|
on Risk Management |
|
Issue of 2023‒07‒31
28 papers chosen by |
| By: | Andrea Renzetti |
| Abstract: | In this paper I propose a parametric framework for modelling and forecasting macroeconomic tail risk based on stochastic volatility models with Skew-Normal and Skew-t shocks featuring stochastic skewness. The paper develops posterior simulation samplers for Bayesian estimation of both univariate and VAR models of this type. In an application, I use the models to predict downside risk to GDP growth and I show that this approach represents a competitive alternative to quantile regression. Finally, estimating a medium scale VAR on US data I show that time varying skewness is a relevant feature of macroeconomic and financial shocks. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.09287&r=rmg |
| By: | Marcos Escobar-Anel; Michel Kschonnek; Rudi Zagst |
| Abstract: | We consider a portfolio optimisation problem for a utility-maximising investor who faces convex constraints on his portfolio allocation in Heston's stochastic volatility model. We apply the duality methods developed in previous work to obtain a closed-form expression for the optimal portfolio allocation. In doing so, we observe that allocation constraints impact the optimal constrained portfolio allocation in a fundamentally different way in Heston's stochastic volatility model than in the Black Scholes model. In particular, the optimal constrained portfolio may be different from the naive capped portfolio, which caps off the optimal unconstrained portfolio at the boundaries of the constraints. Despite this difference, we illustrate by way of a numerical analysis that in most realistic scenarios the capped portfolio leads to slim annual wealth equivalent losses compared to the optimal constrained portfolio. During a financial crisis, however, a capped solution might lead to compelling annual wealth equivalent losses. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.11158&r=rmg |
| By: | Alessandro Doldi; Marco Frittelli; Emanuela Rosazza Gianin |
| Abstract: | Shortfall systemic (multivariate) risk measures $\rho$ defined through an $N$-dimensional multivariate utility function $U$ and random allocations can be represented as classical (one dimensional) shortfall risk measures associated to an explicitly determined $1$-dimensional function constructed from $U$. This finding allows for simplifying the study of several properties of $\rho$, such as dual representations, law invariance and stability. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.10752&r=rmg |
| By: | Anton Malandii; Siddhartha Gupte; Cheng Peng; Stan Uryasev |
| Abstract: | This paper introduces a novel framework for assessing risk and decision-making in the presence of uncertainty, the \emph{$\varphi$-Divergence Quadrangle}. This approach expands upon the traditional Risk Quadrangle, a model that quantifies uncertainty through four key components: \emph{risk, deviation, regret}, and \emph{error}. The $\varphi$-Divergence Quadrangle incorporates the $\varphi$-divergence as a measure of the difference between probability distributions, thereby providing a more nuanced understanding of risk. Importantly, the $\varphi$-Divergence Quadrangle is closely connected with the distributionally robust optimization based on the $\varphi$-divergence approach through the duality theory of convex functionals. To illustrate its practicality and versatility, several examples of the $\varphi$-Divergence Quadrangle are provided, including the Quantile Quadrangle. The final portion of the paper outlines a case study implementing regression with the Entropic Value-at-Risk Quadrangle. The proposed $\varphi$-Divergence Quadrangle presents a refined methodology for understanding and managing risk, contributing to the ongoing development of risk assessment and management strategies. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.16525&r=rmg |
| By: | Joel Ong; Dorien Herremans |
| Abstract: | A diversified risk-adjusted time-series momentum (TSMOM) portfolio can deliver substantial abnormal returns and offer some degree of tail risk protection during extreme market events. The performance of existing TSMOM strategies, however, relies not only on the quality of the momentum signal but also on the efficacy of the volatility estimator. Yet many of the existing studies have always considered these two factors to be independent. Inspired by recent progress in Multi-Task Learning (MTL), we present a new approach using MTL in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks related to volatility, such as forecasting realized volatility as measured by different volatility estimators. Through backtesting from January 2000 to December 2020 on a diversified portfolio of continuous futures contracts, we demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies. Moreover, experiments confirm that adding auxiliary tasks indeed boosts the portfolio's performance. These findings demonstrate that MTL can be a powerful tool in finance. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.13661&r=rmg |
| By: | Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier) |
| Abstract: | In this paper, we find analytic expressions of the lower partial moment and kappa index of linear portfolios when the returns are elliptically distributed. We also introduced the notion of Target Semi-Kurtosis of portfolio return and discuss the robust optimization Mean-LPM problem with non- gaussian risk factors. Special attention is given to the particular case of a mixture of multivariate t-distributions with application for portfolio allocation of some ESG indices and the CAC 40 index. |
| Keywords: | Lower partial moment; Kappa Index; Linear Portfolio; Elliptical port- folio, Performance Measure, GO-GARCH, Lower partial moment Kappa Index Linear Portfolio Elliptical portfolio, Performance Measure, GO-GARCH, Lower partial moment, Kappa Index, Linear Portfolio, Elliptical portfolio, Performance Measure, GO-GARCH |
| Date: | 2023–06–20 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-04134833&r=rmg |
| By: | Alexander N. Bogin (Federal Housing Finance Agency); LaRhonda Ealey (Federal Housing Finance Agency); Kirsten Landeryou (Federal Housing Finance Agency); Scott Smith (Federal Housing Finance Agency); Andrew Tsai (Federal Housing Finance Agency) |
| Abstract: | We explore the impact of geographic disaggregation of house price stress paths on single-family credit risk measurement. Specifically, we focus on the value added of moving from national, to state-level, to core-based statistical area (CBSA)-level house price paths on estimates of mortgage credit related stress losses. To ensure the robustness of our results, we estimate losses across two different loan portfolios and three credit models. We find that CBSA-level paths provide additional insight on localized credit risk and can be reliably constructed using quarterly house price indices. Further, the variation in results across credit models suggests an implicit confidence interval around any one stress loss estimate. Accounting for this uncertainty through a model risk add-on could potentially offer a more conservative view of portfolio credit risk. |
| Keywords: | geographic aggregation, credit modeling, countercyclical |
| JEL: | E44 G53 R30 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:hfa:wpaper:23-02&r=rmg |
| By: | Tongseok Lim |
| Abstract: | The Black-Scholes-Merton model is a mathematical model for the dynamics of a financial market that includes derivative investment instruments, and its formula provides a theoretical price estimate of European-style options. The model's fundamental idea is to eliminate risk by hedging the option by purchasing and selling the underlying asset in a specific way, that is, to replicate the payoff of the option with a portfolio (which continuously trades the underlying) whose value at each time can be verified. One of the most crucial, yet restrictive, assumptions for this task is that the market follows a geometric Brownian motion, which has been relaxed and generalized in various ways. The concept of robust finance revolves around developing models that account for uncertainties and variations in financial markets. Martingale Optimal Transport, which is an adaptation of the Optimal Transport theory to the robust financial framework, is one of the most prominent directions. In this paper, we consider market models with arbitrarily many underlying assets whose values are observed over arbitrarily many time periods, and demonstrates the existence of a portfolio sub- or super-hedging a general path-dependent derivative security in terms of trading European options and underlyings, as well as the portfolio replicating the derivative payoff when the market model yields the extremal price of the derivative given marginal distributions of the underlyings. In mathematical terms, this paper resolves the question of dual attainment for the multi-period vectorial martingale optimal transport problem. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.00807&r=rmg |
| By: | Singh, Mahendra Kumar; Lence, Sergio H. |
| Keywords: | Agricultural Finance, Agribusiness, Marketing |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea22:335789&r=rmg |
| By: | Roger J. A. Laeven; Emanuela Rosazza Gianin; Marco Zullino |
| Abstract: | This paper establishes characterization results for dynamic return and star-shaped risk measures induced via backward stochastic differential equations (BSDEs). We first characterize a general family of static star-shaped functionals in a locally convex Fr\'echet lattice. Next, employing the Pasch-Hausdorff envelope, we build a suitable family of convex drivers of BSDEs inducing a corresponding family of dynamic convex risk measures of which the dynamic return and star-shaped risk measures emerge as the essential minimum. Furthermore, we prove that if the set of star-shaped supersolutions of a BSDE is not empty, then there exists, for each terminal condition, at least one convex BSDE with a non-empty set of supersolutions, yielding the minimal star-shaped supersolution. We illustrate our theoretical results in a few examples and demonstrate their usefulness in two applications, to capital allocation and portfolio choice. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.03447&r=rmg |
| By: | Sander Barendse |
| Abstract: | We propose an $\ell_1$-penalized estimator for high-dimensional models of Expected Shortfall (ES). The estimator is obtained as the solution to a least-squares problem for an auxiliary dependent variable, which is defined as a transformation of the dependent variable and a pre-estimated tail quantile. Leveraging a sparsity condition, we derive a nonasymptotic bound on the prediction and estimator errors of the ES estimator, accounting for the estimation error in the dependent variable, and provide conditions under which the estimator is consistent. Our estimator is applicable to heavy-tailed time-series data and we find that the amount of parameters in the model may grow with the sample size at a rate that depends on the dependence and heavy-tailedness in the data. In an empirical application, we consider the systemic risk measure CoES and consider a set of regressors that consists of nonlinear transformations of a set of state variables. We find that the nonlinear model outperforms an unpenalized and untransformed benchmark considerably. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.01033&r=rmg |
| By: | Yanqin Fan; Hyeonseok Park; Gaoqian Xu |
| Abstract: | This paper studies distributional model risk in marginal problems, where each marginal measure is assumed to lie in a Wasserstein ball centered at a fixed reference measure with a given radius. Theoretically, we establish several fundamental results including strong duality, finiteness of the proposed Wasserstein distributional model risk, and the existence of an optimizer at each radius. In addition, we show continuity of the Wasserstein distributional model risk as a function of the radius. Using strong duality, we extend the well-known Makarov bounds for the distribution function of the sum of two random variables with given marginals to Wasserstein distributionally robust Markarov bounds. Practically, we illustrate our results on four distinct applications when the sample information comes from multiple data sources and only some marginal reference measures are identified. They are: partial identification of treatment effects; externally valid treatment choice via robust welfare functions; Wasserstein distributionally robust estimation under data combination; and evaluation of the worst aggregate risk measures. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.00779&r=rmg |
| By: | Lee, Siun; Vedenov, Dmitry |
| Keywords: | Risk and Uncertainty, International Relations/Trade, Agricultural and Food Policy |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea22:335770&r=rmg |
| By: | Wenbo Ge; Pooia Lalbakhsh; Leigh Isai; Artem Lensky; Hanna Suominen |
| Abstract: | This study aims at comparing several deep learning-based forecasters in the task of volatility prediction using multivariate data, proceeding from simpler or shallower to deeper and more complex models and compare them to the naive prediction and variations of classical GARCH models. Specifically, the volatility of five assets (i.e., S\&P500, NASDAQ100, gold, silver, and oil) was predicted with the GARCH models, Multi-Layer Perceptrons, recurrent neural networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In most cases the Temporal Fusion Transformer followed by variants of Temporal Convolutional Network outperformed classical approaches and shallow networks. These experiments were repeated, and the difference between competing models was shown to be statistically significant, therefore encouraging their use in practice. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.12446&r=rmg |
| By: | Abramov Alexander (RANEPA); Radygin Alexander (Gaidar Institute for Economic Policy); Chernova Maria (RANEPA) |
| Abstract: | The year 2022 was one of the most difficult periods for the global financial market in many years. Due to a unique combination of adverse economic and geopolitical factors, investments in almost all assets, with few exceptions, had negative returns in 2022. Even investment assets such as government securities, precious metals, real estate and cryptocurrency failed to perform their functions of hedging investor returns against losses. In January-February 2023, many financial assets began to show positive returns again, however, this trend gradually slowed down under the influence of the same factors that negatively affected the financial market in 2022. |
| Keywords: | Russian economy, stock market, bond market, corporate bond market, derivatives market, private investors |
| JEL: | G01 G12 G18 G21 G24 G28 G32 G33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:gai:ppaper:ppaper-2023-1275&r=rmg |
| By: | Lorenzo Stanca |
| Abstract: | Models of recursive utility are of central importance in many economic applications. This paper investigates a new behavioral feature exhibited by these models: aversion to risks that exhibit persistence (positive autocorrelation) through time, referred to as correlation aversion. I introduce a formal notion of such a property and provide a characterization based on risk attitudes, and show that correlation averse preferences admit a specific variational representation. I discuss how these findings imply that attitudes toward correlation are a crucial behavioral aspect driving the applications of recursive utility in fields such as asset pricing, climate policy, and optimal fiscal policy. |
| Keywords: | Intertemporal substitution, risk aversion, correlation aversion, recursive utility, preference for early resolution of uncertainty, information. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:cca:wpaper:693&r=rmg |
| By: | Ostry, D. A. |
| Abstract: | I build a model in which speculators unwind carry trades and hedgers fly to relatively liquid U.S. Treasuries during global financial disasters. The net effect of these flows produces an amplified U.S. dollar appreciation against high-yield currencies in disasters and a dampened depreciation, or even an appreciation, against low-yield ones. I verify this prediction by examining deviations from uncovered interest parity (UIP) within a novel quantile-regression framework. In the tail quantiles, I show that interest differentials predict high-yield currencies to suffer depreciations ten times as large as suggested by UIP, while spikes in Treasury liquidity premia meaningfully appreciate the dollar regardless of the U.S. relative interest rate. A complementary analysis of speculators’ and hedgers’ currency futures positions substantiates my model’s mechanism and highlights that hedging agents imbue the U.S. dollar with its unique safe-haven status. |
| Keywords: | Disaster Risk, Exchange Rates, Liquidity Yields, Quantile regression, U.S. Safety |
| JEL: | C22 F31 G15 |
| Date: | 2023–06–07 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2343&r=rmg |
| By: | Chiara Scotti |
| Abstract: | The past 15 years have been eventful. The Global Financial Crisis (GFC) reminded us of the importance of a stable financial system to a well-functioning economy, one with low and stable inflation and maximum employment. Given the recent banking stress, we ponder this issue again. The pandemic was a huge shock surrounded by much uncertainty, making precise forecasts within traditional models difficult. And more recently, there has been continuous talk of a soft landing and recession risks. In this paper, I focus on some of the lessons we have learned over the years: (i) uncertainty and tail risk have cyclical variation; (ii) financial shocks can have a significant effect on macroeconomic outcomes; (iii) the impact of shocks is stronger in periods of high volatility. These lessons have important implications for policymakers in today’s environment. |
| Keywords: | uncertainty; tail risk; stochastic volatility; monetary policy; financial stability |
| JEL: | C32 E44 |
| Date: | 2023–07–07 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddwp:96432&r=rmg |
| By: | Lee, David |
| Abstract: | Equity-linked securities with a guaranteed amount have some specific interesting features for investors, like downside protection and capital appreciation. The contract has a guaranteed return plus a payment linked to the performance of a basket of equities or indices averaged over a certain period. This article presents an analytical model for valuing equity-linked notes and computing the corresponding hedge ratios. The model appears to be accurate over a wide range of valuation parameters based on numerical studies. Finally, we use the model to value a segregated fund with a guarantee amount at maturity. |
| Keywords: | Equity-linked securities, segregated fund, asset pricing, derivative valuation, hedge ratio. |
| JEL: | C58 D46 G12 |
| Date: | 2023–06–27 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:117775&r=rmg |
| By: | Gallagher, Nicholas |
| Keywords: | Production Economics, Risk and Uncertainty, Research Methods/Statistical Methods |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea22:335978&r=rmg |
| By: | Hermanns, Benedicta; Kairies-Schwarz, Nadja; Kokot, Johanna; Vomhof, Markus |
| Abstract: | We investigate heterogeneity in patterns of preferences for health insurance features using health insurance choice data from a controlled laboratory experiment. Within the experiment, participants make consecutive insurance choices based on choice sets that vary in composition and size. We keep the health risk constant and equal for everyone. In addition, we implement a treatment that entails a feature-based insurance filter, allowing us to validate feature preferences. We also account for individually elicited risk preferences. On aggregate, we find that there is considerable heterogeneity in consumer choice. Participants differ particularly (a) in their willingness to pay to insure themselves against illnesses that differ in terms of their probability of occurrence and the size of the losses to be covered and (b) in their preference to forgo deductibles. However, if we measure the quality of individuals' decisions based on risk preferences, the heterogeneity among participants disappears. Our results suggest that heterogeneity in health insurance choices is not reflected in decision quality when we assume a rank-dependent expected utility model of risk preferences. |
| Keywords: | health insurance, consumer preferences, heterogeneity, laboratory experiment, risk preferences |
| JEL: | C91 I13 D81 D83 G22 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:hcherp:202329&r=rmg |
| By: | Jiafa He; Cong Zheng; Can Yang |
| Abstract: | We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it challenging to develop effective market making strategies. To address this challenge, we propose a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals to develop a more accurate and robust market making strategy. Our results of market making strategies based on different deep reinforcement learning algorithms under the simulation scenarios and real data experiments in the cryptocurrency markets show that the proposed framework outperforms existing methods in terms of profitability and risk management. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.17179&r=rmg |
| By: | Jiong Liu; M. Dashti Moghaddam; R. A. Serota |
| Abstract: | We undertake a systematic study of historic market volatility spanning roughly five preceding decades. We focus specifically on the time series of realized volatility (RV) of the S&P500 index and its distribution function. As expected, the largest values of RV coincide with the largest economic upheavals of the period: Savings and Loan Crisis, Tech Bubble, Financial Crisis and Covid Pandemic. We address the question of whether these values belong to one of the three categories: Black Swans (BS), that is they lie on scale-free, power-law tails of the distribution; Dragon Kings (DK), defined as statistically significant upward deviations from BS; or Negative Dragons Kings (nDK), defined as statistically significant downward deviations from BS. In analyzing the tails of the distribution with RV > 40, we observe the appearance of "potential" DK which eventually terminate in an abrupt plunge to nDK. This phenomenon becomes more pronounced with the increase of the number of days over which the average RV is calculated -- here from daily, n=1, to "monthly, " n=21. We fit the entire distribution with a modified Generalized Beta (mGB) distribution function, which terminates at a finite value of the variable but exhibits a long power-law stretch prior to that, as well as Generalized Beta Prime (GB2) distribution function, which has a power-law tail. We also fit the tails directly with a straight line on a log-log scale. In order to ascertain BS, DK or nDK behavior, all fits include their confidence intervals and p-values are evaluated for the data points to check if they can come from the respective distributions. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.03693&r=rmg |
| By: | Berg, Tobias; Carletti, Elena; Claessens, Stijn; Krahnen, Jan Pieter; Monasterolo, Irene; Pagano, Marco |
| Abstract: | Climate risk has become a major concern for financial institutions and financial markets. Yet, climate policy is still in its infancy and contributes to increased uncertainty. For example, the lack of a sufficiently high carbon price and the variety of definitions for green activities lower the value of existing and new capital, and complicate risk management. This column argues that it would be welfare-enhancing if policy changes were to follow a predictable longer-term path. Accordingly, the authors suggest a role for financial regulation in the transition. |
| Keywords: | Climate Change, Financial Regulation and Banking |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safepl:100&r=rmg |
| By: | Carol Bertaut; Valentina Bruno; Hyun Song Shin |
| Abstract: | We highlight the role of duration and exchange rate risks on portfolio flows by using a unique and comprehensive database of US investor flows into emerging market government bonds denominated in local currency. Borrowing long-term mitigates roll-over risk but amplifies valuation changes that further interact with currency movements. Our analysis highlights the double-edged nature of long-term borrowing and draws attention to market stress dynamics from the nonbank financial sector. |
| Keywords: | portfolio flows, local currency bonds, non-bank financial intermediaries |
| JEL: | F65 G23 H63 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1109&r=rmg |
| By: | Thomas N. Cintra; Maxwell P. Holloway |
| Abstract: | We consider a liquidity provider's (LP's) exposure to stablecoin and liquid staking derivative (LSD) depegs on Curve's StableSwap pools. We construct a suite of metrics designed to detect potential asset depegs based on price and trading data. Using our metrics, we fine-tune a Bayesian Online Changepoint Detection (BOCD) algorithm to alert LPs of potential depegs before or as they occur. We train and test our changepoint detection algorithm against Curve LP token prices for 13 StableSwap pools throughout 2022 and 2023, focusing on relevant stablecoin and LSD depegs. We show that our model, trained on 2022 UST data, is able to detect the USDC depeg in March of 2023 at 9pm UTC on March 10th, approximately 5 hours before USDC dips below 99 cents, with few false alarms in the 17 months on which it is tested. Finally, we describe how this research may be used by Curve's liquidity providers, and how it may be extended to dynamically de-risk Curve pools by modifying parameters in anticipation of potential depegs. This research underpins an API developed to alert Curve LPs, in real-time, when their positions might be at risk. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.10612&r=rmg |
| By: | Andrea Ugolini; Juan C. Reboredo; Javier Ojea Ferreiro |
| Abstract: | We study whether the credit default swap (CDS) spreads of firms reflect the risk from climate transition. We first construct a climate transition risk (CTR) factor by using information on the vulnerability of a firm’s value to the transition to a low-carbon economy. We then document how this factor shifts the term structure of the CDS spreads of more vulnerable firms but not of less vulnerable firms. Considering the impact of different climate transition policies on the CTR factor, we find that these policies have asymmetric and significant economic impacts on the credit risk of more vulnerable firms, and negligible effects on other firms. |
| Keywords: | Climate change; Credit risk management; Econometric and statistical methods |
| JEL: | C24 G12 G32 Q54 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:23-38&r=rmg |
| By: | Richard Dewey; Craig Newbold |
| Abstract: | We investigate the most common type of blockchain-based decentralized exchange, which are known as constant function market makers (CFMMs). We examine the the market microstructure around CFMMs and present a model for valuing the liquidity provider (LP) mechanism and estimating the value of the associated derivatives. We develop a model with two types of traders that have different information and contribute methods for simulating the behavior of each trader and accounting for trade PnL. We also develop ideas around the equilibrium distribution of fair price conditional on the arrival of traders. Finally, we show how these findings might be used to think about parameters for alternative CFMMs. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.11580&r=rmg |