Search Results

17 items found

  • How can the Blockchain environment affect competition?

    Click here to read the whole paper : A blockchain is a digital register of transactions, decentralized and distributed, using cryptographic techniques to ensure the integrity and security of processed information. The blockchain is revolutionizing the exchange of values, and its impact on society is compared to that of the Internet. As trust-generating machines, blockchains raise new issues in competition law, namely how to apply rules aimed at maintaining trust (European competition law) in an environment that, by nature, rejects the traditional trust of economic relationships, preferring instead the trust produced by computer code. The blockchain environment tends more and more to become a generator and support of economic exchanges. The acceleration of economic exchanges around and within blockchain technology encourages economic operators to use any means to increase their market share and thus gain a competitive advantage. Competition law aims at maintaining a virtuous economic market where competition on the merits prevails over unfair practices. We will see in the course of our study what the impact of the blockchain environment can be on competition, while making a distinction between practices carried out directly between economic operators and concerning blockchain technologies, and practices carried out within these ecosystems where the law of computer code reigns. The study analyzes the impact of the blockchain environment on competition through the prism of abuse of dominant position and anticompetitive agreements.

  • What is the impact of COVID-19 on Latin America?

    French version : English version : The Covid-19 health crisis has sharpened the economic crisis already impacted by the economic recession in Latin American countries. The contraction of economic activities, particularly in Argentina, largely due to the drop in household consumption, forced politicians to renegotiate the terms of their debts and consequently limit the deterioration of the rating by rating agencies to support the economy. Moreover, their lack of attractiveness to investors accentuated by political and trade tensions also affected by Covid-19 did not help the situation: higher inflation, higher market interest rates, depreciation of national currencies and increased social inequalities, lower oil prices as well as higher unemployment are the main negative effects on the economy of Latin American countries resulting in a downward revision of their GDP growth. As a result, the financial support of policies to curb these effects is widening the fiscal and budgetary deficit already impacted by tax evasion. To curb these crises, governments have respectively put in place exceptional measures such as tax relief, legal labour relief, allocation of financial aid and opening up the domestic market to private investors. This exceptional health crisis has profoundly changed the behaviour of Latin American governments and opened up new horizons of resilience to the economic crisis.

  • Is it possible to generate yield by using pair-trading on bonds with the cointegration method?

    French version : English version : In Finance, derivatives and arbitrage positions are two possible recourses for investors looking to increase their profit. The pair-trading is an alternative asset management method consisting on generating arbitrage opportunity between two assets having a similar evolution. For a given asset A and asset B, having a correlated price movement, the Pair-Trading strategy consists on selling the asset A once it reaches its peak value and buying the asset B once it reaches its minimum value. Specifically, pair-trading matches a long and a short position of two different correlated assets. It allows a simple hedging strategy to benefit from arbitrage opportunity in both bullish (increase) or bearish (decrease) market. Numerous pair-trading strategies are available. For instance, we can analyze the moving average dissimilarity or the correlation evolution. In this study, we will focus on Bond Pair-Trading method using the cointegration method, introduced by Engle & Granger, and stress on the possible spread cointegrations on bonds. The cointegration verifies the long-term relationship between two time series. The method consists, for two time series X𝑡and Y𝑡, to conduct an ordinary least-squares regression is made in order to determine the parameters α and β of the following equation: We then test the residue stationarity with the Augmented Dickey-Fuller (ADF). If it is the case, we consider a cointegration between two time series spread and can profit from a short run divergence between two assets. After simulating a pair-trade strategy on the bond market using the cointegration method regarding different scenarios and respecting given conditions (concerning the regression, the cointegration and the error level to determine the opportune moment to get into position) we notice that a 2-year difference in duration generates, in average, 3 more pair-trades compared to an increase of 50 bps the spread difference. We add 3 to 4 pair-trades with a duration difference of 2.5 years and 5 additional pair-trades for a 3-year duration while having an additional 5 bps for the spread difference. We conclude that increasing the spread difference or the duration difference poorly affects the number of pair-trades engendered. By studying the performance of pair-trades after 1 month, 3 months and 6 months of investment, we find that 50% of pair-trades generate negative returns over the whole time covered. However, 45% of pair-trades generate performance after 1 month, 35% after 3 months and 30% after 6 months. We can terminate on the necessity to not hold a position for a long term. By reckoning the maximum performance, we note that all pair-trades generate performance. Based on the different terms, between 55% and 70% of pair-trades generate a maximum return of 1%. The portion of pair-trades generating between 1% and 2% of return are steady: around 17% to 20%. However, from 1 month to 6 months, the portion of pair-trades generating over 2% of return goes from 30% to 45%. We can conclude that the most performing pair-trades require more time whereas the least performing pair-trades generate return swiftly. The pair-trade performances are represented by a curve with a J form. When we consider transaction costs (at least 1%) it is impossible to outperform the bond market due to peer-trade yields. The bond market is an efficient market.

  • Does artificial intelligence provide good forecasts of CAC 40 prices ?

    The CAC40, or CAC index, is the main stock market index on the Paris stock exchange. Its name, CAC, stands for “Cotation Assistée en Continu” (Continuous Assisted Rating) because it is a set of values that is updated every fifteen seconds. These changes are made every working day from 9am to 5.30pm. It represents the value of the forty largest French companies (the company's value being determined by the volume of share trading carried out). French version : English version : In more concrete terms, the CAC index reflects the general trend of the French economy. In an international context, it is not only the national economy that is represented but also the state of the world market. As France represents around twenty percent of the European economy, the CAC40 has a major international stake for investors. Euronext, the scientific council that is responsible for determining the stocks represented in the index, is made up of experts in stock market operations. It is the independence of this council that allows us to anticipate the evolution of this index. It is therefore the job of the investor that inspires our problem. Being able to predict the CAC40 share price enables us to generate profits thanks to the specific action levers of this profession. Depending on the amount of money invested, profits can be significant. We have limited our problem to the prediction of the evolution of the CAC index. The study will initially consist of a state of the art of prediction technics currently used to predict stock market values by financial professionals. Secondly, we will detail the choices of artificial intelligence algorithms that we have had to use and implement. We have implemented several models such as the LSTM model, ARIMA or GAN. This study retraces our method and the results obtained according to the models used.

  • Does corruption weight on corporate cash holdings decision?

    This paper deals with the issue of exogenous determinants of corporate cash holdings and more specifically the corruption variable. Thus, the study covered a sample of twelve emerging countries in Africa and the Middle East over a period from 2014 to 2019. Using a static regression model on our panel data, the results confirmed the impact of bribery on the cash holdings of African companies, but were not significant in the case of Middle Eastern countries. In order to confirm the robustness of our main model, we extended our estimates using a dynamic model. The results of the alternative model confirmed our initial results on the impact of corruption on the cash holdings of African companies. Consequently, these results show that the spread of corruption in Africa is an important factor in holding liquid assets, with companies amassing more liquidity to cope with this phenomenon. French version : English version :

  • How are financial institutions organized to fight against money laundering and terrorism financing?

    French version: English version: The main anti-money laundering and anti-terrorism financing procedures are known as Know Your Customer (KYC) and Client Due Diligence (CDD). The two processes, very similar, are different because they do not take place at the same stage. While KYC involves obtaining customer data before starting a business relationship, the CDD is about verifying the information provided by a customer throughout the relationship. As a result, the KYC and CDD processes allow banks to better understand and track their customers, thereby preventing the risk of criminal activity. ​ The recent definition of the term "money laundering" and the complexity of the global financial system over the past 30 years raise the challenge of detecting the "effective beneficiaries" of financial transactions that take place, in other words answering the question "who really benefits from the transaction?" This issue pushes financial actors and regulators to organize audit processes for stakeholders and clients with whom they are dealing. These "due diligence" procedures also aim to highlight the various risks, which may be internal to the agencies (operational, legal, reputational or concentration risk) or related to their clients. Finally, the role of these LCB-FT actors is also to provide recommendations on how to effectively address the identified risks. ​ The French regulatory system for fighting against money laundering and the Financing of Terrorism (LCB-FT) derives its source from the legislative part of the French Monetary and Financial Code. However, these instructions can be assessed according to the guidelines jointly issued by the Prudential Control and Resolution Authority (ACPR) and the French financial intelligence unit TRACFIN. Penalties for violations vary depending on the legal personality of the person committing the offence and its nature. In order to harmonize and strengthen the LCB-FT, the Financial Action Task Force (FATF) has issued guidelines to promote the fight against money laundering, to which member states must submit. They are grouped together in the form of 40 recommendations. Following the September 11 attacks, nine new recommendations were issued at the initiative of the FATF and other international standard-setters such as the International Monetary Fund (IMF), the World Bank, the ECB and the Basel Committee. The latter are helping to strengthen prevention against the financing of terrorism, reflecting the challenge of combating money laundering and terrorism financing.

  • How to regulate high-frequency trading strategies?

    French version : English version : The development of high-frequency trading strategies has revolutionized the structure of financial markets, but also the behavior of players operating on these markets, whether they practice this type of strategy or not. High-frequency trading (HFT) is intrinsically linked to technological developments related to Internet networks, information storage and processing. Indeed, it is based on the speed advantage that HFT players have over traditional players. THF players have a speed advantage of making many transactions and making small profits by playing on very short opportunities of up to w milliseconds. ​ We will see that segregation can be established within high frequency trading strategies. Some strategies, known as passive, improve market quality by injecting liquidity, while other strategies, known as aggressive, remove liquidity and thus degrade market quality. The structural modification brought about by high-frequency trading strategies seems to be established within financial markets. The role of regulation is then more to frame the development of such strategies and not to block/oppose them. The regulation of high-frequency trading strategies can be carried out in different ways depending on the objective to be achieved. ​ High-frequency trading strategies are thus regulated through the MiFID 2 regulatory framework governing the structure of European financial markets. The MiFID 2 regulatory framework has greatly modified the European financial markets by enshrining new obligations related to product governance, benefits and compensation, market structure and transparency, but also with regard to the regulation of high-frequency trading strategies. High-frequency trading strategies are also governed by market abuse regulations. High-frequency trading strategies can disrupt the equality of participants in financial markets. Indeed, high frequency trading strategies, which are numerous and different, have in common the technological advantage of speed to reach their end. The processing and use of this information by high-frequency trading firms could constitute market manipulation. ​ High-frequency trading strategies can also be regulated through a Tobin tax on financial transactions or specific taxation of high-frequency trading activities. Finally, the development of artificial intelligence and its application to high-frequency trading strategies can also have significant positive and negative effects. We will therefore examine the opportunity of adopting specific regulation.

  • Can we prevent companies from going bankrupt as a result of their exposure to short selling?

    Short selling is a transaction that consists in selling an asset that one does not hold but that one commits to deliver if a buyer is presented. It is therefore a bet on the decrease in value of this asset. If it is a share, it becomes a bet on the depreciation of the value of the company, or even its bankruptcy. French version : English version : The practice of short selling is to rent an asset, sell it at a higher price and then, buy it back at a lower price to return it to its original owner. There is thus a certain time gap where the position remains open between sale and purchase to generate profits. Therefore, by comparing the number of open short sales on total shares available on the market, one can determine a ratio called short interest, which expresses the percentage of people believing in a fall of the value. In the midst of the economic crisis of 2020, the AMF has decided to ban short selling in order to protect the interests of companies by reducing their exposure to selling pressure. In 1968, Professor Edward Altman published a paper in which he explained a ratio based on the financial data of companies, capable of generating an estimate of concerned companies’ probability of bankruptcy. Named Altman's Z-score, it is still widely used today and it is a good starting point for the search for companies in a fragile or even critical state. By including short interest in Altman's model, resulting in an adjusted Z-score, one can gauge the effect of short selling on the probability of business failure. Therefore, the goal of this study is to weigh the weight of exposure to short selling by comparing the results of the adjusted Z-score predictions to the original model in order to try to improve Altman's model and thus allow analysts to enhance their work. If the model is improved to a fairly good confidence level, this will allow us to generally conclude that short selling is a crucial factor in the road towards a company's bankruptcy.

  • How does the correlation of international markets evolve during crisis?

    2020, the year of pandemic and health disasters, but also of high volatility and crash of the financial markets. Comparable to 2018, between February 19 and March 9, 2020, the CAC lost nearly 23% of its value. On March 23, the S&P 500 lost 2.9% of its value and the VIX is at 62 points. On March 17, 2020, short selling is banned in France, and on April 20, 2020, oil prices became negative. The equity markets touched the ground while the public debt scraped the sky. French version : English version : ​ It is for such moments that the theory of portfolio diversification has been adopted, correlations between markets are taken into account in asset allocations alongside returns and volatilities, but many works have proved that correlations change according to the regime of the markets: the correlation between assets in bull markets differs from the correlation in bear markets. The assertions of Andrew Ang and Geert Bekaert in 1999 that 'if you can't rely on diversification in times of crisis, you may have to change the rules of the game' shed light on a major point in diversification theory which implies that correlations are stable regardless of the market regime. Arriving in 2001, Chesnay and Jondeau study international correlation in several markets such as the S&P, DAX and FTSE between 1988 and 1999 and conclude that correlations increase in periods of turbulence. ​ This paper will draw on Chesnay and Jondeau's studies and project their results over the last ten years in order to understand, using the Markov Switching Model, the behavior of international market correlation during periods of turbulence. Beginning with a review of the literature, we will move on to the Markov Switching Model theory to conduct a practical case that will be used to answer a specific question of : In view of the turbulence of the last ten years, could we reject the hypothesis of constancy of correlations and affirm their dependence on volatility regimes?

  • Is it possible to forecast VaR on oil using GARCHs models ?

    Energy products volatility involves specialists into the estimations of Value-at-Risk. Various models, derived from the GARCH model, are used independently, or not: The Exponential GARCH (EGARCH), Skewed Asymmetric Power ARCH (APARCH), Hyperbolic GARCH (HYGARCH) and Fractionally integrated GARCH (FIAPARCH). Our analysis focuses on the performance of a market yield estimation and the Value-at-Risk projection. This latter is evaluated compared to the closing prices observations from 1987 and December 2019, related to the WTI and to the Brent. French version : English version : Firstly, the following concepts are determined in order to establish the analysis methodology: The ARCH, GARCH, APARCH, FIAPARCH, HYGARCH models and the Value-at-Risk; the statistical precision test on the Value-at-Risk projections (Kupiec, de Eugle et Manganelli) and the long memory tests of the volatility (Geweke and Porter-Hudak log-periodogram regression, Robison semi-parametric Gaussian and the Lo & MacKinlay Variance relation). Then, a descriptive data study on the whole observations highlights a non-Gaussian distribution regarding the closing prices, the absence of white noise and the auto-correlated squares. The stationary test of the time series is positive while the unit root hypothesis is rebutted. The R/S Lo test highlights a long-term memory, confirmed by the GPH and GDP test at 95%. Following the different GARCH models in the sample, the t-student asymmetric distribution seems more productive.Moreover, the tests of FIGARCH, FIAPARCH, HYGARCH models using the Student distribution, the asymmetric Student law and the GARCH model with Gaussian distribution led in the sample highlights the t-Student asymmetric distribution performance, comparatively to the classic distribution. Furthermore, intra and extra-sample, the FIAPARCH model proves to be the closest to the Value-at-Risk. When certain existing models proved to be truly performant on a given day (APARCH), or only took consideration of some parameters (like the distribution model), our analysis demonstrates an experimental performance of the FIARPARCH using the asymmetric distribution of the t-Student law, considering the thick distribution tail and the long-term memory, for a high horizon (above 10 days). These results are in adequacy with the financial institutions requirements.