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on Risk Management |
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Issue of 2024‒01‒29
ten papers chosen by |
| By: | Peter Christensen |
| Abstract: | Inspired by the activity signature introduced by Todorov and Tauchen (2010), which was used to measure the activity of a semimartingale, this paper introduces the roughness signature function. The paper illustrates how it can be used to determine whether a discretely observed process is generated by a continuous process that is rougher than a Brownian motion, a pure-jump process, or a combination of the two. Further, if a continuous rough process is present, the function gives an estimate of the roughness index. This is done through an extensive simulation study, where we find that the roughness signature function works as expected on rough processes. We further derive some asymptotic properties of this new signature function. The function is applied empirically to three different volatility measures for the S&P500 index. The three measures are realized volatility, the VIX, and the option-extracted volatility estimator of Todorov (2019). The realized volatility and option-extracted volatility show signs of roughness, with the option-extracted volatility appearing smoother than the realized volatility, while the VIX appears to be driven by a continuous martingale with jumps. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02819&r=rmg |
| By: | Jalal Etesami; Ali Habibnia; Negar Kiyavash |
| Abstract: | We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series. This framework employs an information-theoretic measure rooted in a generalized version of Granger-causality, which is applicable to both linear and nonlinear dynamics. Our framework offers advancements in measuring systemic risk and establishes meaningful connections with established econometric models, including vector autoregression and switching models. We evaluate the efficacy of our proposed model through simulation experiments and empirical analysis, reporting promising results in recovering simulated time-varying networks with nonlinear and multivariate structures. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network. We focus on cryptocurrencies' potential systemic risks to financial stability, including spillover effects on other sectors during crises like the COVID-19 pandemic and the Federal Reserve's 2020 emergency response. Our findings reveals significant, previously underrecognized pre-2020 influences of cryptocurrencies on certain financial sectors, highlighting their potential systemic risks and offering a systematic approach in tracking evolving cross-sector interactions within financial networks. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16707&r=rmg |
| By: | Haochun Ma; Davide Prosperino; Alexander Haluszczynski; Christoph R\"ath |
| Abstract: | Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper we present a much more general framework for assessing co-dependencies by identifying and interpreting linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation-causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16185&r=rmg |
| By: | Martin Keller-Ressel (TU Dresden - Technische Universität Dresden = Dresden University of Technology); Martin Larsson (D-MATH - Department of Mathematics [ETH Zurich] - ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]); Sergio Pulido (ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise, LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - INRA - Institut National de la Recherche Agronomique - ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - UEVE - Université d'Évry-Val-d'Essonne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | The goal of this survey article is to explain and elucidate the affine structure of recent models appearing in the rough volatility literature, and show how it leads to exponential-affine transform formulas. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02265210&r=rmg |
| By: | Alessandra Mainini; Enrico Moretto; Daniela Visetti |
| Abstract: | This article extends, in a stochastic setting, previous results in the determination of feasible exchange ratios for merging companies. A first outcome is that shareholders of the companies involved in the merging process face both an upper and a lower bounds for acceptable exchange ratios. Secondly, in order for the improved `bargaining region' to be intelligibly displayed, the diagrammatic approach developed by Kulpa is exploited. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02681&r=rmg |
| By: | Albert Dorador |
| Abstract: | We propose an alternative linearization to the classical Markowitz quadratic portfolio optimization model, based on maximum drawdown. This model, which minimizes maximum portfolio drawdown, is particularly appealing during times of financial distress, like during the COVID-19 pandemic. In addition, we will present a Mixed-Integer Linear Programming variation of our new model that, based on our out-of-sample results and sensitivity analysis, delivers a more profitable and robust solution with a 200 times faster solving time compared to the standard Markowitz quadratic formulation. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02601&r=rmg |
| By: | Bruno Feunou; James Kyeong |
| Abstract: | Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023. |
| Keywords: | Business fluctuations and cycles; Econometric and statistical methods |
| JEL: | C32 C58 E44 G17 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocsan:23-18&r=rmg |
| By: | Liu, Jia; Maheu, John M; Song, Yong |
| Abstract: | Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross-section of state specific means and variances to differ over bull and bear markets. We investigate several empirically realistic specifications that permit feasible estimation even with 100 assets. Our results show that the multivariate framework provides competitive bull and bear regime identification and improves portfolio performance and density prediction compared to several benchmark models including univariate Markov switching models. |
| Keywords: | Markov switching, Multivariate analysis, Investment strategies, Market timing |
| JEL: | C32 C53 C58 G1 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119515&r=rmg |
| By: | Carlo Boselli (Italian National Statistical Institute); Stefano Costa (Italian National Statistical Institute); Marco Rinaldi (Italian National Statistical Institute); Claudio Vicarelli (Italian National Statistical Institute) |
| Abstract: | We present a new indicator of economic-financial solidity (EFSI) of Italian firms, considering profitability, solidity and firm liquidity, all evaluated in terms of their sustainability over time. On the basis of EFSI values, we classify firms in four classes, according to their degree of exposure to income and financial risks: Healty, Fragiles, At-risk, Highly at-risk. This indicator shows that in 2011-2020 a tightening process of economic and financial structure took place in the Italian business system, a trend that surprisingly continued also during the pandemic year. To investigate this, we consider the entry of firms into the Highly at-risk class (“downgrades†) in 2019-20. Through a matching technique, we run two counterfactual exercises, estimating at a sector-firm size level what the downgrade rates would have been during the crisis of 2019-20 had the business system had the same economic-financial structure prevailing in 2011 (i.e. at the eve of 2011-12 crisis) or in 2019 (i.e. the last year of economic growth). By this way, we can evaluate whether, and to what extent, the financial support to firms during 2020 contributed to the resilience of the Italian business system. Our results show that, with respect to pre-Covid year, firm aids limited the negative consequences of the pandemic especially on the smaller firms (those more severely hit by the crisis); with respect the 2011-12 crisis, in several sectors support measures more than fully compensate for the negative effects of the pandemic notwithstanding its stronger economic impact on GDP than the previous crisis episode. |
| Keywords: | Covid-19; Economic-financial solidity; Firm aids; Mahalanobis-metric matching |
| JEL: | G01 H12 H81 H84 L60 L80 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:lui:lleewp:23159&r=rmg |
| By: | Taisuke Nakata; Daisuke Fujii; Takeshi Ojima |
| Abstract: | Many countries have experienced multiple waves of infection during the COVID-19 pandemic. We propose a novel but parsimonious extension of the SIR model, a CSIR model, that can endogenously generate waves. In the model, cautious individuals take appropriate prevention measures against the virus and are not exposed to infection risk. Incautious individuals do not take any measures and are susceptible to the risk of infection. Depending on the size of incautious and susceptible population, some cautious people lower their guard and become incautious--thus susceptible to the virus. When the virus spreads sufficiently, the population reaches ``temporary" herd immunity and infection subsides thereafter. Yet, the inflow from the cautious to the susceptible eventually expands the susceptible population and leads to the next wave. We also show that the CSIR model is isomorphic to the SIR model with time-varying parameters. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:tcr:wpaper:e192&r=rmg |