50, no. 9.Piontek K., 2001, Heteroskedastyczność rozkładu stop zwrotu a koncepcja pomiaru ryzyka metodą VaR, „Modelowanie preferencji a ryzyko”, Ustroń.Piontek K., 2002, Pomiar ryzyka metodą VaR a modele AR-GARCH ze składnikiem losowym o warunkowym rozkładzie z „grubymi ogonami, „Materiały Konferencyjne Uniwersytetu Szczecińskiego część II”, Międzyzdroje.Piontek K., 2004, Zastosowanie modeli klasy ARCH do opisu własności szeregu stóp zwrotu indeksu WIG, ”Prace Naukowe Akademii Ekonomicznej we Wrocławiu nr 1021”, Wrocław.Shimizu K., 2010, Bootstrapping Stationary ARMA-GARCH Models, “Vieweg+Teubner”, Wiesbaden.Smolović J.C., Lipovina-Božović, M., Vujošević S., 2017, GARCH models in value at risk estimation: empirical evidence from the Montenegrin stock exchange, ”Economic Research-Ekonomska Istraživanja”.Szegö G., 2002, Measures of risk, ”Journal of Banking & finance 26”, Italy.Taylor S.J., 1986, Modelling financial time series, Chichester, New York.Trzpiot G., 2004, O wybranych własnościach miar ryzyka, „Badania Operacyjne i Decyzje, nr 3-4, Katowice.Trzpiot G., 2012, O własnościach transformujących miar ryzyka, „Analiza Szeregów Czasowych a Statystyczny Pomiar Ryzyka”, Zeszyty Naukowe Wydziałowe Uniwersytetu Ekonomicznego w Katowicach, Katowice.Trzpiot G., 2014, Optymalizacja portfela z wykorzystaniem koherentnych transformujących miar ryzyka, „Studia Ekonomiczne”, nr 208.Tsay R., 2005, Analysis of Financial Time Series, Willey, Hoboken.Wang S.S., 2002, A Risk a Measure that Goes beyond Coherence, “Actuarial Approach to Financial Risks”, Colloquium.Wang Y.-J., Chung H., Guo J.-H., 2012, A value-at-risk analysis of carry trades using skew-GARCH models, “Studies in Nonlinear Dynamics and Econometrics”, vol. 17, no. 4.Zhu D., Galbraith J. W., 2011, Modelling and forecasting expected shortfall with the generalized asymmetric Student-t and symmetric exponential power distributions, “Journal of Empirical Finance”, vol. 18, Issue 4.3(87)17619