nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒02‒28
eleven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Maximizing the Out-of-Sample Sharpe Ratio By Lassance, Nathan
  2. Stock returns predictability with unstable predictors By Calonaci, Fabio; Kapetanios, George; Price, Simon
  3. How easy is it for investment managers to deploy their talent in green and brown stocks? By David Ardia; Keven Bluteau; Thien Duy Tran
  4. Skewness Expectations and Portfolio Choice By Drerup, Tilman; Wibral, Matthias; Zimpelmann, Christian
  5. Understanding the Ownership Structure of Corporate Bonds By Ralph S. J. Koijen; Motohiro Yogo
  6. Conditional Heteroskedasticity in the Volatility of Asset Returns By Ding, Y.
  7. Credit Default Swaps and Credit Risk Reallocation By Dorian Henricot; Thibaut Piquard
  8. Internal multi-portfolio rebalancing processes: Linking resource allocation models and biproportional matrix techniques to portfolio management By Kelli Francis-Staite
  9. A composite indicator of sovereign bond market liquidity in the euro area By Riccardo Poli; Marco Taboga
  11. Return and volatility spillovers between Chinese and U.S. Clean Energy Related Stocks: Evidence from VAR-MGARCH estimations By Karel Janda; Ladislav Kristoufek; Binyi Zhang

  1. By: Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: Maximizing the out-of-sample Sharpe ratio is an important objective for investors. To achieve this, we characterize optimal portfolio combinations maximizing expected out-of-sample Sharpe ratio. When investing in the risk-free asset is allowed and combination coefficients are unconstrained, as in Kan and Zhou (2007), we uncover that combining portfolios to maximize expected out-of-sample utility optimizes expected outof-sample Sharpe ratio as well. However, the two criteria are not equivalent for portfolios fully invested in risky assets, and in this case we show how to adapt the optimal portfolio combination of Kan, Wang, and Zhou (2021) to maximize expected out-of-sample Sharpe ratio. We find that the proposed mean-variance portfolio combinations calibrated to maximize expected out-of-sample Sharpe ratio generally outperform the considered benchmarks. Relative to the minimum-variance portfolio estimated with a nonlinearly shrunk covariance matrix, the annualized out-of-sample Sharpe ratio increases from 0.988 to 1.110 before transaction costs, and from 0.914 to 1.007 net of transaction costs, on average across four typical datasets.
    Keywords: Mean-variance portfolio ; parameter uncertainty ; estimation risk ; out-of-sample performance
    Date: 2021–12–23
  2. By: Calonaci, Fabio; Kapetanios, George; Price, Simon
    Abstract: We re-examine predictability of US stock returns. Theoretically well-founded models predict that stationary combinations of I(1) variables such as the dividend or earnings to price ratios or the consumption/asset/income relationship often known as CAY may predict returns. However, there is evidence that these relationships are unstable, and that allowing for discrete shifts in the unconditional mean (location shifts) can lead to greater predictability. It is unclear why there should be a small number of discrete shifts and we allow for more general instability in the predictors, characterised by smooth variation variation, using a method introduced by Giraitis, Kapetanios and Yates. This can remove persistent components from observed time series, that may otherwise account for the presence of near unit root type behaviour. Our methodology may therefore be seen as an alternative to the widely used IVX methods where there is strong persistence in the predictor. We apply this to the three predictors mentioned above in a sample from 1952 to 2019 (including the financial crisis but excluding the Covid pandemic) and find that modelling smooth instability improves predictability and forecasting performance and tends to outperform discrete location shifts, whether identified by in-sample Bai-Perron tests or Markov-switching models.
    Keywords: returns predictability; long horizons; instability
    Date: 2022–02–18
  3. By: David Ardia; Keven Bluteau; Thien Duy Tran
    Abstract: We explore the realized alpha-performance heterogeneity in green and brown stocks' universes using the peer performance ratios of Ardia and Boudt (2018). Focusing on S&P 500 index firms over 2014-2020 and defining peer groups in terms of firms' greenhouse gas emission levels, we find that, on average, about 20% of the stocks differentiate themselves from their peers in terms of future performance. We see a much higher time-variation in this opportunity set within brown stocks. Furthermore, the performance heterogeneity has decreased over time, especially for green stocks, implying that it is now more difficult for investment managers to deploy their skills when choosing among low-GHG intensity stocks.
    Date: 2022–01
  4. By: Drerup, Tilman (University of Bonn); Wibral, Matthias (Maastricht University); Zimpelmann, Christian (IZA)
    Abstract: Many models of investor behavior predict that investors prefer assets that they believe to have positively skewed return distributions. We provide a direct test of this prediction in a representative sample of the Dutch population. Using individual-level data on return expectations for a broad index and a single stock, we show that portfolio allocations increase with the skewness of respondents’ return expectations for the respective asset, controlling for other moments of a respondent’s expectations and sociodemographic information. We also show that while an individual’s expectations are correlated across assets, sociodemographics only capture very little of the substantial heterogeneity in expectations.
    Keywords: portfolio choice, stock market expectations, skewness, behavioral finance
    JEL: D14 D84 G02 G11
    Date: 2022–01
  5. By: Ralph S. J. Koijen; Motohiro Yogo
    Abstract: Insurers are the largest institutional investors of corporate bonds. However, a standard theory of insurance markets, in which insurers maximize firm value subject to regulatory or risk constraints, predicts no allocation to corporate bonds. We resolve this puzzle in an equilibrium asset pricing model with leverage-constrained households and institutional investors. Insurers have relatively cheap access to leverage through their underwriting activity. They hold a leveraged portfolio of low-beta assets in equilibrium, relaxing other investors' leverage constraints. The model explains recent empirical findings on insurers' portfolio choice and its impact on asset prices.
    JEL: G12 G22
    Date: 2022–01
  6. By: Ding, Y.
    Abstract: We propose a new class of conditional heteroskedasticity in the volatility (CHV) models which allows for time-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model of Ding (2021). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then introduce two examples of CH-V in which we specify a GJR-GARCH and an E-GARCH processes for the volatility of volatility, respectively. We also show a novel way of introducing the leverage effect of negative returns on the volatility through the volatility of volatility process. Empirical study confirms that CH-V models have better goodness-of-fit and out-of-sample volatility and Value-at-Risk forecasts than common GARCH-type models.
    Keywords: forecasting, GARCH, SHARV, volatility, volatility of volatility
    JEL: C22 C32 C53 C58 G17
    Date: 2021–11–09
  7. By: Dorian Henricot; Thibaut Piquard
    Abstract: We use data on granular holdings of debt and Credit Default Swaps (CDS) referencing non-financial corporations across financial investors, to investigate how CDS reallocate credit risk and whether this increases investor-level riskiness. To guide our investigation, we propose a methodology to disentangle CDS positions between three strategies: hedging, speculation, and arbitrage. In our dataset, arbitrage remains anecdotal. We find that CDS reduce exposure concentration, as hedgers shed off their most concentrated exposures, while speculators substitute debt for CDS. CDS also facilitate risk-taking by speculators. Overall, CDS increase portfolio risk metrics, due to a limited effect of hedging strategies compared to speculative ones.
    Keywords: Credit Default Swaps, Credit Risk
    JEL: E44 G11 G20 G23
    Date: 2022
  8. By: Kelli Francis-Staite
    Abstract: This paper describes multi-portfolio `internal' rebalancing processes used in the finance industry. Instead of trading with the market to `externally' rebalance, these internal processes detail how portfolio managers buy and sell between their portfolios to rebalance. We give an overview of currently used internal rebalancing processes, including one known as the `banker' process and another known as the `linear' process. We prove the banker process disadvantages the nominated banker portfolio in volatile markets, while the linear process may advantage or disadvantage portfolios. We describe an alternative process that uses the concept of `market-invariance'. We give analytic solutions for small cases, while in general show that the $n$-portfolio solution and its corresponding `market-invariant' algorithm solve a system of nonlinear polynomial equations. It turns out this algorithm is a rediscovery of the RAS algorithm (also called the `iterative proportional fitting procedure') for biproportional matrices. We show that this process is more equitable than the banker and linear processes, and demonstrate this with empirical results. The market-invariant process has already been implemented by industry due to the significance of these results.
    Date: 2022–01
  9. By: Riccardo Poli (Bank of Italy); Marco Taboga (Bank of Italy)
    Abstract: We propose a methodology to build and validate a composite indicator of the market liquidity of euro-area sovereign bonds. The indicator aggregates several metrics from different trading venues, with the aim of providing a comprehensive measurement of prevailing bond-market liquidity conditions in the four largest euro-area economies (Germany, France, Italy and Spain). The composite indicator, which starts in 2010, allows us to put into historical context the sharp liquidity deterioration experienced at the height of the COVID-19 crisis. The deterioration was comparable to, although slightly less severe than, that experienced during the European sovereign debt crisis. However, while at the time the impairment in liquidity conditions had lasted for more than two years, this time it was quickly re-absorbed. We provide some evidence that the promptness and boldness of the ECB’s interventions in 2020 could help to explain this difference: according to our indicator, the announcements of the Pandemic Emergency Purchase Programme and other policy measures having an explicit market stabilization function were immediately followed by significant improvements in the liquidity of sovereign bonds.
    Keywords: market liquidity, sovereign bonds, market microstructure, Covid-19
    JEL: G12
    Date: 2021–12
  10. By: Mustafa Hakan Eratalay; Ariana Paola Cortés à ngel
    Abstract: There are diverging results in the literature on whether engaging in ESG related activities increases or decreases the financial and systemic risks of firms. In this paper we explore whether maintaining higher ESG ratings would reduce the systemic risks of firms in a stock market context. For this purpose we analyse the systemic risk indicators of the constituent stocks of S&P Europe 350 for the period of January 2016 - September 2020, which also partly covers the Covid-19 period. We apply a VAR-MGARCH model to extract the volatilities and correlations of the return shocks of these stocks. Then we obtain the systemic risk indicators by applying a principle components approach to the estimated volatilities and correlations. Our focus is on the impact of ESG ratings on systemic risk indicators, while we consider network centralities, volatilities and financial performance ratios as control variables. We use fixed effects and OLS methods for our regressions. Our results indicate that (1) the volatility of a stock’s returns and its centrality measures in the stock network are the main sources contributing to the systemic risk measure (2) firms with higher ESG ratings face up to 7.3% less systemic risk contribution and exposure compared to firms with lower ESG ratings, (3) Covid-19 augmented the partial effects of volatility, centrality measures and some financial performance ratios. When considering only the Covid-19 period, we found that social and governance factors have statistically significant impacts on systemic risk.
    Keywords: systemic risk, network centrality, sustainable, ESG, volatility, principal components, Covid-19
    Date: 2022
  11. By: Karel Janda; Ladislav Kristoufek; Binyi Zhang
    Abstract: Objective of this paper is to empirically investigate the dynamic connectedness between oil prices and stock returns of clean energy related and technology companies in China and U.S. financial markets. Three different multivariate Generalised Autoregression Conditional Heteroscedasticity (VAR-MGARCH) model specifications are used to investigate the return and volatility spillovers among series. By comparing these three models, we find that the VAR(1)-DCC(1,1) model with the skewed Student t distribution fits the data the best. The results of DCC estimation reveal that, on average, a $1 long position in Chinese clean energy companies in the Chinese financial market can be hedged for 18 cents with a short position in clean energy index in the U.S market. Our empirical findings provide investors and policymakers with the systematic understanding of spillover effects between China and U.S. clean energy stock markets.
    Keywords: Clean energy, Oil, Technology, Stock prices, VAR-MGARCH
    JEL: G11 Q20
    Date: 2021–11–16

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