nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒03‒06
ten papers chosen by

  1. Risk Budgeting Portfolios from Simulations By Bernardo Freitas Paulo da Costa; Silvana M. Pesenti; Rodrigo S. Targino
  2. Volatility modeling of property markets: A note on the distribution of GARCH innovation By Karl-Friedrich Keunecke; Cay Oertel
  3. Modeling and Simulation of Financial Returns under Non-Gaussian Distributions By Federica De Domenico; Giacomo Livan; Guido Montagna; Oreste Nicrosini
  4. f-Betas and Portfolio Optimization with f-Divergence induced Risk Measures By Rui Ding
  5. Data-driven Approach for Static Hedging of Exchange Traded Options By Vikranth Lokeshwar Dhandapani; Shashi Jain
  6. Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data By Paul Labonne
  7. Dividend predictability and higher moment risk premia By Al-Jaaf, Asty
  8. Aggregating heavy-tailed random vectors: from finite sums to L\'evy processes By Bikramjit Das; Vicky Fasen-Hartmann
  9. Bitcoin Mining Meets Wall Street: A Study of Publicly Traded Crypto Mining Companies By Hanna Halaburda; David Yermack
  10. Do pension funds reach for yield? Evidence from a new database By Maximilian Konradt

  1. By: Bernardo Freitas Paulo da Costa; Silvana M. Pesenti; Rodrigo S. Targino
    Abstract: Risk budgeting is a portfolio strategy where each asset contributes a prespecified amount to the aggregate risk of the portfolio. In this work, we propose an efficient numerical framework that uses only simulations of returns for estimating risk budgeting portfolios. Besides a general cutting planes algorithm for determining the weights of risk budgeting portfolios for arbitrary coherent distortion risk measures, we provide a specialised version for the Expected Shortfall, and a tailored Stochastic Gradient Descent (SGD) algorithm, also for the Expected Shortfall. We compare our algorithm to standard convex optimisation solvers and illustrate different risk budgeting portfolios, constructed using an especially designed Julia package, on real financial data and compare it to classical portfolio strategies.
    Date: 2023–02
  2. By: Karl-Friedrich Keunecke; Cay Oertel
    Abstract: Autoregressive heteroscedastic effects in financial time series have been subject to a broad field of applied econometrics. Both academic research, as well as the industry, apply GARCH processes to real estate data with previous investigation mostly focused on securitized real estate positions. So far, the common approach in the literature has been to assume normal distribution of the innovation term for the GARCH modelling of direct real estate markets (Miles, 2008). The specified assumption of normality however falls short of the data characteristics exhibited by direct real estate markets, such as returns of real estate prices explicitly not normally distributed and better characterized by a more leptokurtic, skewed distribution (Schindler, 2009). Ghahramani and Thavaneswaran (2007) point out that typically the innovation distribution is selected without further justification (also see Pin-te & Fuest (2014) footnote for a simple switch to student-t without further justification). Consequently, the omission of a priori assumptions about the innovation term distributions being fit to direct real estate leading to misspecification and -parameterization of GARCH models is the research aim of this study. The employed analysis will utilize monthly transaction-based data for ten US property market subsets, whilst observing a window of time to encompass different market conditions and volatility regimes (Perlin et al., 2021). Determining how ARCH effects might differ across different US real estate submarkets as well as major and non-major markets builds on and extends previous research focused on geographical disaggregation (see Crawford and Fratantoni, 2003; Dolde and Tirtioglu, 1997; Miles, 2008; Schindler, 2009). Subsequently fitting and estimating each data subset with a conditionally normally distributed GARCH model will be juxtaposed by employing a variety of innovation distributions to the data. It follows the central hypothesis of this paper, that the goodness of fit for GARCH models can be improved by allowing for the conditional distribution to be modeled as a flexible a priori assumption. Investigating the differing goodness of fit for the models and employing the most appropriate models to re-estimate the GARCH parameters will allow an analysis of the differences in volatility clustering effects to the model employing normally distributed innovations. The aim is to show empirically, that non-normal innovation term distribution leads to a potentially better goodness of fit of the GARCH model. The utilization of a priori assumptions of GARCH model specification is of high importance not only for portfolio management of investors, but also risk management for economic institutions such as central banks and mortgage banks (Schindler, 2009). To the best of the authors’ knowledge, there is no study which scientifically examines the innovation term distribution of GARCH models of direct real estate investments. This paper aims to provide a better understanding of the influence a priori assumptions of the innovation term can take to increase the validity of volatility models for direct real estate investments.
    Keywords: Capital Values; GARCH; Innovation term distribution; Volatiltiy modeling
    JEL: R3
    Date: 2022–01–01
  3. By: Federica De Domenico; Giacomo Livan; Guido Montagna; Oreste Nicrosini
    Abstract: It is well known that the probability distribution of high-frequency financial returns is characterized by a leptokurtic, heavy-tailed shape. This behavior undermines the typical assumption of Gaussian log-returns behind the standard approach to risk management and option pricing. Yet, there is no consensus on what class of probability distributions should be adopted to describe financial returns and different models used in the literature have demonstrated, to varying extent, an ability to reproduce empirically observed stylized facts. In order to provide some clarity, in this paper we perform a thorough study of the most popular models of return distributions as obtained in the empirical analyses of high-frequency financial data. We compare the statistical properties and simulate the dynamics of non-Gaussian financial fluctuations by means of Monte Carlo sampling from the different models in terms of realistic tail exponents. Our findings show a noticeable consistency between the considered return distributions in the modeling of the scaling properties of large price changes. We also discuss the convergence rate to the asymptotic distributions of the non-Gaussian stochastic processes and we study, as a first example of possible applications, the impact of our results on option pricing in comparison with the standard Black and Scholes approach.
    Date: 2023–02
  4. By: Rui Ding
    Abstract: In this paper, we build on using the class of f-divergence induced coherent risk measures for portfolio optimization and derive its necessary optimality conditions formulated in CAPM format. We have derived a new f-Beta similar to the Standard Betas and previous works in Drawdown Betas. The f-Beta evaluates portfolio performance under an optimally perturbed market probability measure and this family of Beta metrics gives various degrees of flexibility and interpretability. We conducted numerical experiments using DOW 30 stocks against a chosen market portfolio as the optimal portfolio to demonstrate the new perspectives provided by Hellinger-Beta as compared with Standard Beta and Drawdown Betas, based on choosing square Hellinger distance to be the particular choice of f-divergence function in the general f-divergence induced risk measures and f-Betas. We calculated Hellinger-Beta metrics based on deviation measures and further extended this approach to calculate Hellinger-Betas based on drawdown measures, resulting in another new metric which we termed Hellinger-Drawdown Beta. We compared the resulting Hellinger-Beta values under various choices of the risk aversion parameter to study their sensitivity to increasing stress levels.
    Date: 2023–02
  5. By: Vikranth Lokeshwar Dhandapani; Shashi Jain
    Abstract: In this paper, we present a data-driven explainable machine learning algorithm for semi-static hedging of Exchange Traded options taking into account transaction costs with efficient run-time. Further, we also provide empirical evidence on the performance of hedging longer-term National Stock Exchange (NSE) Index options using a self-replicating portfolio of shorter-term options and cash position, achieved by the automated algorithm, under different modeling assumptions and market conditions including covid stressed period. We also systematically assess the performance of the model using the Superior Predictive Ability (SPA) test by benchmarking against the static hedge proposed by Peter Carr and Liuren Wu and industry-standard dynamic hedging. We finally perform a Profit and Loss (PnL) attribution analysis for the option to be hedged, delta hedge, and static hedge portfolio to identify the factors that explain the performance of static hedging.
    Date: 2023–02
  6. By: Paul Labonne
    Abstract: This paper presents a new way to account for downside and upside risks when producing density nowcasts of GDP growth. The approach relies on modelling location, scale and shape common factors in real-time macroeconomic data. While movements in the location generate shifts in the central part of the predictive density, the scale controls its dispersion (akin to general uncertainty) and the shape its asymmetry, or skewness (akin to downside and upside risks). The empirical application is centred on US GDP growth and the real-time data come from Fred-MD. The results show that there is more to real-time data than their levels or means: their dispersion and asymmetry provide valuable information for nowcasting economic activity. Scale and shape common factors (i) yield more reliable measures of uncertainty and (ii) improve precision when macroeconomic uncertainty is at its peak.
    Keywords: density nowcasting, downside risk, fred-md, nowcasting uncertainty, score driven models
    JEL: C32 C53 E66
    Date: 2022–10
  7. By: Al-Jaaf, Asty
    Date: 2022–03
  8. By: Bikramjit Das; Vicky Fasen-Hartmann
    Abstract: The tail behavior of aggregates of heavy-tailed random vectors is known to be determined by the so-called principle of "one large jump'', be it for finite sums, random sums, or, L\'evy processes. We establish that, in fact, a more general principle is at play. Assuming that the random vectors are multivariate regularly varying on various subcones of the positive quadrant, first we show that their aggregates are also multivariate regularly varying on these subcones. This allows us to approximate certain tail probabilities which were rendered asymptotically negligible under classical regular variation, despite the "one large jump'' asymptotics. We also discover that depending on the structure of the tail event of concern, the tail behavior of the aggregates may be characterized by more than a single large jump. Eventually, we illustrate a similar phenomenon for multivariate regularly varying L\'evy processes, establishing as well a relationship between multivariate regular variation of a L\'evy process and multivariate regular variation of its L\'evy measure on different subcones.
    Date: 2023–01
  9. By: Hanna Halaburda; David Yermack
    Abstract: This paper studies the operations and financial valuations of 13 cryptocurrency mining companies that are listed on the NASDAQ stock exchange and have facilities in North America. We find that miners using Texas wind power are offline more than other miners, in a more erratic pattern, while receiving significant revenue augmentations from “curtailment” payments by electric utilities. Despite having relatively low activity levels, these Texas miners are more profitable than those using more stable sources of energy such as hyrdo power or solar power, as reflected in significantly higher enterprise values. We find a negative and significant beta between crypto mining stocks and an index of electric utilities, suggesting that ownership of a crypto mining company might provide a useful channel for risk management in the electric power industry.
    JEL: G23 L23 L94
    Date: 2023–02
  10. By: Maximilian Konradt (IHEID, Graduate Institute of International and Development Studies, Geneva)
    Abstract: This paper investigates the financial risk-taking behavior of pension funds since 2000. I assemble a new database containing portfolio holdings of more than 100 pension funds from 14 advanced economies. The study reveals three key findings. First, I show that pension fund portfolios have become riskier over that period, with an average increase in risky asset weights of 4 percentage points since 2008. European pension funds tend to invest more in public equities while North American and Asian funds focus on alternative assets. Second, I find evidence that declining domestic risk-free rates play a significant role in driving the trend, with pension funds increasing their risky asset exposure in response to falling short-term interest rates. Third, I demonstrate that less underfunded pension funds with fewer risky assets tend to reach for yield more aggressively, which is exacerbated during periods of low risk-free rates. This is most pronounced for European pension funds, particularly after the global financial crisis.
    Keywords: Low interest rates; Pension funds; Risk-taking; Reach for yield
    JEL: E43 F21 G11 G23
    Date: 2023–02–01

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