nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒04‒26
nine papers chosen by

  1. COVID-19 Pandemic, Stimulus Packages and Stock Returns in Vietnam By Vu, Son T.; Le, Tam T.; Nguyen, Chi N. L.; Le, Duong T.; Le, Phuc H.; Truong2, Ha K.
  2. What Triggers Stock Market Jumps? By Scott R. Baker; Nicholas Bloom; Steven J. Davis; Marco C. Sammon
  3. Habits die hard: implications for bond and stock markets internationally By Thomas Nitschka; Shajivan Satkurunathan
  4. Tackling the Volatility Paradox: Spillover Persistence and Systemic Risk By Christian Kubitza
  5. Accuracies of Model Risks in Finance using Machine Learning By Berthine Nyunga Mpinda; Jules Sadefo Kamdem; Salomey Osei; Jeremiah Fadugba
  6. Event studies on investor sentiment By Marc-Aurèle Divernois; Damir Filipović
  7. Sentiment Regimes and Reaction of Stock Markets to Conventional and Unconventional Monetary Policies: Evidence from OECD Countries By Oguzhan Cepni; Rangan Gupta; Qiang Ji
  8. Green Bonds: the Sovereign Issuers' Perspective By Raffaele Doronzo; Vittorio Siracusa; Stefano Antonelli
  9. Foreign exchange markets: price response and spread impact By Juan Camilo Henao Londono; Thomas Guhr

  1. By: Vu, Son T.; Le, Tam T.; Nguyen, Chi N. L.; Le, Duong T.; Le, Phuc H.; Truong2, Ha K.
    Abstract: This paper investigates the impacts of COVID-19’s new cases and stimulus packages on the daily stock returns of five key economic sectors (Finance, Fast-moving-consumer-goods (FMCG), Healthcare, Oil and Gas, and Telecommunication) in Vietnam – one of the best countries in the world for handling COVID-19. The research team uses the Pool OLS method, with the panel data of 11 342 observations from 107 listed firms in these five sectors in the period January-June 2020. The key findings are (i) all sectors’ stock returns are negatively affected by daily new confirmed cases of COVID-19, the hardest hit is on the financial sector, followed by FMCG, healthcare, oil and gas, and telecommunications sectors. Vietnam did not have many affected cases, but low average income makes investors and consumers more careful and hesitate to spend/invest; (ii) in contrast to prior studies, stimulus packages did not accelerate the growth of stock returns in all sectors, with the order from most to least negatively affected: finance, oil and gas, telecommunication, healthcare, and FMCG. The slow implementation made investors skeptical of the growth potential of firms, they assess the stimulus packages as the signs of economic downturn. This fact leads to different recommendations for the Vietnamese Government in combating COVID-19.
    Date: 2021–04–11
  2. By: Scott R. Baker; Nicholas Bloom; Steven J. Davis; Marco C. Sammon
    Abstract: We examine next-day newspaper accounts of large daily jumps in 16 national stock markets to assess their proximate cause, clarity as to cause, and the geographic source of the market-moving news. Our sample of 6,200 market jumps yields several findings. First, policy news – mainly associated with monetary policy and government spending – triggers a greater share of upward than downward jumps in all countries. Second, the policy share of upward jumps is inversely related to stock market performance in the preceding three months. This pattern strengthens in the postwar period. Third, market volatility is much lower after jumps triggered by monetary policy news than after other jumps, unconditionally and conditional on past volatility and other controls. Fourth, greater clarity as to jump reason also foreshadows lower volatility. Clarity in this sense has trended upwards over the past century. Finally, and excluding U.S. jumps, leading newspapers attribute one-third of jumps in their own national stock markets to developments that originate in or relate to the United States. The U.S. role in this regard dwarfs that of Europe and China.
    JEL: E44 E58 E62 G12 G17
    Date: 2021–04
  3. By: Thomas Nitschka; Shajivan Satkurunathan
    Abstract: This paper assesses whether the global fall in inflation expectations together with increased fear of recession, the economic mechanism that drives asset prices in a model with consumption habits, help to explain the downward trajectory in nominal government bond yields and the stock price dynamics of six major economies from 1988 to 2019. We calibrate the habit model for each country separately. For most countries, focusing the calibrations on matching average ten-year bond yields allows one to generate artificial time series of bond yields and price-consumption ratios that follow the long-run time series patterns of their counterparts in the data.
    Keywords: Consumption habit, return, risk premium, yields
    JEL: G12 G15
    Date: 2021
  4. By: Christian Kubitza (University of Bonn)
    Abstract: This paper proposes Spillover Persistence as a measure for financial fragility. The volatility paradox predicts that fragility builds up when volatility is low, which challenges existing measures. Spillover Persistence tackles this challenge by exploring a novel dimension of systemic risk: loss dynamics. I document that Spillover Persistence declines when fragility builds up, during the run-up phase of crises and asset price bubbles, and increases when systemic risk materializes. Variation in financial constraints connects Spillover Persistence to fragility. The results are consistent with the volatility paradox in recent macro-finance models, and highlight the usefulness of loss dynamics to disentangle fragility from amplification effects.
    Keywords: Systemic Risk, Fragility, Financial Crises, Asset Price Bubbles, Fire Sales
    JEL: E44 G01 G12 G20 G32
    Date: 2021–04
  5. By: Berthine Nyunga Mpinda; Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Salomey Osei; Jeremiah Fadugba
    Abstract: There is increasing interest in using Artificial Intelligence (AI) and machine learning techniques to enhance risk management from credit risk to operational risk. Moreover, recent applications of machine learning models in risk management have proved efficient. That notwithstanding, while using machine learning techniques can have considerable benefits, they also can introduce risk of their own, when the models are wrong. Therefore, machine learning models must be tested and validated before they can be used. The aim of this work is to explore some existing machine learning models for operational risk, by comparing their accuracies. Because a model should add value and reduce risk, particular attention is paid on how to evaluate it's performance, robustness and limitations. After using the existing machine learning and deep learning methods for operational risk, particularly on risk of fraud, we compared accuracies of these models based on the following metrics: accuracy, F1-Score, AUROC curve and precision. We equally used quantitative validation such as Back-testing and Stress-testing for performance analysis of the model on historical data, and the sensibility of the model for extreme but plausible scenarios like the Covid-19 period. Our results show that, Logistic regression out performs all deep learning models considered for fraud detection
    Keywords: Machine Learning,Model Risk,Credit Card Fraud,Decisions Support,Stress-Testing
    Date: 2021–04–07
  6. By: Marc-Aurèle Divernois (EPFL; Swiss Finance Institute); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute)
    Abstract: 60 million tweets are scraped from over 10 years and classified into bullish, bearish or neutral classes to create firm-individual polarity time-series. Changes in polarity are associated with changes of the same sign in contemporaneous stock returns. On average, polarity is not able to predict next day stock returns but when we focus on specific events (defined as sudden peak of tweet activity), polarity has predictive powers on abnormal returns. Finally, we show that bad events act more as surprises than good events.
    Keywords: Investor sentiment, Event study, Polarity, Social Media, Microblogging, Natural Language Processing, Crowd Wisdom
    JEL: G11 G14 C32
    Date: 2021–04
  7. By: Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Qiang Ji (Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China)
    Abstract: In this paper, we investigate how conventional and unconventional monetary policy shocks affect the stock market of eight advanced economies, namely, Canada, France, Germany, Japan, Italy, Spain, the U.K., and the U.S., conditional on the state of sentiment. In this regard, we use a panel vector auto-regression (VAR) with monthly data (on output, prices, equity prices, metrics of monetary policies, and consumer and business sentiments) over the period of January 2007 till July 2020, with the monetary policy shock identified through the use of both zero and sign restrictions. We find robust evidence that, compared to the low investor sentiment regime, the reaction of stock prices to expansionary monetary policy shocks is stronger in the state associated with relatively higher optimism, both for the overall panel and the individual countries (with some degree of heterogeneity). Our findings have important implications for academicians, investors, and policymakers.
    Keywords: Conventional and unconventional monetary policies, equity prices, sentiment, OECD countries, panel VAR, zero and sign restrictions
    JEL: C32 C33 E30 E51 E52 G15
    Date: 2021–04
  8. By: Raffaele Doronzo (Bank of Italy); Vittorio Siracusa (Bank of Italy); Stefano Antonelli (Bank of Italy)
    Abstract: This paper aims at providing an assessment of green bonds from the perspective of sovereign issuers. After a brief depiction of green bonds’ features, we describe the market evolution, present the EU regulatory framework and identify the main benefits and costs for sovereign issuers. We focus on the financial performance of these securities in primary and secondary markets. First, we compare the yields at issuance of sovereign green bonds with non-green bonds of the same issuer with the same maturity. Then we analyse the secondary market performance of green bonds issued by France, Belgium, Ireland and the Netherlands, and we do not find, any remarkable price difference between green and conventional bonds, even after controlling for their different degree of liquidity. Nevertheless, this should not discourage Sovereigns from entering the market since the reason for issuing these securities does not simply hinge upon short-term financial convenience. Green bonds can actually help Sovereigns to mitigate environmental risks and to cope with the intergenerational trade-off in climate-related policies.
    Keywords: green bonds, public debt, debt management
    JEL: H23 H63 Q56
    Date: 2021–03
  9. By: Juan Camilo Henao Londono; Thomas Guhr
    Abstract: In spite of the considerable interest, a thorough statistical analysis of foreign exchange markets was hampered by limited access to data. This changed, and nowadays such data analyses are possible down to the level of ticks and over long time scales. We analyze price response functions in the foreign exchange market for different years and different time scales. Such response functions provide quantitative information on the deviation from Markovian behavior. The price response functions show an increase to a maximum followed by a slow decrease as the time lag grows, in trade time scale and in physical time scale, for all analyzed years. Furthermore, we use a price increment point (pip) spread definition to group different foreign exchange pairs and analyze the impact of the spread in the price response functions. We found that large pip spreads have stronger impact on the response. This is similar to what has been found in stock markets.
    Date: 2021–04

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