nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒08‒21
seven papers chosen by
Kwang Soo Cheong, Johns Hopkins University


  1. The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis By Apostolos Ampountolas
  2. Systemic risk indicator based on implied and realized volatility By Pawe{\l} Sakowski; Rafa{\l} Sieradzki; Robert \'Slepaczuk
  3. To Lend or Not to Lend: The Bank of Japan’s ETF Purchase Program and Securities Lending By Mitsuru Katagiri; Junnosuke Shino; Koji Takahashi
  4. Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation By Chung I Lu
  5. Effects of extreme temperature on the European equity market By Bellocca, Gian Pietro Enzo; Alessi, Lucia; Poncela Blanco, Maria Pilar; Ruiz Ortega, Esther
  6. Central Bank Digital Currency Adoption: A Two-Sided Model By Brandon Tan
  7. Machine learning for option pricing: an empirical investigation of network architectures By Laurens Van Mieghem; Antonis Papapantoleon; Jonas Papazoglou-Hennig

  1. By: Apostolos Ampountolas
    Abstract: This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish-Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model's results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets' sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR's highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.09137&r=fmk
  2. By: Pawe{\l} Sakowski; Rafa{\l} Sieradzki; Robert \'Slepaczuk
    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 2023 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 that the simpler is always better than the more complex if it leads to the same results. Such an approach significantly limits model risk and substantially decreases computational burden. Robustness checks show that IVRVSRI is a precise and valid measure of the current systemic risk in the stock markets. Moreover, it can be used for other types of assets and high-frequency data. The forecasting ability of various SRIs (including CATFIN, CISS, IVRVSRI, SRISK, and Cleveland FED) with regard to weekly returns of S&P 500 index is evaluated based on the simple linear, quasi-quantile, and quantile regressions. We show that IVRVSRI has the strongest predicting power among them.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.05719&r=fmk
  3. By: Mitsuru Katagiri (Department of Business Administration, Hosei University); Junnosuke Shino (School of International Liberal Studies, Waseda University); Koji Takahashi (Monetary and Economic Department, Bank for International Settlements)
    Abstract: This study investigates the effects of the Bank of Japan’s (BOJ) exchange-traded fund (ETF) purchase program on stock returns, particularly focusing on the role of the stock lending market. Using firm-level panel data, we find that the BOJ’s purchases raised stock returns more for those stocks with limited availability in the stock lending market. Nonetheless, over the longer term, the BOJ’s accumulated purchases lowered lending fees and weakened the effects of their purchases on stock returns. This result suggests that ETF managers supply stocks that constitute ETFs held by the BOJ to the stock lending market, which weakens the policy effects of the program.
    Keywords: Large-scale asset purchase (LSAP); ETF purchase program; stock lending market; Bank of Japan
    JEL: E58 G12 G14
    Date: 2023–08
    URL: http://d.repec.org/n?u=RePEc:wap:wpaper:2304&r=fmk
  4. By: Chung I Lu
    Abstract: We evaluate benchmark deep reinforcement learning (DRL) algorithms on the task of portfolio optimisation under a simulator. The simulator is based on correlated geometric Brownian motion (GBM) with the Bertsimas-Lo (BL) market impact model. Using the Kelly criterion (log utility) as the objective, we can analytically derive the optimal policy without market impact and use it as an upper bound to measure performance when including market impact. We found that the off-policy algorithms DDPG, TD3 and SAC were unable to learn the right Q function due to the noisy rewards and therefore perform poorly. The on-policy algorithms PPO and A2C, with the use of generalised advantage estimation (GAE), were able to deal with the noise and derive a close to optimal policy. The clipping variant of PPO was found to be important in preventing the policy from deviating from the optimal once converged. In a more challenging environment where we have regime changes in the GBM parameters, we found that PPO, combined with a hidden Markov model (HMM) to learn and predict the regime context, is able to learn different policies adapted to each regime. Overall, we find that the sample complexity of these algorithms is too high, requiring more than 2m steps to learn a good policy in the simplest setting, which is equivalent to almost 8, 000 years of daily prices.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.07694&r=fmk
  5. By: Bellocca, Gian Pietro Enzo; Alessi, Lucia; Poncela Blanco, Maria Pilar; Ruiz Ortega, Esther
    Abstract: The increasing frequency and severity of extreme temperatures are potential threats to financial stability. Indeed, physical risk related to these extreme phenomena can affect the whole financial system and, in particular, the equity market. In this study, we analyze the impact of extreme temperature exposure on firms' performance in Europe over the XXI century. We show that extreme temperatures can affect firms' profitability depending on their industry and the quarter of the year. Our results are of interest for both investors operating in the equity market and for regulators in charge of securing financial stability.
    Keywords: Climate Change; Equity Market; Firm Performance; Physical Risk; Temperatures
    JEL: C23 C55 G12 G14 Q54
    Date: 2023–07–24
    URL: http://d.repec.org/n?u=RePEc:cte:wsrepe:37973&r=fmk
  6. By: Brandon Tan
    Abstract: For central bank digital currencies (CBDCs) to accomplish their intended objectives, it is necessary for both consumers to use them and for merchants to accept them. This paper develops a dynamic two-sided payments model with both heterogeneous households and merchants/firms to study: (1) The adoption of CBDC by households and firms, and (2) The impact of CBDC issuance on financial inclusion, informality, and disintermediation. Our model shows that there is a feedback loop where more households will adopt CBDC if more firms accept CBDC and vice versa -- incentivizing both households and firms will result in greater levels of take-up. Households are more likely to adopt CBDC if it is low cost, provides an attractive savings vehicle, reduces the cost of remittances, improves the efficiency of government payments, and (if accepted by merchants) offers a valuable means of payment. Firms are more likely to accept CBDC if fees are low, if there are tax exemptions or subsidies for transactions made in CBDC, and if households who prefer to make payments with CBDC make up a large share of revenue. Upon CBDC issuance, an economy can get stuck at a steady state with low CBDC adoption and small welfare gains if the features of CBDC which do not rely on merchant acceptance (remuneration, efficiency of cross border and government payments) are not sufficiently attractive, or if the households benefiting from these features make up a small share of merchant revenue. Temporary subsidies and using CBDC for government payments can spur initial take-up to transition an economy to a welfare improving steady state with high(er) CBDC usage. Greater adoption of CBDC will result in greater financial inclusion and formalization, but potentially the disintermediation of banks and card payments. Thus, there is a trade-off in designing CBDC for greater adoption. However, the gains are more likely to outweigh the risks in lower income economies with larger unbanked populations and informal sectors.
    Keywords: Central bank digital currency; financial inclusion; informality; digital money; disintermediation; two-sided market; adoption; payments
    Date: 2023–06–16
    URL: http://d.repec.org/n?u=RePEc:imf:imfwpa:2023/127&r=fmk
  7. By: Laurens Van Mieghem; Antonis Papapantoleon; Jonas Papazoglou-Hennig
    Abstract: We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons used for learning the function mapping inputs to outputs. In this article, motivated by methods in image classification and recent advances in machine learning methods for PDEs, we investigate empirically whether and how the choice of network architecture affects the accuracy and training time of a machine learning algorithm. We find that for option pricing problems, where we focus on the Black--Scholes and the Heston model, the generalized highway network architecture outperforms all other variants, when considering the mean squared error and the training time as criteria. Moreover, for the computation of the implied volatility, after a necessary transformation, a variant of the DGM architecture outperforms all other variants, when considering again the mean squared error and the training time as criteria.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.07657&r=fmk

This nep-fmk issue is ©2023 by Kwang Soo Cheong. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at http://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.