nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒03‒27
nine papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Money Market Funds and the Pricing of Near-Money Assets By Sebastian Doerr; Egemen Eren; Semyon Malamud
  2. Mutual Fund Flows and the Supply of Capital in Municipal Financing By Manuel Adelino; Sophia Chiyoung Cheong; Jaewon Choi; Ji Yeol Jimmy Oh
  3. The Market-Based Probability of Stock Returns By Victor Olkhov
  4. The systemic risk approach based on implied and realized volatility By Paweł Sakowski; Rafał Sieradzki; Robert Ślepaczuk
  5. Price Discovery for Derivatives By Christian Keller; Michael Tseng
  6. SPX, VIX and scale-invariant LSV\footnote{Local Stochastic Volatility} By Alexander Lipton; Adil Reghai
  7. RIM-Based Value Premium and Factor Pricing Using Value-Price Divergence By Lin William Cong; Nathan Darden George; Guojun Wang
  8. Finding the Optimal Currency Composition of Foreign Exchange Reserves with a Quantum Computer By Martin Vesely
  9. Physical Momentum in the Indian Stock Market By Naresh Kumar Devulapally; Tulasi Narendra Das Tripurana

  1. By: Sebastian Doerr (Bank for International Settlements); Egemen Eren (Bank for International Settlements); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute)
    Abstract: US money market funds (MMFs) play an important role in short-term markets as large investors of Treasury bills (T-bills) and repurchase agreements (repos) with banks and the Federal Reserve, some of the world’s safest and most liquid assets. We build a theoretical model in which MMFs’ strategic interactions generate a trade-off between their market power in the repo market and their price impact in the T-bill market. Empirically, we show that MMFs’ portfolio allocation decisions between repos and T-bills have an economically significant impact on T-bill rates and market liquidity, and the liquidity premium on T-bills. Guided by our model, we devise instrumental variables to establish a causal effect. Using a granular holding-level dataset we confirm the model’s prediction that MMFs internalize their price impact in the T-bill market when they set repo rates. Moreover, when Treasury market liquidity is low, MMFs tilt their portfolios away from T-bills towards repos with the Federal Reserve. Our results have broad implications.
    Keywords: T-bills, repo, market power, price impact, liquidity premium, money market funds
    JEL: E44 G11 G12 G23
    Date: 2023–01
  2. By: Manuel Adelino; Sophia Chiyoung Cheong; Jaewon Choi; Ji Yeol Jimmy Oh
    Abstract: This paper identifies the impact of fluctuations in the supply of capital from mutual funds on municipal bond financing and makes three contributions to the literature. First, we develop an identification strategy based on the Morningstar rating methodology at the moment that funds reach 5 years in operation. This approach isolates supply-side effects that are orthogonal to both fund and issuer fundamentals and can be applied in a broad range of settings. Second, we show that exogeneous fund flows lead to more municipal bond issuances and raise bond prices, but only when funds, issuers, and underwriters are connected through existing relationships. This result highlights the role of relationship lending in the context of municipal bond financing. Third, our results suggest that municipal bond issuers exploit favorable financing conditions to issue bonds with shorter delays and lower transaction costs, such as non-general-obligation bonds that require no voter approval and non-green bonds. These frictions can limit the impact of capital-supply shocks on municipal financing.
    JEL: G23 G32 H74
    Date: 2023–02
  3. By: Victor Olkhov
    Abstract: We show how time-series of random market trade values and volumes completely describe stochasticity of stock returns. We derive equation that links up returns with current and past trade values and show how statistical moments of the trade values and volumes determine statistical moments of stock returns. We estimate statistical moments of the trade values and volumes by the conventional frequency-based probability. However we believe that frequencies of stock returns don't define its probabilities as market and financial concepts. We present the market-based treatment of the probability of stock returns that defines average returns during "trading day" that completely match conventional notion of the weighted value return of the portfolio. We derive how statistical moments of the market trade values and volumes define approximations of the characteristic functions and probability density functions of stock returns. We derive volatility of stock returns, autocorrelations of stock returns, returns-volume and returns-price correlations through corresponding relations between statistical moments of the market trade values and volumes. The market-based probability of stock returns reveals direct dependence of statistical properties of stock returns on market trade randomness and economic uncertainty. Any reasonable forecasting of stock returns should be based on well-grounded predictions of the market trades and economic environment.
    Date: 2023–02
  4. By: Paweł Sakowski (University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance, Quantitative Finance Research Group); Rafał Sieradzki (New York University Stern School of Business; Cracow University of Economics); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance, Quantitative Finance Research Group)
    Abstract: We propose a new measure of systemic risk to analyze the impact of the major financial market turmoils in the stock markets from 2000 to 2021 in the USA, Europe, Brazil, and Japan. Our Implied Volatility Realized Volatility Systemic Risk Indicator (IVRVSRI) shows that the reaction of stock markets varies across different geographical locations and the persistence of the shocks depends on the historical volatility and long-term average volatility level in a given market. The methodology applied is based on the logic “the simpler is always better than the more complex, if it leads to the same results”. Such an approach significantly limits the model risk and substantially decreases computational burden. Robustness checks show that IVRVSRI is a precise measure of the current systemic risk in the stock markets. Moreover, IVRVSRI seems to be a valid indication of current systemic risk in equity markets and it can be used for other types of assets and high-frequency data.
    Keywords: systemic risk, implied volatility, realized volatility, volatility indices, equity index options, market volatility
    JEL: G14 G15 C61 C22
    Date: 2023
  5. By: Christian Keller; Michael Tseng
    Abstract: A theory of price discovery across derivative markets with respect to higher-order information is obtained, via a model where an informed agent trades a complete set of state-contingent claims under general information asymmetry. In an equivalent options formulation, the informed agent has private information regarding arbitrary aspects of the payoff distribution of an underlying asset and trades a complete menu of options, with no assumption on the possible payoff distributions or the nature of private information. We characterize, in closed form, the informed demand, price impact, and information efficiency of prices. Our results contain a theory of insider trading on higher-order moments of the underlying payoff as a special case. The informed demand formula prescribes options strategies for trading on any given moment and extends those already used in practice for, e.g.~volatility trading. The volatility smile is explained by an "insider smile" of implied volatilities.
    Date: 2023–02
  6. By: Alexander Lipton; Adil Reghai
    Abstract: Local Stochastic Volatility (LSV) models have been used for pricing and hedging derivatives positions for over twenty years. An enormous body of literature covers analytical and numerical techniques for calibrating the model to market data. However, the literature misses a potent approach commonly used in physics and works with absolute (dimensional) variables rather than with relative (non-dimensional) ones. While model parameters defined in absolute terms are counter-intuitive for trading desks and tend to be heavily time-dependent, relative parameters are intuitive and stable, making it easy to steer the model adequately and consistently with its Profit and Loss (PnL) explanation power. We propose a specification that first explores historical data and uses physically well-defined relative quantities to design the model. We then develop an efficient hybrid method to price derivatives under this specification. We also show how our method can be used for robust scenario generation purposes - an important risk management task vital for buy-side firms.\footnote{The authors would like to thank Prof. Marcos Lopez de Prado and Dr. Vincent Davy Zoonekynd for valuable comments.}
    Date: 2023–02
  7. By: Lin William Cong; Nathan Darden George; Guojun Wang
    Abstract: We document that value-to-price, the ratio of Residual-Income-Model-based valuation to market price, subsumes the power of book-to-market ratio and many other value or quality measures in predicting stock returns. Long-short value-to-price portfolios hedge against momentum, revitalize the seemingly missing value premium over past decades, and generate significant returns after adjusting for common factors. The value-price-divergence (VPD) factor constructed from the average returns of these portfolios within small and big stocks is not spanned by these known factors. Max Sharpe ratio and constrained R-squared tests reveal that VPD is a better substitute for the traditional value factor and a four-factor model using the VPD, market, momentum, and size factors outperforms most extant benchmarks in explaining the cross-section of expected equity returns. The findings remain robust under alternative specifications of equity cost of capital.
    JEL: C52 G11 G12 M41
    Date: 2023–02
  8. By: Martin Vesely
    Abstract: Portfolio optimization is an inseparable part of strategic asset allocation at the Czech National Bank. Quantum computing is a new technology offering algorithms for that problem. The capabilities and limitations of quantum computers with regard to portfolio optimization should therefore be investigated. In this paper, we focus on applications of quantum algorithms to dynamic portfolio optimization based on the Markowitz model. In particular, we compare algorithms for universal gate-based quantum computers (the QAOA, the VQE and Grover adaptive search), single-purpose quantum annealers, the classical exact branch and bound solver and classical heuristic algorithms (simulated annealing and genetic optimization). To run the quantum algorithms we use the IBM QuantumTM gate-based quantum computer. We also employ the quantum annealer offered by D-Wave. We demonstrate portfolio optimization on finding the optimal currency composition of the CNB's FX reserves. A secondary goal of the paper is to provide staff of central banks and other financial market regulators with literature on quantum optimization algorithms, because financial firms are active in finding possible applications of quantum computing.
    Keywords: Foreign exchange reserves, portfolio optimization, quadratic unconstrained binary optimization, quantum computing
    JEL: C61 C63 G11
    Date: 2023–02
  9. By: Naresh Kumar Devulapally; Tulasi Narendra Das Tripurana
    Abstract: Our study focuses on determining the presence of abnormal returns for physical momentum portfolios in the context of the Indian market. The physical momentum portfolios, comprising stocks from the NSE 500, are constructed for the daily, weekly, monthly, and yearly timescales. In the aforementioned timescales, we empirically evaluate the historical returns and varied risk profiles of these portfolios for the years 2014-2021. It has been observed that the best-performing physical momentum portfolios from each of the four timescales achieved higher returns and better risk measures when compared to the benchmark NIFTY 50 portfolio. We further find that the high-frequency daily time scale exhibits the strongest reversal in the physical momentum effect, wherein the portfolio yielded a 16-fold profit over the initial investment.
    Date: 2023–02

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