nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒11‒20
27 papers chosen by
Stan Miles, Thompson Rivers University

  1. Black-Litterman Asset Allocation under Hidden Truncation Distribution By Jungjun Park; Andrew L. Nguyen
  2. Quantum Computational Algorithms for Derivative Pricing and Credit Risk in a Regime Switching Economy By Eric Ghysels; Jack Morgan; Hamed Mohammadbagherpoor
  3. From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI By Alex Kim; Maximilian Muhn; Valeri Nikolaev
  4. Estimation of VaR with jump process: application in corn and soybean markets By Minglian Lin; Indranil SenGupta; William Wilson
  5. Power law in Sandwiched Volterra Volatility model By Giulia Di Nunno; Anton Yurchenko-Tytarenko
  6. Assessing Macrofinancial Risks from Crypto Assets By Ms. Burcu Hacibedel; Hector Perez-Saiz
  7. Managing Liquidity Risk and the Importance of Bank Contingency Funding Plans By Carl White
  8. New Evidence on Spillovers Between Crypto Assets and Financial Markets By Roshan Iyer
  9. Impact of Loss-Framing and Risk Attitudes on Insurance Purchase: Insights from a Game-like Interface Study By Kunal Rajesh Lahoti; Shivani Hanji; Pratik Kamble; Kavita Vemuri
  10. Dynamic Realized Minimum Variance Portfolio Models By Donggyu Kim; Minseog Oh
  11. Causality in empirical analyses with emphasis on asymmetric information and risk management By Dionne, Georges
  12. The cumulant risk premium By Albert S. (Pete); Karamfil Todorov
  13. Economic Theory as Successive Approximations of Statistical Moments By Victor Olkhov
  14. Measuring Interest Rate Risk Management by Financial Institutions By Celso Brunetti; Nathan Foley-Fisher; Stéphane Verani
  15. Hidden semi-Markov models for rainfall-related insurance claims By Shi, Yue; Punzo, Antonio; Otneim, Håkon; Maruotti, Antonello
  16. Gambling in Risk-Taking Contests: Experimental Evidence By Matthew Embrey; Christian Seel; J. Philipp Reiss
  17. Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach By Shijia Song; Handong Li
  18. Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation By Xin Du; Kai Moriyama; Kumiko Tanaka-Ishii
  19. A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market By Jaydip Sen; Arup Dasgupta; Partha Pratim Sengupta; Sayantani Roy Choudhury
  20. Biodiversity-related Financial Risks - why it matters and how can we measure them? By Elene Nikuradze; Salome Tvalodze
  21. Geopolitical Risk and Foreign Portfolio Investment: A Tale of Advanced and Emerging Markets By Sangyup Choi; Jiri Havel
  22. Ambiguity, value of information and forest rotation decision under storm risk By Patrice Loisel; Marielle Brunette; Stéphane Couture
  23. Banks’ Portfolio of Government Debt and Sovereign Risk By António Afonso; José Alves; Sofia Monteiro
  24. A Portfolio Rebalancing Approach for the Indian Stock Market By Jaydip Sen; Arup Dasgupta; Subhasis Dasgupta; Sayantani Roychoudhury
  25. Household portfolio choices under (non-)linear income risk: an empirical framework By Julio Gálvez
  26. Cancer and portfolio choice: Evidence from Norwegian register data By Døskeland, Trond; Kværner, Jens
  27. Perpetual Futures Pricing By Damien Ackerer; Julien Hugonnier; Urban Jermann

  1. By: Jungjun Park; Andrew L. Nguyen
    Abstract: In this paper, we study the Black-Litterman (BL) asset allocation model (Black and Litterman, 1990) under the hidden truncation skew-normal distribution (Arnold and Beaver, 2000). In particular, when returns are assumed to follow this skew normal distribution, we show that the posterior returns, after incorporating views, are also skew normal. By using Simaan three moments risk model (Simaan, 1993), we could then obtain the optimal portfolio. Empirical data show that the optimal portfolio obtained this way has less risk compared to an optimal portfolio of the classical BL model and that they become more negatively skewed as the expected returns of portfolios increase, which suggests that the investors trade a negative skewness for a higher expected return. We also observe a negative relation between portfolio volatility and portfolio skewness. This observation suggests that investors may be making a trade-off, opting for lower volatility in exchange for higher skewness, or vice versa. This trade-off indicates that stocks with significant price declines tend to exhibit increased volatility.
    Date: 2023–10
  2. By: Eric Ghysels; Jack Morgan; Hamed Mohammadbagherpoor
    Abstract: Quantum computers are not yet up to the task of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper we introduce a class of stochastic processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages. The type of models we study are based on a regime switching volatility model driven by a Markov chain with observable states. The basic model features a Geometric Brownian Motion with drift and volatility parameters determined by the finite states of a Markov chain. We study algorithms to estimate credit risk and option pricing on a gate-based quantum computer. These models bring us closer to realistic market settings, and therefore quantum computing closer the realm of practical applications.
    Date: 2023–11
  3. By: Alex Kim; Maximilian Muhn; Valeri Nikolaev
    Abstract: We explore the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk. We develop and validate firm-level measures of risk exposure to political, climate, and AI-related risks. Using the GPT 3.5 model to generate risk summaries and assessments from the context provided by earnings call transcripts, we show that GPT-based measures possess significant information content and outperform the existing risk measures in predicting (abnormal) firm-level volatility and firms' choices such as investment and innovation. Importantly, information in risk assessments dominates that in risk summaries, establishing the value of general AI knowledge. We also find that generative AI is effective at detecting emerging risks, such as AI risk, which has soared in recent quarters. Our measures perform well both within and outside the GPT's training window and are priced in equity markets. Taken together, an AI-based approach to risk measurement provides useful insights to users of corporate disclosures at a low cost.
    Date: 2023–10
  4. By: Minglian Lin; Indranil SenGupta; William Wilson
    Abstract: Value at Risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital. Estimation of the VaR entails the quantification of prospective losses in a portfolio of investments, using a certain likelihood, under normal market conditions within a specific time period. The objective of this paper is to construct a model and estimate the VaR for a diversified portfolio consisting of multiple cash commodity positions driven by standard Brownian motions and jump processes. Subsequently, a thorough analytical estimation of the VaR is conducted for the proposed model. The results are then applied to two distinct commodities -- corn and soybean -- enabling a comprehensive comparison of the VaR values in the presence and absence of jumps.
    Date: 2023–11
  5. By: Giulia Di Nunno; Anton Yurchenko-Tytarenko
    Abstract: In this paper, we present analytical proof demonstrating that the Sandwiched Volterra Volatility (SVV) model is able to reproduce the power-law behavior of the at-the-money implied volatility skew, provided the correct choice of the Volterra kernel. To obtain this result, we assess the second-order Malliavin differentiability of the volatility process and investigate the conditions that lead to explosive behavior in the Malliavin derivative. As a supplementary result, we also prove a general Malliavin product rule.
    Date: 2023–11
  6. By: Ms. Burcu Hacibedel; Hector Perez-Saiz
    Abstract: Failures in the crypto space—including the fall of Terra USD and the FTX debacle—have sparked calls for strengthening countries’ policy frameworks for crypto assets, including by enhanced regulation and supervision. How have these heightened concerns about crypto assets been picked up in systemic risk assessment, and what can be done going forward? In this paper, we introduce a conceptual macrofinancial framework to understand and track systemic risks stemming from crypto assets. Specifically, we propose a country-level Crypto-Risk Assessment Matrix (C-RAM) to summarize the main vulnerabilities, useful indicators, potential triggers and potential policy responses related to the crypto sector. We also discuss how experts and officials can weave in specific vulnerabilities stemming from crypto asset activity into their assessment of systemic risk, and how they can provide policy advice and take action to help contain systemic risks when needed.
    Keywords: Crypto assets; vulnerabilities; systemic risk; macrofinancial; analyzing Macrofinancial risk; macro-prudential risk; country-level Crypto-Risk Assessment Matrix; micro-prudential risk; price fluctuation; Virtual currencies; Financial sector; Currencies; Credit risk; Blockchain and DLT; Global
    Date: 2023–09–29
  7. By: Carl White
    Abstract: Federal banking regulators have issued updated guidance on funding and liquidity risk management to include the importance of contingency funding plans.
    Keywords: banking regulators; liquidity risk; bank contingency funding plans
    Date: 2023–09–28
  8. By: Roshan Iyer
    Abstract: We analyze returns and volatility spillovers among a representative set of crypto and financial assets. The magnitude of spillovers increases during periods of heightened turbulence due to negative economic-financial news, crypto market events, or exogenous shocks. There is evidence of increasing spillovers over time, with a peak during the COVID-19 pandemic, implying growing interdependence. Crypto assets predominantly transmit spillovers to financial markets, though reversals occur during periods of financial stress. The increased correlation during risk-off episodes suggests that crypto assets could serve as important conduits for financial market shocks, generating financial stability risks.
    Keywords: Cryptocurrencies; Crypto assets; Bitcoin; Spillovers; Return and Volatility Connectedness
    Date: 2023–09–29
  9. By: Kunal Rajesh Lahoti; Shivani Hanji; Pratik Kamble; Kavita Vemuri
    Abstract: This study investigates the impact of loss-framing and individual risk attitude on willingness- to purchase insurance products utilizing a game-like interface as choice architecture. The application presents events as experienced in real life. Both financial and emotional loss-framing events are followed by choices to purchase insurance. The participant cohorts considered were undergraduate students and older participants; the latter group was further subdivided by income and education. The within-subject analysis reveals that the loss framing effect on insurance consumption is higher in the younger population, though contingent on the insurance product type. Health and accident insurance shows a negative correlation with risk attitudes for younger participants and a positive correlation with accident insurance for older participants. Risk attitude and life insurance products showed no dependency. The findings elucidate the role of age, income, family responsibilities, and risk attitude in purchasing insurance products. Importantly, it confirms the heuristics of framing/nudging.
    Date: 2023–10
  10. By: Donggyu Kim; Minseog Oh
    Abstract: This paper introduces a dynamic minimum variance portfolio (MVP) model using nonlinear volatility dynamic models, based on high-frequency financial data. Specifically, we impose an autoregressive dynamic structure on MVP processes, which helps capture the MVP dynamics directly. To evaluate the dynamic MVP model, we estimate the inverse volatility matrix using the constrained $\ell_1$-minimization for inverse matrix estimation (CLIME) and calculate daily realized non-normalized MVP weights. Based on the realized non-normalized MVP weight estimator, we propose the dynamic MVP model, which we call the dynamic realized minimum variance portfolio (DR-MVP) model. To estimate a large number of parameters, we employ the least absolute shrinkage and selection operator (LASSO) and predict the future MVP and establish its asymptotic properties. Using high-frequency trading data, we apply the proposed method to MVP prediction.
    Date: 2023–10
  11. By: Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: We discuss the difficult question of measuring causality effects in empirical analyses, with applications to asymmetric information and risk management. It is now well documented in the economic literature that policy analysis must be causal. Hence, the measurement of its effects must also be causal. After having presented the main frameworks for causality analysis, including instrumental variable, difference-in-differences, and generalized method of moments, we analyze the following questions: Does risk management affect firm value and risk? Do we face a moral hazard problem in the insurance data? How can we separate moral hazard from adverse selection and asymmetric learning? Is liquidity creation a causal factor for reinsurance demand? We show that residual information problems are often present in different markets, while risk management may increase firm value when appropriate methodologies are applied.
    Keywords: Asymmetric information; moral hazard; adverse selection; risk learning; risk management; causality test; dynamic data; essential heterogeneity; difference-in-differences; instrumental variable; propensity scor; generalized method of moments
    JEL: C12 C18 C23 C25 C26 D80 G11 G22
    Date: 2023–10–31
  12. By: Albert S. (Pete); Karamfil Todorov
    Abstract: We develop a novel methodology to measure the risk premium of higher-order cumulants (closely related to the moments of a distribution) based on leveraged ETFs. We show that the risk premium on these ETFs reflects the difference between physical and risk-neutral cumulants, which we call the cumulant risk premium (CRP). We show that the CRP is different from zero across asset classes (equities, bonds, commodities, currencies, and volatility) and is large in times of stress. We illustrate that highly leveraged strategies are extremely exposed to higher-order cumulants. Our results have implications for hedge funds, factor models, momentum strategies, and options.
    Keywords: cumulants, leverage, ETF, factor models, VIX, momentum, options
    JEL: G1 G12 G13 G23
    Date: 2023–10
  13. By: Victor Olkhov
    Abstract: This paper highlights the links between the descriptions of macroeconomic variables and statistical moments of market trade, price, and return. We consider economic transactions during the averaging time interval {\Delta} as the exclusive matter that determines the change of any economic variables. We regard the stochasticity of market trade values and volumes during {\Delta} as the only root of the random properties of price and return. We describe how the market-based n-th statistical moments of price and return during {\Delta} depend on the n-th statistical moments of trade values and volumes or equally on sums during {\Delta} of the n-th power of market trade values and volumes. We introduce the secondary averaging procedure that defines statistical moments of trade, price, and return during the averaging interval {\Delta}2>>{\Delta}. As well, the secondary averaging during {\Delta}2>>{\Delta} introduces statistical moments of macroeconomic variables, which were determined as sums of economic transactions during {\Delta}. In the coming years, predictions of the market-based probabilities of price and return will be limited by Gaussian-type distributions determined by the first two statistical moments. We discuss the roots of the internal weakness of the conventional hedging tool, Value-at-Risk, that could not be solved and thus remain the source of additional risks and losses. One should consider economic theory as a set of successive approximations, each of which describes the next array of the n-th statistical moments of market transactions and macroeconomic variables, which are repeatedly averaged during the sequence of increasing time intervals.
    Date: 2023–09
  14. By: Celso Brunetti; Nathan Foley-Fisher; Stéphane Verani
    Abstract: Financial intermediaries manage myriad interest rate risk exposures. We propose a new method to measure financial intermediaries' residual interest rate risk using high-frequency financial market data. Our method exploits all available high-frequency information and is valid under extremely weak assumptions. Applying the method to U.S. life insurers, we find their interest rate risk management strategies are generally effective. However, life insurers are more sensitive to changes in long-term interest rates than property and casualty insurers. We show that the term premium helps to explain the difference in sensitivities between the two types of insurer.
    Keywords: Financial institutions; Interest rate risk management; High-frequency financial econometrics; Subsampling; Life insurers
    JEL: G20 C58
    Date: 2023–10–12
  15. By: Shi, Yue (Dept. of Business and Management Science, Norwegian School of Economics); Punzo, Antonio (Dept. of Economics and Business, University of Catania); Otneim, Håkon (Dept. of Business and Management Science, Norwegian School of Economics); Maruotti, Antonello (Dept. GEPLI, LUMSA University)
    Abstract: We analyze the temporal structure of a novel insurance dataset about home insurance claims related to rainfall-induced damage in Norway, and employ a hidden semi-Markov model to capture the non-Gaussian nature and temporal dynamics of these claims. By exploring a wide range of candidate distributions and evaluating their goodness-of-fit as well as commonly used risk measures, we identify a suitable model for effectively modeling insurance losses stemming from rainfall-related incidents. Our findings highlight the importance of considering the temporal aspects of weather-related insurance claims and demonstrate that the proposed hidden semi-Markov model adeptly captures this feature. Moreover, the model estimates reveal a concerning trend: the risks associated with heavy rain in the context of home insurance have exhibited an upward trajectory between 2004 and 2020, aligning with the evidence of a changing climate. This insight has significant implications for insurance companies, providing them with valuable information for accurate and robust modeling in the face of climate uncertainties. By shedding light on the evolving risks related to heavy rain and their impact on home insurance, our study offers essential insights for insurance companies to adapt their strategies and effectively manage these emerging challenges. It underscores the necessity of incorporating climate change considerations into insurance models and emphasizes the importance of continuously monitoring and reassessing risk levels associated with rainfall-induced damage. Ultimately, our research contributes to the broader understanding of climate risk in the insurance industry and supports the development of resilient and sustainable insurance practices.
    Keywords: Mixtures; Non-Gaussian distributions; EM algorithm; Risk measures; Rainfall data
    JEL: C02 C40 C60
    Date: 2023–11–06
  16. By: Matthew Embrey (Department of Economics, University of Sussex, BN1 9SL Falmer, United Kingdom); Christian Seel; J. Philipp Reiss
    Abstract: This paper experimentally investigates excessive risk taking in contest schemes by implementing a stopping task based on Seel and Strack (2013). In this stylized setting, managers with contest payoffs have an incentive to delay halting projects with a negative expectation, with the induced inefficiency being highest for a moderately negative drift. The experiment systematically varies the negative drift (between-subjects) and the payoff incentives (within-subject). We find evidence for excessive risk taking in all our treatment conditions, with the non-monotonicity at least as problematic as predicted. Contrary to the theoretical predictions, this aggregate pattern of behaviour is seen even without contest incentives. Further analysis suggests that many subjects display behaviour consistent with some intrinsic motivation for taking risk. This intrinsic motive and the strategic motive for excessive risk taking reinforce the non-monotonicity. The experiment uncovers a behavioural nuance where contest incentives crowd out an intrinsic inclination to gamble.
    Keywords: Contests, Relative performance pay, Risk-taking behaviour, Laboratory experiment
    JEL: C72 C92 D81
    Date: 2023–07
  17. By: Shijia Song; Handong Li
    Abstract: Bank crisis is challenging to define but can be manifested through bank contagion. This study presents a comprehensive framework grounded in nonlinear time series analysis to identify potential early warning signals (EWS) for impending phase transitions in bank systems, with the goal of anticipating severe bank crisis. In contrast to traditional analyses of exposure networks using low-frequency data, we argue that studying the dynamic relationships among bank stocks using high-frequency data offers a more insightful perspective on changes in the banking system. We construct multiple recurrence networks (MRNs) based on multidimensional returns of listed banks' stocks in China, aiming to monitor the nonlinear dynamics of the system through the corresponding indicators and topological structures. Empirical findings indicate that key indicators of MRNs, specifically the average mutual information, provide valuable insights into periods of extreme volatility of bank system. This paper contributes to the ongoing discourse on early warning signals for bank instability, highlighting the applicability of predicting systemic risks in the context of banking networks.
    Date: 2023–10
  18. By: Xin Du; Kai Moriyama; Kumiko Tanaka-Ishii
    Abstract: This paper shows a novel machine learning model for realized volatility (RV) prediction using a normalizing flow, an invertible neural network. Since RV is known to be skewed and have a fat tail, previous methods transform RV into values that follow a latent distribution with an explicit shape and then apply a prediction model. However, knowing that shape is non-trivial, and the transformation result influences the prediction model. This paper proposes to jointly train the transformation and the prediction model. The training process follows a maximum-likelihood objective function that is derived from the assumption that the prediction residuals on the transformed RV time series are homogeneously Gaussian. The objective function is further approximated using an expectation-maximum algorithm. On a dataset of 100 stocks, our method significantly outperforms other methods using analytical or naive neural-network transformations.
    Date: 2023–10
  19. By: Jaydip Sen; Arup Dasgupta; Partha Pratim Sengupta; Sayantani Roy Choudhury
    Abstract: This chapter presents a comparative study of the three portfolio optimization methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing on the stocks chosen from 15 sectors listed on the National Stock Exchange of India. The top stocks of each cluster are identified based on their free-float market capitalization from the report of the NSE published on July 1, 2022 (NSE Website). For each sector, three portfolios are designed on stock prices from July 1, 2019, to June 30, 2022, following three portfolio optimization approaches. The portfolios are tested over the period from July 1, 2022, to June 30, 2023. For the evaluation of the performances of the portfolios, three metrics are used. These three metrics are cumulative returns, annual volatilities, and Sharpe ratios. For each sector, the portfolios that yield the highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio over the training and the test periods are identified.
    Date: 2023–10
  20. By: Elene Nikuradze (Sustainable Finance Division, National Bank of Georgia); Salome Tvalodze (Head of Sustainable Finance Division, National Bank of Georgia)
    Abstract: The potential consequences of biodiversity and ecosystem services loss can have a significant impact on the stability of economies and financial systems. The following research paper contributes to a growing body of literature that seeks to analyze the connections between biodiversity loss and financial stability. The study focuses on the assessment of biodiversity-related financial risks (BRFR) in Georgia and provides quantitative estimates of the dependencies and impacts of the financial system on biodiversity and ecosystem services. The findings reveal that around 46 percent of Georgian commercial banks' lending portfolio to legal entities could be exposed to biodiversity-related physical risk, being moderately or highly/very highly dependent on one or more ecosystem services. Additionally, around 54 percent of Georgian banks’ business lending portfolio could be exposed to sectors that strongly impact ecosystem services and, thus, may face a high transition risk.
    Keywords: Biodiversity; Biodiversity-related Financial Risks; Ecosystem Services, ENCORE
    JEL: E58 G21 Q01 Q57
    Date: 2023–07
  21. By: Sangyup Choi (Yonsei University); Jiri Havel (University of Rochester)
    Abstract: We study the influence of local geopolitical risk on U.S. cross-border portfolio investment, covering the period from 1994 to 2021. We uncover significant heterogeneity between advanced and emerging market destinations, revealing that local geopolitical risk exerts a dampening effect on U.S. purchases of bonds and equities solely within emerging markets, while having no discernible impact on advanced markets. We identify poor institutional quality as the primary driver behind the heightened sensitivity of portfolio investment to geopolitical risk in emerging markets, thereby signaling potential implications for financial stability. Moreover, our analysis reveals a noteworthy phenomenon where U.S. investment in emerging market bonds experiences a considerable decline in response to the geopolitical risk within other emerging markets in close geographical proximity, displaying a robust contagion effect. However, such contagions do not manifest in cross-border equity investment. Notably, these contagion effects are observed exclusively among emerging markets, providing valuable insights into investors’ portfolio adjustments in the face of elevated geopolitical risk.
    Keywords: Geopolitical risk; Foreign portfolio investment; Emerging markets; Institutional quality; Trilemma; Contagion
    JEL: E44 F21 F51 G11
    Date: 2023–10
  22. By: Patrice Loisel (MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie - INRA - Institut National de la Recherche Agronomique - Montpellier SupAgro - Institut national d’études supérieures agronomiques de Montpellier); Marielle Brunette (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Stéphane Couture (MIAT INRAE - Unité de Mathématiques et Informatique Appliquées de Toulouse - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Date: 2023–06–20
  23. By: António Afonso; José Alves; Sofia Monteiro
    Abstract: We analyze domestic, foreign, and central banks holdings of public debt for 31 countries for the period of 1989-2022, applying panel regressions and quantile analysis. We conclude that an increase in sovereign risk raises the share of domestic banks’ portfolio of public debt and reduces the percentage holdings in the case of central banks. Better sovereign ratings also increase (decrease) the share of commercial (central) banks’ holdings. Furthermore, the effects of an increment in the risk for domestic investors have increased since the 2010 financial crisis.
    Keywords: banking, sovereign debt, sovereign risk, financial crisis, ratings
    JEL: C21 E58 G24 G32 H63
    Date: 2023
  24. By: Jaydip Sen; Arup Dasgupta; Subhasis Dasgupta; Sayantani Roychoudhury
    Abstract: This chapter presents a calendar rebalancing approach to portfolios of stocks in the Indian stock market. Ten important sectors of the Indian economy are first selected. For each of these sectors, the top ten stocks are identified based on their free-float market capitalization values. Using the ten stocks in each sector, a sector-specific portfolio is designed. In this study, the historical stock prices are used from January 4, 2021, to September 20, 2023 (NSE Website). The portfolios are designed based on the training data from January 4, 2021 to June 30, 2022. The performances of the portfolios are tested over the period from July 1, 2022, to September 20, 2023. The calendar rebalancing approach presented in the chapter is based on a yearly rebalancing method. However, the method presented is perfectly flexible and can be adapted for weekly or monthly rebalancing. The rebalanced portfolios for the ten sectors are analyzed in detail for their performances. The performance results are not only indicative of the relative performances of the sectors over the training (i.e., in-sample) data and test (out-of-sample) data, but they also reflect the overall effectiveness of the proposed portfolio rebalancing approach.
    Date: 2023–10
  25. By: Julio Gálvez (Banco de España)
    Abstract: This paper develops a flexible, semi-structural framework to empirically quantify the non-linear transmission of income shocks to household portfolio choice decisions both at the extensive and intensive margins. I model stock market participation and portfolio allocation rules as age-dependent functions of persistent and transitory earnings components, wealth and unobserved taste shifters. I establish non-parametric identification and propose a tractable, simulation-based estimation algorithm, building on recent developments in the sample selection literature. Using recent waves of PSID data, I find heterogeneous income and wealth effects on both extensive and intensive margins, over the wealth and life-cycle dimensions. These results suggest that preferences are heterogeneous across the wealth distribution and over the life cycle. Moreover, in impulse response exercises, I find sizeable extensive margin responses to persistent income shocks. Finally, I find heterogeneity in participation costs across households in the wealth distribution.
    Keywords: stock market participation, non-linear income persistence, sample selection, quantile selection models, latent variables
    JEL: C23 C24 D31 G50 J24
    Date: 2023–09
  26. By: Døskeland, Trond; Kværner, Jens (Tilburg University, School of Economics and Management)
    Date: 2022
  27. By: Damien Ackerer; Julien Hugonnier; Urban Jermann
    Abstract: Perpetual futures are contracts without expiration date in which the anchoring of the futures price to the spot price is ensured by periodic funding payments from long to short. We derive explicit expressions for the no-arbitrage price of various perpetual contracts, including linear, inverse, and quantos futures in both discrete and continuous-time. In particular, we show that the futures price is given by the risk-neutral expectation of the spot sampled at a random time that reflects the intensity of the price anchoring. Furthermore, we identify funding specifications that guarantee the coincidence of futures and spot prices, and show that for such specifications perpetual futures contracts can be replicated by dynamic trading in primitive securities.
    Date: 2023–10

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