Dependence and Value at Risk in the Stock Markets from the Americas: A Copula Approach

This work applies copula modeling to estimate the degree of dependence among the nine major equity markets from the Western Hemisphere, seven emerging markets from Latin America (Argentina, Brazil, Chile, Colombia, Peru, Venezuela, Mexico) and the two mature markets from North America (Canada, United States). The relevance of copula-measured dependence is assessed estimating Value at Risk for bilateral portfolio investments, comparing it with conventional VaR methodologies. The data encompass daily time series for the 1992-2009 period

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Systemic risk in Chinese financial industries: a vine copula grouped CoVaR approach

This paper investigates systemic risk in Chinese financial industries by constructing a vine copula grouped CoVaR model, which accounts for the fact that various sub-industries are comprised of multiple financial institutions. The backtesting results indicate that the vine copula grouped model performs better in measuring the systemic risk in comparison to the vine copula model, which in turn validates the accuracy and effectiveness of the former. Moreover, the results indicate that banking is a major systemic risk contributor, even though it has a strong ability to resist risk. Additionally, the potential loss faced by the securities industry is big, but its systemic risk contribution is small. These results are of significance to investment decision and risk management

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

A new Copula-CoVaR approach incorporating the PSO-SVM for identifying systemically important financial institutions

The effective identification of systemically important financial institutions (SIFIs) is key to preventing and resolving systemic financial risks; thus, it is of great research significance for emerging countries to supervise SIFIs and manage systemic financial risks. Since traditional research on identifying SIFIs does not consider emerging machine learning models, it is difficult to properly fit the characteristics of actual financial institutions’ asset distribution. This paper proposes a new method for measuring SIFIs, integrating the PSO-SVM model into the Copula-CoVaR model. This new PSO-SVM-Copula-CoVaR model is meant to evaluate China’s SIFIs based on the publicly traded price data of Chinese listed financial institutions. The empirical results show that, compared with the traditional parameter method (GARCH model) and the nonparametric method (kernel density estimation), the marginal distribution estimation method using the PSO-SVM method can better fit the distribution of an institution’s financial asset return sequence. That is, the model proposed in this paper helps regulatory authorities improve the list of SIFIs more reasonably and implement effective regulatory measures

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

Nonparametric Multiple Change Point Analysis of the Global Financial Crisis

This paper presents an application of a recently developed approach by Matteson and James (2012) for the analysis of change points in a data set, namely major financial market indices converted to financial return series. The general problem concerns the inference of a change in the distribution of a set of time-ordered variables. The approach involves the nonparametric estimation of both the number of change points and the positions at which they occur. The approach is general and does not involve assumptions about the nature of the distributions involved or the type of change beyond the assumption of the existence of the α absolute moment, for some α ε (0,2). The estimation procedure is based on hierarchical clustering and the application of both divisive and agglomerative algorithms. The method is used to evaluate the impact of the Global Financial Crisis (GFC) on the US, French, German, UK, Japanese and Chinese markets, as represented by the S&P500, CAC, DAX, FTSE All Share, Nikkei 225 and Shanghai A share Indices, respectively, from 2003 to 2013. The approach is used to explore the timing and number of change points in the datasets corresponding to the GFC and subsequent European Debt Crisis

Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions

No embargo requiredThe global financial crisis in 2008 spurred the need to study systemic risk in financial markets, which is of interest to both academics and practitioners alike. We first aimed to measure and forecast systemic risk in global financial markets and then to construct a trade decision model for investors and financial institutions to assist them in forecasting risk and potential returns based on the results of the analysis of systemic risk. The factor copula-generalized autoregressive conditional heteroskedasticity (GARCH) models and component expected shortfall (CES) were combined for the first time in this study to measure systemic risk and the contribution of individual countries to global systemic risk in global financial markets. The use of factor copula-based models enabled the estimation of joint models in stages, thereby considerably reducing computational burden. A high-dimensional dataset of daily stock market indices of 43 countries covering the period 2003 to 2019 was used to represent global financial markets. The CES portfolios developed in this study, based on the forecasting results of systemic risk, not only allow spreading of systemic risk but may also enable investors and financial institutions to make profits. The main policy implication of our study is that forecasting systemic risk of global financial markets and developing portfolios can provide valuable insights for financial institutions and policy makers to diversify portfolios and spread risk for future investments and trade.</jats:p

New insights on hidden Markov models for time series data analysis

The goal of this thesis is to develop novel methods for the analysis of financial data by using hidden Markov models based approaches. The analysis focuses on univariate and multivariate financial time series, modeling interrelationships between financial returns throughout different statistical methods, such as graphical models, quantile and expectile regressions. The dissertation is divided into three chapters, each of them examining different classes of assets returns for a comprehensive risk analysis. The methodologies we propose are illustrated using real-world data and simulation studies