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L'objectif du travail était d'analyser les performances de la VaR à travers le backtesting de façon évaluer son remplacement par l'Expected shortfall. Pour cela, nous avons calculé la VaR à travers deux approches à savoir la simulation historique et l'approche paramétrique. Ensuite nous avons procédé au backtesting de ces VaR. Le résultat de ces backtesting nous a permis de voir que la VaR est inefficace comme modèle d'évaluation du risque de marché pendant les période de crise. Nous avons également remarqué que la VaR ne respecte pas toutes les propriétés d'une mesure cohérente du risque de marché. La VaR se limite uniquement à évaluer la perte pris en compte par son seuil de confiance. Or en ayant procéder au bactesting, nous avons remarqué que cette perte est souvent dépassée. Et l'outil de gestion du risque de marché qui permet d'estimer cette perte est l'Expected shortfall. Donc en plus de remplir toutes les propriétés d'une mesure cohérente du risque, l'Expected shortfall s'intéresse également à la perte que peut réaliser l'institution et qui se situe bien au-delà du seuil de confiance de la VaR. Fort de ces résultats, nous avons confirmés que le remplacement de la VaR par l'Expected shortfall est bien justifié.

Value at Risk, Expected Shortfall, Backtesting --- Sciences économiques & de gestion > Finance

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This master thesis examines the loss distribution of fourteen mutual fund investment styles, based on the Morningstar style boxes and the Environmental Social Governance score. A large dataset of 276056 to 1122872 monthly return observations were utilized for every investment style, from the observation period October 1999 to September 2019. We first use the concept of the standardized momentums to compare the investment style returns. After that we apply risk measurement techniques such as value at risk, expected shortfall and the parameters of the generalized pareto distribution to compare the probability of high negative returns across the investment styles. Finally we investigate the effect of macroeconomic changes on extreme negative mutual fund returns, by using the parameters of the generalized pareto distribution to calculate and compare the value at risk with a confidence level of 99.9%. To this end, we use U.S. and German data series for variables of money growth, economic growth, inflation, interest rates, VIX, equity and commodity markets as a fair representation of the macroeconomic fundamentals that can possibly influence mutual fund returns. Most results from our empirical study agree with the existing literature about the different risks and returns in regard to the mutual fund style dimensions and the impact of covariates on extreme negative returns of these investment styles. However, some of our findings are not in line with our expectations and the existing literature. Our key findings are, growth investment styles have on average a higher return than value investment styles. Furthermore, when the inflation is low growth investment styles have a lower probability of extreme negative returns than value investment styles. Moreover mutual funds that focus on companies with high market capitalization have on average higher returns by a similar risk, than mutual funds that focus on companies with low market capitalization. Lastly, the style dimensions, credit quality and interest sensitivity are not relevant for mutual funds, since they have similar returns and risk across the investment styles.

Value at Risk --- Expected Shortfall --- Risk measurement --- Generalized Pareto Distribution --- Extreme Value Theory --- Mutual funds --- Investment styles --- Sciences économiques & de gestion > Finance

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With the availability of new and more comprehensive financial market data, making headlines of massive public interest due to recent periods of extreme volatility and crashes, the field of computational finance is evolving ever faster thanks to significant advances made theoretically, and to the massive increase in accessible computational resources. This volume includes a wide variety of theoretical and empirical contributions that address a range of issues and topics related to computational finance. It collects contributions on the use of new and innovative techniques for modeling financial asset returns and volatility, on the use of novel computational methods for pricing, hedging, the risk management of financial instruments, and on the use of new high-dimensional or high-frequency data in multivariate applications in today’s complex world. The papers develop new multivariate models for financial returns and novel techniques for pricing derivatives in such flexible models, examine how pricing and hedging techniques can be used to assess the challenges faced by insurance companies, pension plan participants, and market participants in general, by changing the regulatory requirements. Additionally, they consider the issues related to high-frequency trading and statistical arbitrage in particular, and explore the use of such data to asses risk and volatility in financial markets.

insurance --- Solvency II --- risk-neutral models --- computational finance --- asset pricing models --- overnight price gaps --- financial econometrics --- mean-reversion --- statistical arbitrage --- high-frequency data --- jump-diffusion model --- instantaneous volatility --- directional-change --- seasonality --- forex --- bitcoin --- S&amp --- P500 --- risk management --- drawdown --- safe assets --- securitisation --- dealer behaviour --- liquidity --- bid–ask spread --- least-squares Monte Carlo --- put-call symmetry --- regression --- simulation --- algorithmic trading --- market quality --- defined contribution plan --- probability of shortfall --- quadratic shortfall --- dynamic asset allocation --- resampled backtests --- stochastic covariance --- 4/2 model --- option pricing --- risk measures --- American options --- exercise boundary --- Monte Carlo --- multiple exercise options --- dynamic programming --- stochastic optimal control --- asset pricing --- calibration --- derivatives --- hedging --- multivariate models --- volatility

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Recently, the world economy has witnessed some turbulence and instability, both of which have raised concerns and added threats to the global economy. For example, climate change, trade war, regional political tension, Brexit, and the very recent Coronavirus epidemic have hit several countries across all continents at an astonishing rate and are among some of the factors that have increased uncertainty. We have also noticed a surge in technological innovations and their implications in the banking and financial sectors. Today, we talk about blockchain, fintech, insurtech, regtech, and big tech, which have changed the business model of banks, financial institutions, and also the management model for firms and public administration. To get better insight into all these trends, economists have used the finance and macroeconomic theory to analyze the micro- and macroeconomic consequences of all these events and to study their impacts on economic and financial sector stability, as well as economic development and growth. In this Special Issue, Economies is inviting researchers and academicians to submit their work to a Special Issue dedicated to “The Theory Applications of Finance and Macroeconomics”. Some of the topics that contribute to the Issue might address issues of trade tension, climate change, blockchain and cryptocurrencies, financial liberalization, macroeconomic issues, principles of international finance, and open economy macroeconomics.

Economics, finance, business & management --- broad money --- share market --- bank credit --- private sector growth --- financial inclusion --- banking sector of Pakistan --- supply side --- ARDL --- banking --- competition --- Lerner Index --- economic relationship --- TFP --- system GMM --- China–Africa --- bounds cointegration test --- information criterion --- model selection techniques --- plausible model --- fiscal redistribution --- financial development --- fiscal expenditure --- property income --- economic growth --- trade balance --- panel estimates --- macroeconomics --- poverty --- microfinance --- Pakistan --- firm growth --- threshold estimation --- public debt --- threshold effects --- MENA region --- quantum mechanics --- wave function --- extreme value analysis --- Bayesian inference --- stock market --- Value at Risk (VaR) --- Expected Shortfall (ES) --- prediction

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Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.

cluster analysis --- equity index networks --- machine learning --- copulas --- dependence structures --- quotient of random variables --- density functions --- distribution functions --- multi-factor model --- risk factors --- OLS and ridge regression model --- python --- chi-square test --- quantile --- VaR --- quadrangle --- CVaR --- conditional value-at-risk --- expected shortfall --- ES --- superquantile --- deviation --- risk --- error --- regret --- minimization --- CVaR estimation --- regression --- linear regression --- linear programming --- portfolio safeguard --- PSG --- equity option pricing --- factor models --- stochastic volatility --- jumps --- mathematics --- probability --- statistics --- finance --- applications --- investment home bias (IHB) --- bivariate first-degree stochastic dominance (BFSD) --- keeping up with the Joneses (KUJ) --- correlation loving (CL) --- return spillover --- volatility spillover --- optimal weights --- hedge ratios --- US financial crisis --- Chinese stock market crash --- stock price prediction --- auto-regressive integrated moving average --- artificial neural network --- stochastic process-geometric Brownian motion --- financial models --- firm performance --- causality tests --- leverage --- long-term debt --- capital structure --- shock spillover