nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒05‒18
eleven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis By Papadamou, Stephanos; Fassas, Athanasios; Kenourgios, Dimitris; Dimitriou, Dimitrios
  2. The Global Impact of COVID-19 on Fintech Adoption By Jonathan Fu; Mrinal Mishra
  3. How Market Sentiment Drives Forecasts of Stock Returns By Roman Frydman; Nicholas Mangee; Josh Stillwagon
  4. On the Profitability of Momentum Strategies and Optimal Leverage Rules By Lundström, Christian
  5. Measuring Systemic Risk: A Quantile Factor Analysis By Andrés Sagner
  6. Quantile Consumption-Capital Asset Pricing By Wang, Chih-Wei; Lopes Moreira Da Veiga, María Helena; Taamouti, Abderrahim; Ramos, Sofía B.
  7. Direct versus iterated multi-period Value at Risk By Ruiz Ortega, Esther; Nieto Delfin, Maria Rosa
  8. RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio By Kei Nakagawa; Shuhei Noma; Masaya Abe
  9. The Jarrow & Turnbull setting revisited By Thomas Krabichler; Josef Teichmann
  10. Equity Premium Prediction and the State of the Economy By Ilias Tsiakas; Jiahan Li; Haibin Zhang
  11. Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio By Rui Pedro Brito; Pedro Maria Corte Real Alarcão Judice

  1. By: Papadamou, Stephanos; Fassas, Athanasios; Kenourgios, Dimitris; Dimitriou, Dimitrios
    Abstract: We investigate the effects of a google trend synthetic index concerning corona virus, as a composite indicator of searching term and theme, on the implied volatility of thirteen major stock markets, covering Europe, Asia, USA and Australia regions by using panel data analysis along with several model specifications and robustness tests. Increased search queries for COVID-19 not only have a direct effect on implied volatility, but also have an indirect effect via stock returns highlighting a risk-aversion channel operating over pandemic conditions. We show that these direct and indirect effects are stronger in Europe relative to the rest of the world. Moreover, in a PVAR framework, a positive shock on stock returns may calm down google searching about COVID-19 in Europe. Our findings suggest that google based anxiety about COVID-19 contagion effects leads to elevated risk-aversion in stock markets. Understanding the links between investors’ decision over a pandemic crisis and asset prices variability is critical for understanding the policy measures needed in markets and economies.
    Keywords: COVID-19 pandemic; google trends; implied volatility; stock returns; panel data
    JEL: C33 D83 G12 G14
    Date: 2020–05–02
  2. By: Jonathan Fu (Center for Sustainable Finance and Private Wealth; University of Zurich - Department of Banking and Finance); Mrinal Mishra (Swiss Finance Institute; University of Zurich - Department of Banking and Finance)
    Abstract: We draw on mobile application data from 74 countries to document the effects of the COVID-19 pandemic on the adoption of digital finance and fintech. We estimate that the spread of COVID-19 and related government lockdowns have led to between a 24 and 32 percent increase in the relative rate of daily downloads of finance mobile applications in the sample countries. In absolute terms, this equates to an average daily increase of roughly 5.2 to 6.3 million application downloads and an aggregate increase of about 316 million app downloads since the pandemic’s outbreak to the present, taking into account prior trends. Most regions across the world exhibit notable increases in absolute, relative, and per capita terms. Preliminary analysis of country-level characteristics suggest that market size and demographics, rather than level of economic development and ex-ante adoption rates, drive differential trends.
    Keywords: digital finance, fintech, financial inclusion, technological adoption, COVID-19, cross-country
    JEL: G23 G20
    Date: 2020–05
  3. By: Roman Frydman (New York University); Nicholas Mangee (Georgia Southern University); Josh Stillwagon (Babson College)
    Abstract: We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our analysis yields four main findings. First, if good (bad) “news” about dividends and interest rates coincides with participants’ optimism (pessimism), the news about these fundamentals has a significant effect on participants’ forecasts of future returns and has the expected signs (positive for dividends and negative for interest rates). Second, in models without interactions, or when market sentiment is neutral or conflicts with news about dividends and/or interest rates, this news often does not have a significant effect on ex ante or ex post returns. Third, market sentiment is largely unrelated to the state of economic activity, indicating that it is driven by non-fundamental considerations. Moreover, market sentiment influences stock returns highly irregularly, in terms of both timing and magnitude. This finding supports recent theoretical approaches recognizing that economists and market participants alike face Knightian uncertainty about the correct model driving stock returns.
    Keywords: stock-return forecasts, fundamentals, market sentiment, structural change, model ambiguity.
    JEL: G12 G14 C58
    Date: 2020–04
  4. By: Lundström, Christian (Department of Economics, Umeå University)
    Abstract: Paper [I] tests the success rate of trades and the returns of the Opening Range Breakout (ORB) day trading strategy. A trader that trades the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days, and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game when applied to a long time series of crude oil futures contracts. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of low, high, open and close over a given time horizon. Paper [II] assesses the returns of the Opening Range Breakout (ORB) day trading strategy across volatility states of the underlying asset. We calculate the average daily returns of the ORB strategy for each volatility state when applied on long time series of crude oil and S&P 500 index futures contracts. We find an average difference in returns between the highest and lowest volatility state of around 200 basis points per day for crude oil, and of around 150 basis points per day for the S&P 500. Our result suggests that ORB strategy traders can be profitable, even in the long-run, but that the success in day trading to a large extent depend on the volatility of the underlying asset. Paper [III] performs empirical analysis on short-term and long-term Commodity Trading Advisor (CTA) strategies regarding their exposures to unanticipated risk shocks. Previous research documents that CTA strategies in general offer diversification opportunities during equity market crisis situations when evaluated as a group, but do not separate between short-term and long-term CTA strategies. When separating between short-term and long-term CTA strategies, this paper finds that only short-term CTA strategies provide a significant, and consistent, exposure to unanticipated risk shocks while long-term CTA strategies do not. For the purpose of diversifying a portfolio during equity market crisis situations, our result suggests that an investor should allocate to short-term CTA strategies rather than to long-term CTA strategies. Paper [IV] posits that it is possible to obtain an optimal leverage factor for financial instruments equipped with embedded leverage. By applying the Kelly criterion for optimal leverage, we show that there exists a uniquely optimal level of leverage for maximizing the long-run profit of embedded leverage instruments. The implication of an existing unique optimum is that a smaller leverage factor than optimal leads to a lower long-term profit than is feasible, but also that a larger leverage factor leads to a lower long-term profit than is feasible. Our empirical analysis shows how an optimal level of embedded leverage can increase the profitability of Exchange Traded Products. Paper [V] systematically analyses the effect of leverage on long-run profit when trading the Opening Range Breakout (ORB) day trading strategy. This paper clarifies the relation to two optimal leverage rules proposed for maximizing trading profit; the Kelly criterion and the Optimal fraction criterion. Our empirical analysis shows how leverage can increase day trading profit in-sample and out-of-sample when applied to a long time series of DAX 30 index futures contracts.
    Keywords: Bootstrap; Exchange Traded Products; Kelly criterion; Money management; Opening Range Breakout strategies; Optimal fraction criterion; Time series momentum
    JEL: C22 C58 C63 G11 G14 G17
    Date: 2020–05–06
  5. By: Andrés Sagner
    Abstract: This paper proposes a novel measure to quantify systemic risk from the information contained in asset returns. In the context of the external habits formation model of Campbell and Cochrane (1999), and under the assumption that stock returns are heteroskedastic, I show that equilibrium risk premium has a factor structure where the factors are a monotonic transformation of the surplus consumption ratio, a state variable that captures the systemic risk in the structural model. The restrictions implied by the model suppose a setup where one of the factors affects the variance of excess returns. Therefore, the factor model is estimated employing an adapted version of the Quantile Principal Components estimation procedure proposed by Sagner (2019). Simulations of the structural model under alternative parameterizations calibrated for the US show a good performance of the proposed systemic risk metric computed at quantiles different than the median. When estimated using quarterly post-war data, the proposed measure displays significant hikes that coincide with both several US recession periods and episodes of substantial financial market turbulences. Finally, the systemic risk estimator can forecast sharp shifts in both economic activity and industrial production up to one quarter ahead.
    Date: 2020–04
  6. By: Wang, Chih-Wei; Lopes Moreira Da Veiga, María Helena; Taamouti, Abderrahim; Ramos, Sofía B.
    Abstract: The Consumption-Capital Asset Pricing Model is a statement about the mean of asset returns anddoes not provide any information on the returns' quantiles. Using quantile maximization decisiontheory, this paper considers a quantile-based Euler equation that states that the asset price is afunction of the quantiles of the payoff, consumption growth, stochastic discount factor and riskaversion. Assuming that the consumption growth rate is log-elliptically distributed, we show thatreturns' quantiles are non-monotone functions of the consumption growth volatility. Using data fromthe United States and United Kingdom, empirical evidence validates our theoretical results and showsthat this volatility is a driving factor of the returns' distribution.
    Keywords: Stochastic Volatility; Quantile Utility Function; Quantile-Based Euler Equation; Consumption Volatility; Ccapm; Asset Pricing
    JEL: G12 C58 C21
    Date: 2020–05–06
  7. By: Ruiz Ortega, Esther; Nieto Delfin, Maria Rosa
    Abstract: Although the Basel Accords require financial institutions to report daily predictions ofValue at Risk (VaR) computed using ten-day returns, a vast part of the literature deals withVaR predictions based on one-day returns. From the practitioner point of view, some ofthe conclusions about the best methods to estimate one-period VaR could not be directlygeneralized to multi-period VaR. Consequently, in the context of two-step VaR predictors,we use simulated and real data to compare direct and iterated predictions of multi-periodVaR based on ten-day returns assuming that the conditional variances of one-period returnsfollow a GARCH-type model. We show that multiperiod VaR predictions based on iteratingan asymmetric GJR model with normal or bootstrapped errors are often preferred whencompared with direct methods that are often biased and inefficient.
    Keywords: Risk; Multi-Step Forecasts; Gjr Model; Feasible Historical Simulation
    JEL: C58 C53 C22 G17
    Date: 2020–05–07
  8. By: Kei Nakagawa; Shuhei Noma; Masaya Abe
    Abstract: The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e., mean and variance). Although the variance would be the most fundamental risk measure to be minimized, it has several drawbacks. Conditional Value-at-Risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of well-known variance-related risk measures, and because of its computational efficiencies, it has gained popularity. CVaR is defined as the expected value of the loss that occurs beyond a certain probability level ($\beta$). However, portfolio optimization problems that use CVaR as a risk measure are formulated with a single $\beta$ and may output significantly different portfolios depending on how the $\beta$ is selected. We confirm even small changes in $\beta$ can result in huge changes in the whole portfolio structure. In order to improve this problem, we propose RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio. We perform experiments on well-known benchmarks to evaluate the proposed portfolio. Compared with various portfolios, RM-CVaR demonstrates a superior performance of having both higher risk-adjusted returns and lower maximum drawdown.
    Date: 2020–04
  9. By: Thomas Krabichler; Josef Teichmann
    Abstract: We consider a financial market with zero-coupon bonds that are exposed to credit and liquidity risk. We revisit the famous Jarrow & Turnbull setting in order to account for these two intricately intertwined risk types. We utilise the foreign exchange analogy that interprets defaultable zero-coupon bonds as a conversion of non-defaultable foreign counterparts. The relevant exchange rate is only partially observable in the market filtration, which leads us naturally to an application of the concept of platonic financial markets. We provide an example of tractable term structure models that are driven by a two-dimensional affine jump diffusion. Furthermore, we derive explicit valuation formulae for marketable products, e.g., for credit default swaps.
    Date: 2020–04
  10. By: Ilias Tsiakas (Department of Economics and Finance, University of Guelph, Canada; Rimini Centre for Economic Analysis); Jiahan Li (GMO LLC); Haibin Zhang (University of Guelph, Canada)
    Abstract: We detect cyclical variation in the predictive information of economic fundamentals, which can be used to substantially improve and simplify out-of-sample equity premium prediction. Economic fundamentals based on stock-specific information (notably the dividend yield) deliver better predictions in expansions. Economic fundamentals based on aggregate information (notably the short rate) deliver better predictions in recessions. Accordingly, a simple forecast combination of one predictor that generates cyclical forecasts and one predictor that generates countercyclical forecasts can deliver statistically significant and economically valuable equity premium predictions in both expansions and recessions. A prominent two-predictor forecast combination that performs well is the dividend yield and the short rate. Strategies designed for ex-ante timing of the business cycle can provide additional economic gains in equity premium prediction.
    Keywords: Equity Premium; Out-of-Sample Prediction; Economic Fundamentals; Business Cycle; Financial Cycle; Diversification
    JEL: G11 G14 G17
    Date: 2020–05
  11. By: Rui Pedro Brito (Centre for Business and Economics CeBER and Faculty of Economics, University of Coimbra); Pedro Maria Corte Real Alarcão Judice (ISCTE Business Research Unit)
    Abstract: In this paper we perform a quantitative analysis, under the IFRS 9 framework, on the tradeoff of classifying a financial asset at amortized cost versus at fair value. We define and implement a banking impairment model in order to quantify the forward-looking expected credit loss. Based on the suggested impairment model we conduct a backtest on the 10-year Portuguese Government bonds, for the time period from January 2003 to December 2019. The Portuguese bonds’ history constitutes a very rich data set for our experiment, as these bonds have experienced significant downgrades during the 2011-2014 financial crisis. We suggest a quantitative and systematic approach in order to find efficient allocations, in an income/downside comprehensive income bi-dimensional space. Resorting to stochastic simulation, we show a possible approach to mitigate the estimation error ingrained in the proposed bi-objective stochastic model. Finally, we assess the out-of-sample performance of some of the suggested efficient allocations.
    Keywords: Asset Classification, Backtesting, IFRS 9, Derivative-Free Optimization, Sensitivity Analysis, Stochastic Simulation.; Directed technical change; lobbying power; ineciency; economic growth; wage inequality; quantitative implications..
    JEL: C44 C51 C61 C63 C88 G11 G24
    Date: 2020–05

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