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on Risk Management |
| By: | Fabien Le Floc'h |
| Abstract: | The measures of roughness of the volatility in the litterature are based on the realized volatility of high frequency data. Some authors show that this leads to a biased estimate, and does not necessarily indicate roughness of the underlying volatility process. Here, we attempt to measure the roughness of the implied volatility of short term options, and of the VIX index, which may be more appropriate proxies of the underlying instant volatility. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.04930&r= |
| By: | Kara Karpman (Department of Statistics and Data Science, Cornell University); Samriddha Lahiry (Department of Statistics and Data Science, Cornell University); Diganta Mukherjee (Sampling and Official Statistics Unit, Indian Statistical Institute Kolkata); Sumanta Basu (Department of Statistics and Data Science, Cornell University) |
| Abstract: | In the post-crisis era, financial regulators and policymakers are increasingly interested in data-driven tools to measure systemic risk and to identify systemically important firms. Granger Causality (GC) based techniques to build networks among financial firms using time series of their stock returns have received significant attention in recent years. Existing GC network methods model conditional means, and do not distinguish between connectivity in lower and upper tails of the return distribution - an aspect crucial for systemic risk analysis. We propose statistical methods that measure connectivity in the financial sector using system-wide tail-based analysis and is able to distinguish between connectivity in lower and upper tails of the return distribution. This is achieved using bivariate and multivariate GC analysis based on regular and Lasso penalized quantile regressions, an approach we call quantile Granger causality (QGC). By considering centrality measures of these financial networks, we can assess the build-up of systemic risk and identify risk propagation channels. We provide an asymptotic theory of QGC estimators under a quantile vector autoregressive model, and show its benefit over regular GC analysis on simulated data. We apply our method to the monthly stock returns of large U.S. firms and demonstrate that lower tail based networks can detect systemically risky periods in historical data with higher accuracy than mean-based networks. In a similar analysis of large Indian banks, we find that upper and lower tail networks convey different information and have the potential to distinguish between periods of high connectivity that are governed by positive vs negative news in the market. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.10705&r= |
| By: | Fantazzini, Dean |
| Abstract: | This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice, alternative forecasting models, ranging from credit scoring models to machine learning and time series-based models, and different forecasting horizons. We found that the choice of the coin death definition affected the set of the best forecasting models to compute the probability of death. However, this choice was not critical, and the best models turned out to be the same in most cases. In general, we found that the \textit{cauchit} and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit scoring models and machine learning methods using lagged trading volumes and online searches were better choices for older coins. These results also held after a set of robustness checks that considered different time samples and the coins' market capitalization. |
| Keywords: | Bitcoin, Crypto-assets, Crypto-currencies, Credit risk, Default Probability, Probability of Death, ZPP, Cauchit, Logit, Probit, Random Forests, Google Trends. |
| JEL: | C32 C35 C51 C53 C58 G12 G17 G32 G33 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113744&r= |
| By: | Griffith, Andrew P.; Boyer, Christopher N.; Kane, Ian |
| Abstract: | Environmental and social sustainability have historically been the focus of beef sustainability research and are probably the most familiar among the public. However, there is considerable need for research related to economic sustainability in the beef cattle industry. Economic sustainability is commonly understood to be a farm’s capability to survive or to be economically viable in over time, and a key component is access to and using effective tools and strategies to reduce losses. Cattle producers manage many forms of risk including price risk (Hart, Babcock, and Hayes, 2001). Researchers have investigated the effectiveness of various ways to mitigate price risk (Burdine and Halich, 2014; Hall et al., 2003; Hill, 2015; Williams et al., 2014), but producers have been reluctant to adopt these management tools (Hill, 2015). The events occurring in 2019 (Finney County Tyson Foods slaughterhouse fire) and 2020 (COVID-19) strengthen the argument that managing price risk is vital for long-term economic sustainability for beef cattle producers. Providing stocker and cow-calf producers with information on how to utilize price risk management tools would benefit these producers in making economically sustainable decisions and allowing them to endure and continue operating during and following economic shocks. Therefore, the specific objectives of this literature review were to: 1. Determine the positive attributes of currently available price risk management tools for beef cattle including futures contracts, options and livestock risk protection insurance; and 2. Determine the attributes of currently available price risk management tools that lead to non-use or fail to mitigate risk. The goal of this literature review is to provide a comprehensive summary of research on risk management tools for beef cattle producers and help guide continuing education to beef cattle producers as well as inform policy makers and private industry on ways to improve price risk management to enhance economic sustainability for beef cattle producers. |
| Keywords: | Farm Management, Marketing, Risk and Uncertainty |
| Date: | 2022–07–27 |
| URL: | http://d.repec.org/n?u=RePEc:ags:utaeer:322767&r= |
| By: | Bhaskarjit Sarmah; Nayana Nair; Dhagash Mehta; Stefano Pasquali |
| Abstract: | Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and the weighted links between pairs of nodes representing the corresponding pair-wise correlation coefficients. The traditional network science techniques, which are extensively utilized in financial literature, require handcrafted features such as centrality measures to understand such correlation networks. However, manually enlisting all such handcrafted features may quickly turn out to be a daunting task. Instead, we propose a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec. In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other. By using log returns of S&P 500 stock data, we show that our proposed algorithm can learn such an embedding from its correlation network. We define various domain specific quantitative (and objective) and qualitative metrics that are inspired by metrics used in the field of Natural Language Processing (NLP) to evaluate the embeddings in order to identify the optimal one. Further, we discuss various applications of the embeddings in investment management. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.07183&r= |
| By: | Alexander Barzykin; Philippe Bergault; Olivier Gu\'eant |
| Abstract: | In FX cash markets, market makers provide liquidity to clients for a wide variety of currency pairs. Because of flow uncertainty and market volatility, they face inventory risk. To mitigate this risk, they typically skew their prices to attract or divert the flow and trade with their peers on the dealer-to-dealer segment of the market for hedging purposes. This paper offers a mathematical framework to FX dealers willing to maximize their expected profit while controlling their inventory risk. Approximation techniques are proposed which make the framework scalable to any number of currency pairs. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.04100&r= |
| By: | Griffith, Andrew P.; Boyer, Christopher N.; Kane, Ian |
| Abstract: | Sustainable beef production is categorized into environmental stewardship, economic opportunity and social diligence across the beef value chain. However, cattle producers must be able to benefit from the economic opportunity in order to adopt the environmental and social components. Economic sustainability is commonly understood to be a farm’s capability to survive or to be economically viable over time. Making profitable short-run decisions is key to surviving long-term (Griffith and Boyer, 2020). A key component in economic sustainability is having access to and using effective tools and strategies to reduce economic losses. Cattle producers must manage many forms of risk (e.g. production, financial, technological, legal, casualty, policy), but all sources of risk have been relatively small compared to price risk (Hart, Babcock, and Hayes, 2001). Providing stocker and cow-calf producers with information on how to utilize price risk management tools would benefit these producers in making economically sustainable decisions and allowing them to endure and continue operating during and following economic shocks. However, it is also important to gain the cattle producer’s viewpoint on price risk management tools. Therefore, the specific objectives of the focus groups were to: 1. Determine the attributes of currently available price risk management tools that lead to non-use or fail to mitigate risk; and 2. Provide discussion from producers about ways to improve risk management tools and strategies for cow-calf and stocker producers. The goal of this effort is to help guide continuing education to beef cattle producers as well as inform policy makers and private industry on ways to improve price risk management to enhance economic sustainability for beef cattle producers. |
| Keywords: | Farm Management, Marketing, Risk and Uncertainty |
| Date: | 2022–07–27 |
| URL: | http://d.repec.org/n?u=RePEc:ags:utaeer:322768&r= |
| By: | Giulia Peveri (University of Milan) |
| Abstract: | Chronic diseases, de |
| Date: | 2022–07–03 |
| URL: | http://d.repec.org/n?u=RePEc:boc:isug22:11&r= |
| By: | Vassilis Polimenis |
| Abstract: | The Regression Tree (RT) sorts the samples using a specific feature and finds the split point that produces the maximum variance reduction from a node to its children. Our key observation is that the best factor to use (in terms of MSE drop) is always the target itself, as this most clearly separates the target. Thus using the target as the splitting factor provides an upper bound on MSE drop (or lower bound on the residual children MSE). Based on this observation, we define the k-bit lepto-variance ${\lambda}k^2$ of a target variable (or equivalently the lepto-variance at a specific depth k) as the variance that cannot be removed by any regression tree of a depth equal to k. As the upper bound performance for any feature, we believe ${\lambda}k^2$ to be an interesting statistical concept related to the underlying structure of the sample as it quantifies the resolving power of the RT for the sample. The max variance that may be explained using RTs of depth up to k is called the sample k-bit macro-variance. At any depth, total sample variance is thus decomposed into lepto-variance ${\lambda}^2$ and macro-variance ${\mu}^2$. We demonstrate the concept, by performing 1- and 2-bit RT based lepto-structure analysis for daily IBM stock returns. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.04867&r= |
| By: | Worley, Julian M.; Dorfman, Jeffrey H. |
| Keywords: | Agribusiness, Risk and Uncertainty, Marketing |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea22:322091&r= |
| By: | Clements, Adam; Vasnev, Andrey |
| Abstract: | The Heterogeneous Autoregressive (HAR) model of Corsi (2009) has become the benchmark model for predicting realized volatility given its simplicity and consistent empirical performance. Many modifications and extensions to the original model have been proposed that often only provide incremental forecast improvements. In this paper, we take a step back and view the HAR model as a forecast combination that combines three predictors: previous day realization (or random walk forecast), previous week average, and previous month average. When applying the Ordinary Least Squares (OLS) to combine the predictors, the HAR model uses optimal weights that are known to be problematic in the forecast combination literature. In fact, the simple average forecast often outperforms the optimal combination in many empirical applications. We investigate the performance of the simple average forecast for the realized volatility of the Dow Jones Industrial Average equity index. We find dramatic improvements in forecast accuracy across all horizons and different time periods. This is the first time the forecast combination puzzle is identified in this context. |
| Keywords: | Realized volatility, forecast combination, HAR model |
| JEL: | C53 C58 |
| Date: | 2021–02–24 |
| URL: | http://d.repec.org/n?u=RePEc:syb:wpbsba:2123/25045&r= |
| By: | Jun Lu; Shao Yi |
| Abstract: | Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN) and conditional GAN (CGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN framework stands in putting too much emphasis on generating series rather than keeping features that can help this generator. In this paper, we introduce an autoencoding CGAN (ACGAN) based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed ACGAN model leads to better portfolio allocation and generates series that are closer to true data compared to the existing Markowitz and CGAN approaches. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.05701&r= |
| By: | Araba, Narjiss |
| Keywords: | Crop Production/Industries, Demand and Price Analysis |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aesc22:321209&r= |
| By: | Muriuki, James M.; Badruddoza, Syed; Fuad, Syed M. |
| Keywords: | Risk and Uncertainty, Institutional and Behavioral Economics, Agribusiness |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea22:322533&r= |
| By: | Ozili, Peterson K |
| Abstract: | This article discusses the need for climate change risk mitigation and why it is not the responsibility of Central Banks to mitigate climate change risk. The paper argues that the responsibility for managing climate change risk should lie with elected officials, other groups and institutions but not Central Banks. Elected officials, or politicians, should be held responsible to deal with the consequence of climate change events. Also, international organizations and everybody can take responsibility for climate change while the Central Bank can provide assistance - but Central Banks should not lead the climate policy making or mitigation agenda. |
| Keywords: | Climate change, environment, Central Bank, government, atmosphere, financial stability, risk management, climate change risk, financial sector, responsibility, financial institutions. |
| JEL: | G28 Q54 Q56 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113468&r= |
| By: | Andrea Giacomelli (Department of Economics, University Of Venice CÃ Foscari; KnowShape, Italy) |
| Abstract: | Forward-looking information is taking on an increasingly important role in firms’ decision-making processes, in communicating with stakeholders and in improving market information. Its relevance is also increased due to recent structural breaks, namely the Covid-19 pandemic and the Ukrainian war, which make historical data uninformative. In the last period forward-looking information has been the object of different international regulations for firms and financial institutions and its scope has expanded from financial performance to ESG sustainability performance. Despite its relevance, forward-looking information is still a confusing topic, in terms of contents and applications, especially in the context of ESG, where there is a need for greater clarity and standardization of the definitions of the ESG indicators. To address these issues, this paper introduces two contributions. The first one is an analytical definition of idiosyncratic forward-looking information, called Mark to Target Information (MtTI), which is suitable for representing both the financial and the ESG sustainability performance. MtTI refers directly to firms’ plans and their risk indicators, thus differing from forecasts and systematic forward-looking information (based only on macroeconomic or sector scenarios). The second contribution is the introduction of formal criteria for identifying, within the general definition of MtTI, a list of primary ESG indicators which must be considered for the establishment of ESG information standards. These indicators are called “MtTI-based ESG indicators†and they refer directly to a firm's ESG sustainability plan and its risk indicators. |
| Keywords: | Forward looking information, idiosyncratic information, enterprise risk management, expert judgment, ESG information, ESG risk, ESG sustainability disclosure, ESG transition plan |
| JEL: | D81 D82 G32 K32 Q01 Q51 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2022:08&r= |