nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒02‒14
nine papers chosen by

  1. Exact Post-selection Inference For Tracking S&P500 By Farshad Noravesh; Hamid Boustanifar
  2. Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach By Iuri H. Ferreira; Marcelo C. Medeiros
  3. The repo market under Basel III By Gerba, Eddie; Katsoulis, Petros
  4. Skewness Expectations and Portfolio Choice By Tilman H. Drerup; Matthias Wibral; Christian Zimpelmann
  5. New volatility evolution model after extreme events By Mei-Ling Cai; Zhang-HangJian Chen; Sai-Ping Li; Xiong Xiong; Wei Zhang; Ming-Yuan Yang; Fei Ren
  6. Measuring and stress-testing market-implied bank capital By Martin Indergand; Eric Jondeau; Andreas Fuster
  7. Dimensionality reduction for prediction: Application to Bitcoin and Ethereum By Hugo Inzirillo; Benjamin Mat
  8. Hedging cryptos with Bitcoin futures By Liu, Francis; Packham, Natalie; Lu, Meng-Jou; Härdle, Wolfgang
  9. A Survey of Quantum Computing for Finance By Dylan Herman; Cody Googin; Xiaoyuan Liu; Alexey Galda; Ilya Safro; Yue Sun; Marco Pistoia; Yuri Alexeev

  1. By: Farshad Noravesh; Hamid Boustanifar
    Abstract: The problem that is solved in this paper is known as index tracking. The method of Lasso is used to reduce the dimensions of S&P500 index which has many applications in both investment and portfolio management algorithms. The novelty of this paper is that post-selection inference is used to have better modeling and inference for Lasso approach to index tracking. Both confidence intervals and curves indicate that the performance of Lasso type method for dimension reduction of S&P500 is remarkably high. Keywords: index tracking, lasso, post-selection inference, S&P500
    Date: 2021–12
  2. By: Iuri H. Ferreira; Marcelo C. Medeiros
    Abstract: In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the strongest candidate predictor for intraday market returns in our analysis, specially when implemented through the LSTM model. This model also improves significantly the performance of the lagged market return as predictive variable. Finally, intraday RF estimation outputs indicate that there is no performance improvement with this method, and it may even worsen the results in some cases.
    Date: 2021–12
  3. By: Gerba, Eddie (Bank of England); Katsoulis, Petros (Bank of England)
    Abstract: This paper assesses the impact of banking regulation (Basel III) on financial market dynamics using the repo market as an important case study. To this end, we use unique proprietary data sets from the Bank of England to examine the individual and joint impact of leverage, capital and liquidity coverage ratios on participants’ trading in all collateral segments of the UK repo market. We find non-uniform effects across ratios and participants and non-linear effects across time. For instance, we find that the leverage ratio induces participants to charge lower (higher) interest margins on repo (reverse repo) trades that are non-nettable compared to the nettable ones. Second,we document a change in market microstructure under the new regulatory regime. Specifically, we evidence a substitution effect of banks’ long-term repo borrowing backed by gilts from dealers to investment funds which can be fragile during times of stress. Likewise, we find an increasing prominence of central counterparties. Third, we find evidence that participants who are jointly constrained by multiple ratios and closer to the regulatory thresholds during times of stress reduce their activity to a greater extent than those that are constrained by a single ratio or not constrained, with implications for market liquidity.
    Keywords: Banking regulation; repo market; market microstructure; liquidity; monetary policy transmission
    JEL: E44 E52 G11 G21 G28
    Date: 2021–12–17
  4. By: Tilman H. Drerup; Matthias Wibral; Christian Zimpelmann
    Abstract: Many models of investor behavior predict that investors prefer assets that they believe to have positively skewed return distributions. We provide a direct test of this prediction in a representative sample of the Dutch population. Using individuallevel data on return expectations for a broad index and a single stock, we show that portfolio allocations increase with the skewness of respondents’ return expectations for the respective asset, controlling for other moments of a respondent’s expectations and sociodemographic information. We also show that while an individual’s expectations are correlated across assets, sociodemographics only capture very little of the substantial heterogeneity in expectations.
    Keywords: Skewness, Stock Market Expectations, Portfolio Choice, Behavioral Finance
    JEL: D14 D84 G02 G11
    Date: 2022–02
  5. By: Mei-Ling Cai; Zhang-HangJian Chen; Sai-Ping Li; Xiong Xiong; Wei Zhang; Ming-Yuan Yang; Fei Ren
    Abstract: In this paper, we propose a new dynamical model to study the two-stage volatility evolution of stock market index after extreme events, and find that the volatility after extreme events follows a stretched exponential decay in the initial stage and becomes a power law decay at later times by using high frequency minute data. Empirical study of the evolutionary behaviors of volatility after endogenous and exogenous events further demonstrates the descriptive power of our new model. To further explore the underlying mechanisms of volatility evolution, we introduce the sequential arrival of information hypothesis (SAIH) and the mixture of distribution hypothesis (MDH) to test the two-stage assumption, and find that investors transform from the uninformed state to the informed state in the first stage and informed investors subsequently dominate in the second stage. The testing results offer a supporting explanation for the validity of our new model and the fitted values of relevant parameters.
    Date: 2022–01
  6. By: Martin Indergand; Eric Jondeau; Andreas Fuster
    Abstract: We propose a methodology for measuring the market-implied capital of banks by subtracting from the market value of equity (market capitalization) a credit spread-based correction for the value of shareholders' default option. We show that without such a correction, the estimated impact of a severe market downturn is systematically distorted, underestimating the risk of banks with low market capitalization. We argue that this adjusted measure of capital is the relevant market-implied capital measure for policymakers. We propose an econometric model for the combined simulation of equity prices and CDS spreads, which allows us to introduce this correction in the SRISK framework for measuring systemic risk.
    Keywords: Banking, capital, stress test, systemic risk, multifactor model
    JEL: C32 G01 G21 G28 G32
    Date: 2022
  7. By: Hugo Inzirillo; Benjamin Mat
    Abstract: The objective of this paper is to assess the performances of dimensionality reduction techniques to establish a link between cryptocurrencies. We have focused our analysis on the two most traded cryptocurrencies: Bitcoin and Ethereum. To perform our analysis, we took log returns and added some covariates to build our data set. We first introduced the pearson correlation coefficient in order to have a preliminary assessment of the link between Bitcoin and Ethereum. We then reduced the dimension of our data set using canonical correlation analysis and principal component analysis. After performing an analysis of the links between Bitcoin and Ethereum with both statistical techniques, we measured their performance on forecasting Ethereum returns with Bitcoin s features.
    Date: 2021–12
  8. By: Liu, Francis; Packham, Natalie; Lu, Meng-Jou; Härdle, Wolfgang
    Abstract: The introduction of derivatives on Bitcoin enables investors to hedge risk exposures in cryptocurrencies. Because of volatility swings and jumps in cryptocurrency prices, the traditional variance-based approach to obtain hedge ratios is infeasible. As a consequence, we consider two extensions of the traditional approach: first, different dependence structures are modelled by different copulae, such as the Gaussian, Student-t, Normal Inverse Gaussian and Archimedean copulae; second, different risk measures, such as value-at-risk, expected shortfall and spectral risk measures are employed to and the optimal hedge ratio. Extensive out-of-sample tests give insights in the practice of hedging various cryptos and crypto indices, including Bitcoin, Ethereum, Cardano, the CRIX index and a number of crypto-portfolios in the time period December 2017 until May 2021. Evidences show that BTC futures can effectively hedge BTC and BTC-involved indices. This promising result is consistent across different risk measures and copulae except for Frank. On the other hand, we observe complex and diverse dependence structures between BTC-not-involved assets and the futures. As a consequence, results of hedging other assets and indices are diverse and, in some occasions, not ideal.
    Keywords: Cryptocurrencies,risk management,hedging,copulas
    JEL: G11 G13
    Date: 2021
  9. By: Dylan Herman; Cody Googin; Xiaoyuan Liu; Alexey Galda; Ilya Safro; Yue Sun; Marco Pistoia; Yuri Alexeev
    Abstract: Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on Monte Carlo integration, optimization, and machine learning, showing how these solutions, adapted to work on a quantum computer, can help solve more efficiently and accurately problems such as derivative pricing, risk analysis, portfolio optimization, natural language processing, and fraud detection. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.
    Date: 2022–01

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