|
on Risk Management |
| By: | Tong Pu; Yunran Wei; Yiying Zhang |
| Abstract: | We introduce a novel class of systemic risk measures, the Vulnerability Conditional risk measures, which try to capture the "tail risk" of a risky position in scenarios where one or more market participants is experiencing financial distress. Various theoretical properties of Vulnerability Conditional risk measures, along with a series of related contribution measures, have been considered in this paper. We further introduce the backtesting procedures of VCoES and MCoES. Through numerical examples, we validate our theoretical insights and further apply our newly proposed risk measures to the empirical analysis of cryptocurrencies, demonstrating their practical relevance and utility in capturing systemic risk. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09676 |
| By: | Albrecher, Hansjörg; Dacorogna, Michel M |
| Abstract: | Assessing time-related risks in long-tailed insurance is challenging. Regulatory capital allocation rules may underestimate credit deterioration risk by not requiring insurers to hold solvency capital early, while actuarial practices often allocate capital sooner than mandated. We propose a framework to quantify these time-associated risks and evaluate capital allocation strategies based on time to ultimate, aiming to manage long-tail business effectively. By modeling the impact of exogenous credit migration risk, we evaluate six strategies, including costs associated with potential company bankruptcy until long-term claims are settled. Using a numerical example of a future heavy-tailed insurance risk, we estimate a Markov chain credit migration model with insurance market data and analyze liability values from various capital management strategies. Our findings show that early capital raising is costly, even with penalties for avoided credit risk, unless the company's initial credit rating is poor. In such cases, purchasing protection through a credit derivative may be more efficient, if available. |
| Keywords: | Insurance, solvency capital requirement, credit risk, bankruptcy, regulation |
| JEL: | G22 G28 G32 |
| Date: | 2024–10–02 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122323 |
| By: | Christian Laudag\'e; Felix-Benedikt Liebrich; J\"orn Sass |
| Abstract: | We revisit the recently introduced concept of return risk measures (RRMs). We extend it by allowing risk management via multiple so-called eligible assets. The resulting new risk measures are called multi-asset return risk measures (MARRMs). We analyze properties of these risk measures. In particular, we prove that a positively homogeneous MARRM is quasi-convex if and only if it is convex. Furthermore, we state conditions to avoid inconsistent risk evaluations. Then, we point out the connection between MARRMs and the well-known concept of multi-asset risk measures (MARMs). This is used to obtain various dual representations of MARRMs. Moreover, we compare RRMs, MARMs, and MARRMs in numerous case studies. First, using typical continuous-time financial markets and different notions of acceptability of losses, we compare MARRMs and MARMs and draw conclusions about the cost of risk mitigation. Second, in a real-world example, we compare the relative difference between RRMs and MARRMs in times of crisis. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08763 |
| By: | Haoyu Chen; Tiantian Mao; Fan Yang |
| Abstract: | In this paper, we modify the Bayes risk for the expectile, the so-called variantile risk measure, to better capture extreme risks. The modified risk measure is called the adjusted standard-deviatile. First, we derive the asymptotic expansions of the adjusted standard-deviatile. Next, based on the first-order asymptotic expansion, we propose two efficient estimation methods for the adjusted standard-deviatile at intermediate and extreme levels. By using techniques from extreme value theory, the asymptotic normality is proved for both estimators. Simulations and real data applications are conducted to examine the performance of the proposed estimators. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.07203 |
| By: | Chang, Kuo-Ping |
| Abstract: | Risk can be defined as the likelihood that you can deliver your promise. This paper has used the European put option and the European call option to construct the p-index and c-index to measure the risk levels (likelihoods) of owning or short-selling an asset when the asset provides at least � rate of return. The p-index measures the insurance fees for each insured dollar so that the asset can deliver at least � rate of return. The c-index measures the insurance fees for each dollar of the insurance deductible if the asset delivers at least � rate of return. It shows that higher p-index means higher c-index. In the binomial case with up move and down move, (1) assets having lower down move have higher p-index, i.e., higher risk for owning the assets; and (2) assets having higher up move have higher c-index, i.e., higher risk for shortselling the assets. The trinomial example however shows that the rankings of risk levels of assets' providing different rates of returns could reverse. |
| Keywords: | The put-call parity, the p-index, the c-index, risk structures of assets. |
| JEL: | D81 G13 G32 |
| Date: | 2023–02–12 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122653 |
| By: | Anika Tahsin Meem; Mst. Shapna Akter; Deponker Sarker Depto; M. R. C. Mahdy |
| Abstract: | In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market. Risk factor is also called volatility. Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience. Our approach starts with calculating the risk factor of the cryptocurrency market from the existing parameters. In twenty elements of the cryptocurrency market, the risk factor has been predicted using different machine learning algorithms such as CNN, LSTM, BiLSTM, and GRU. All of the models have been applied to the calculated risk factor parameter. A new model has been developed to predict better than the existing models. Our proposed model gives the highest RMSE value of 1.3229 and the lowest RMSE value of 0.0089. Following our model, it will be easier for investors to trade in complicated and challenging financial assets like bitcoin, Ethereum, dogecoin, etc. Where the other existing models, the highest RMSE was 14.5092, and the lower was 0.02769. So, the proposed model performs much better than models with proper generalization. Using our approach, it will be easier for investors to trade in complicated and challenging financial assets like Bitcoin, Ethereum, and Dogecoin. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13615 |
| By: | Jihyun Park; Andrey Sarantsev |
| Abstract: | We apply Principal Component Analysis for zero-coupon Treasury bonds to get level, slope, and curvature series. We model these as autoregressions of order 1, and analyze their innovations. For slope, but not for level and curvature, dividing these innovations by the Volatility Index VIX made for Standard \& Poor 500 makes them closer to independent identically distributed normal. We state and prove stability results for bond returns based on this observation. We chose zero-coupon as opposed to classic coupon Treasury bonds because it is much easier to compute returns for these. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.03699 |
| By: | Aleksandr Simonyan |
| Abstract: | This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-based models for their ability to address the unique challenges posed by highly volatile financial markets. The primary contribution of this work lies in demonstrating the effectiveness of combining time series representation learning with LLM prediction frameworks. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06076 |
| By: | Graham L. Giller |
| Abstract: | In this short note the theory for multivariate asset allocation with elliptically symmetric distributions of returns, as developed in the author's prior work, is specialized to the case of returns drawn from a multivariate Laplace distribution. This analysis delivers a result closely, but not perfectly, consistent with the conjecture presented in the author's article Thinking Differently About Asset Allocation. The principal differences are due to the introduction of a term in the dimensionality of the problem, which was omitted from the conjectured solution, and a rescaling of the variance due to varying parameterizations of the univariate Laplace distribution. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08967 |