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on Risk Management |
| By: | Tomaso Aste |
| Abstract: | The multivariate conditional probability distribution quantifies the effects of a set of variables onto the statistical properties of another set of variables. In the study of systemic risk in the financial system, the multivariate conditional probability distribution can be used for stress-testing by quantifying the propagation of losses from a set of `stressing' variables to another set of `stressed' variables. Here it is described how to compute such conditional probability distributions for the vast family of multivariate elliptical distributions, which includes the multivariate Student-t and the multivariate Normal distributions. Simple measures of stress impact and systemic risk are also proposed. An application to the US equity market illustrates the potentials of this approach. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.06420&r=all |
| By: | Mpoha, Salifya; Bonga-Bonga, Lumengo |
| Abstract: | This paper assesses the extent of exchange rate risk pricing in emerging and developed economies to infer whether this risk is systematic or unsystematic in these economies. The pricing of this risk is based on the two- and three-factor extended CAPM (capital asset pricing model). The US and South Africa are used as proxy for developed and emerging economies, respectively. The findings suggest strong evidence for exchange rate risk premia in both cases and highlight that contrary to many studies, exchange rate risk is systematic in developed economies, despite the possibility and variety of instruments of exchange rate hedging in these economies, particularly in developed economies. |
| Keywords: | Exchange-Rate Exposure, Premia, Arbitrage Pricing Theory, Rolling Window, Emerging, Developed. |
| JEL: | F31 G12 G15 |
| Date: | 2020–04–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:99597&r=all |
| By: | Alois Pichler; Ruben Schlotter |
| Abstract: | This paper enhances the pricing of derivatives as well as optimal control problems to a level comprising risk. We employ nested risk measures to quantify risk, investigate the limiting behavior of nested risk measures within the classical models in finance and characterize existence of the risk-averse limit. As a result we demonstrate that the nested limit is unique, irrespective of the initially chosen risk measure. Within the classical models risk aversion gives rise to a stream of risk premiums, comparable to dividend payments. In this context, we connect coherent risk measures with the Sharpe ratio from modern portfolio theory and extract the Z-spread - a widely accepted quantity in economics to hedge risk. By involving the Z-spread we demonstrate that risk-averse problems are conceptually equivalent to the risk-neutral problem. The results for European option pricing are then extended to risk-averse American options, where we study the impact of risk on the price as well as the optimal time to exercise the option. We also extend Merton's optimal consumption problem to the risk-averse setting. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.04397&r=all |
| By: | Giuseppe Brandi; T. Di Matteo |
| Abstract: | Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems. The construction of these networks is difficult and is mostly done with a static approach, neglecting time delayed interdependences. Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network directly from data. This methodology captures within and between connections across layers and makes use of a filtering procedure to extract relevant information and improve visualization. We show the application of this methodology to different stationary fractionally differenced financial data. We argue that our result is useful to understand the dependencies across three different aspects of financial risk, namely market risk, liquidity risk, and volatility risk. Indeed, we show how the resulting visualization is a useful tool for risk managers depicting dependency asymmetries between different risk factors and accounting for delayed cross dependencies. The constructed multilayer network shows a strong interconnection between the volumes and prices layers across all the stocks considered while a lower number of interconnections between the uncertainty measures is identified. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.05367&r=all |
| By: | Ivan Paya; David Peel; Konstantinos Georgalos |
| Abstract: | This is the first paper to provide a comprehensive theoretical analysis of the third and fourth order lottery preferences implied by cumulative prospect theory (CPT). We consider the lottery choices from three alternative reference points: the status quo, the expected payout and the MaxMin. We report a large number of new results given the standard assumptions about probability weighting. We demonstrate, for example, the general result that from the status quo reference point there is no third order reflection effect but there is a fourth order reflection effect. When the average payout or the MaxMin is the reference point, we lose generality but can demonstrate that representative individuals with power value functions can make prudent or imprudent, temperate or intemperate choices depending on the precise magnitude of lottery payoffs. In addition to this, we show that these representative CPT individuals can exhibit some surprising combinations of second with third and fourth order risk attitudes. Throughout the paper, we contrast our theoretical predictions with results reported in the literature and we are able to reconcile some conflicting evidence on higher order risk preferences. |
| Keywords: | cumulative prospect theory, decision making under risk, experiments, higher order preferences, reflection effect |
| JEL: | D8 E21 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:293574809&r=all |
| By: | Youssef Nassef |
| Abstract: | The PCL framework provides a comprehensive climate risk management approach grounded in the assessment of societal values of financial and non-financial loss tolerability. The framework optimizes response action across three main clusters, namely preemptive adaptation (P) or risk reduction, contingent arrangements (C), and loss acceptance (L); without a predetermined hierarchy across them. The PCL Framework aims at including the three clusters of outlay within a single continuum, and with the main policy outcome being a balanced portfolio of actions across the three clusters by way of an optimization module, such that the aggregate outlay is optimized in the long-term. It is proposed that the approach be applied separately for each hazard to which the target community is exposed. While it is currently applied to climate-related risk management, the methodology can be repurposed for use in other contexts where societal buy-in is central. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.06144&r=all |
| By: | Nassim Nicholas Taleb |
| Abstract: | Empirical distributions have their in-sample maxima as natural censoring. We look at the "hidden tail", that is, the part of the distribution in excess of the maximum for a sample size of $n$. Using extreme value theory, we examine the properties of the hidden tail and calculate its moments of order $p$. The method is useful in showing how large a bias one can expect, for a given $n$, between the visible in-sample mean and the true statistical mean (or higher moments), which is considerable for $\alpha$ close to 1. Among other properties, we note that the "hidden" moment of order $0$, that is, the exceedance probability for power law distributions, follows an exponential distribution and has for expectation $\frac{1}{n}$ regardless of the parametrization of the scale and tail index. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.05894&r=all |
| By: | Xiaoling Tan; Jichang Zhao |
| Abstract: | The stock market of China experienced an abrupt crash in 2015 and evaporated over one third of the market value. Given its associations with fear and fine-resolutions in frequency, the illiquidity of stocks may offer a promising perspective of understanding and even signaling the market crash. In this study, by connecting stocks that mutually explain illiquidity fluctuations, a illiquidity network is established to model the market. It is found that as compared to non-crash days, the market is more densely connected on crash days due to heavier but more homogeneous illiquidity dependencies that facilitate abrupt collapses. Critical socks in the illiquidity network, in particular the ones in sector of finance are targeted for inspection because of their crucial roles in taking over and passing on the losing of illiquidity. The cascading failures of stocks in market crash is profiled as disseminating from small degrees to high degrees that usually locate in the core of the illiquidity network and then back to the periphery. And by counting the days with random failures in previous five days, an early single is implemented to successfully warn more than half crash days, especially those consecutive ones at early phase. Our results would help market practitioners like regulators detect and prevent risk of crash in advance. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.01917&r=all |
| By: | Scott R. Baker; Nicholas Bloom; Steven J. Davis; Kyle J. Kost; Marco C. Sammon; Tasaneeya Viratyosin |
| Abstract: | No previous infectious disease outbreak, including the Spanish Flu, has impacted the stock market as powerfully as the COVID-19 pandemic. We use text-based methods to develop this point with respect to large daily stock market moves back to 1900 and with respect to overall stock market volatility back to 1985. We also argue that policy responses to the COVID-19 pandemic provide the most compelling explanation for its unprecedented stock market imact. |
| JEL: | E44 G12 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26945&r=all |
| By: | Busch, Christopher; Ludwig, Alexander |
| Abstract: | We extend the canonical income process with persistent and transitory risk to shock distributions with left-skewness and excess kurtosis, to which we refer as higherorder risk. We estimate our extended income process by GMM for household data from the United States. We find countercyclical variance and procyclical skewness of persistent shocks. All shock distributions are highly leptokurtic. The existing tax and transfer system reduces dispersion and left-skewness of shocks. We then show that in a standard incomplete-markets life-cycle model, first, higher-order risk has sizable welfare implications, which depend crucially on risk attitudes of households; second, higher-order risk matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, higher-order risk has non-trivial implications for the degree of self-insurance against both transitory and persistent shocks. |
| Keywords: | Labor Income Risk,Business Cycle,GMM Estimation,Skewness,Persistent and Transitory Income Shocks,Risk Attitudes,Life-Cycle Model |
| JEL: | D31 E24 E32 H31 J31 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:274&r=all |
| By: | Laura Alfaro; Anusha Chari; Andrew N. Greenland; Peter K. Schott |
| Abstract: | We show that unanticipated changes in predicted infections during the SARS and COVID-19 pandemics forecast aggregate equity market returns. We model cumulative infections as either exponential or logistic, and re-estimate the parameters of these models each day of the outbreak using information reported up to that day. For each trading day t we compute the change in predicted infections using day t – 1 versus day t – 2 information. Regression results imply that a doubling of such predictions is associated with a 4 to 11 percent decline in aggregate market value. This result implies a decline in returns' volatility as the trajectory of the pandemic becomes clearer. |
| JEL: | E27 F1 G12 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26950&r=all |
| By: | Antoine Bozio (IPP - Institut des politiques publiques, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics); Simon Rabaté (IPP - Institut des politiques publiques, Centraal Planbureau); Audrey Rain (IPP - Institut des politiques publiques); Maxime Tô (IPP - Institut des politiques publiques, UCL - University College of London [London], Institute for Fiscal Studies) |
| Abstract: | A points system, operating at defined yield, makes it possible to rethink how pension systems are managed. Instead of having to make repeated ad hoc changes to the parameters of the system, it is possible to define change rules that other guarantees to future pensioners, as regards not only their entitlements but also the long-term sustainability of the system. In this brief, and based on simulations of a variety of shocks to the pension system, we study what management rules deserve to be chosen. Two rules absolutely must be selected: firstly the growth in the value of the pension point should match the growth in salaries; and secondly converting the points into pension should take into account the life expectancy of each generation (cohort). A third rule that is important for the long term, is the relationship between the rules for index-linking claimed pensions and the amounts of the pensions when they start being claimed. This rule should serve as a guide to managers so that they can steer the system towards an equilibrium that is not based on too low an index-linking of the pensions. Such management implies high institutional autonomy for the system, whereby the managers need to be accountable for the finnancial equilibrium and for the risks to pension revaluation. |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-02516413&r=all |
| By: | Shi, Yun |
| Abstract: | We solve a portfolio selection problem when both expected return, idiosyncratic volatility, and transaction cost are time-varying. Our optimal strategy suggests trading partially toward a dynamic aim portfolio, which is a weighted average of expected future tangency portfolio and is highly influenced by the common fluctuation of idiosyncratic volatility (CIV). When CIV is high, the investor would invest less and trade less frequently to avoid risk and transaction cost. Moreover, the investor trades more closely to the aim portfolio with a more persistent CIV signal. Our strategy outperforms alternative strategies empirically and the benefits mainly come from timing idiosyncratic volatility. |
| Date: | 2020–04–06 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:9kber&r=all |
| By: | Stefano Giglio; Matteo Maggiori; Johannes Stroebel; Stephen Utkus |
| Abstract: | We provide a data-driven analysis of how investor expectations about economic growth and stock market returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic. We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020, around the all-time stock market high, and then again on March 11 and 12, after the stock market had collapsed by over 20%. The average investor turned more pessimistic about the short-run performance of both stock markets and the economy. Investors also perceived higher probability of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investors' expectations about the long run remained largely unchanged, and if anything improved. Disagreement among investors about economic and stock market outcomes also increased substantially. Our analysis is an input in both the design of the ongoing economic policy response and in further advancing economic theories. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.01831&r=all |
| By: | Kirill S. Glavatskiy; Mikhail Prokopenko; Adrian Carro; Paul Ormerod; Michael Harre |
| Abstract: | Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.07571&r=all |
| By: | Ye-Sheen Lim; Denise Gorse |
| Abstract: | In this paper we propose a deep recurrent architecture for the probabilistic modelling of high-frequency market prices, important for the risk management of automated trading systems. Our proposed architecture incorporates probabilistic mixture models into deep recurrent neural networks. The resulting deep mixture models simultaneously address several practical challenges important in the development of automated high-frequency trading strategies that were previously neglected in the literature: 1) probabilistic forecasting of the price movements; 2) single objective prediction of both the direction and size of the price movements. We train our models on high-frequency Bitcoin market data and evaluate them against benchmark models obtained from the literature. We show that our model outperforms the benchmark models in both a metric-based test and in a simulated trading scenario |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.01498&r=all |