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on Risk Management |
| By: | Brahmana, Rayenda Khresna |
| Abstract: | The emergence of cryptocurrencies as digital investments drives scholars to explore their predictive prices. Intriguingly, most research focuses on its price and returns prediction using various models, leaving out the importance of persistent risk for portfolio management. This is not to mention that most research focuses only on Bitcoin, neglecting other altcoins and stablecoins. Therefore, this study comprehensively examines the cryptocurrency investment’s persistent risk from the forecasting point of view. We focus on comparing the best forecasting methods because they are vital for volatility-targeting and risk-parity in portfolio strategy. Four time-series model performances will be compared to select a suitable volatility prediction model: Machine Learning-Based GARCH, Machine Learning-Based SVR-GARCH, Neural Network, and Deep Learning. Using six different cryptocurrencies proxies: Bitcoin, Ethereum, Ripple, USD Coin, Tether, and Binance Coin, we found that ML-Based SVR-GARCH outperformed the peers in volatility forecasting. However, the prediction accuracy differences among all models are not significant. Finally, our paper provides new insights into machine learning methods’ applications in cryptocurrency market volatility prediction, which is helpful for academics, policy-makers, and investors in forming portfolio strategies. |
| Keywords: | Volatility Forecasting; Cryptocurrencies; Bitcoin; SVR-GARCH; Neural Network; Deep Learning |
| JEL: | C53 G17 G32 |
| Date: | 2022–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119598&r=rmg |
| By: | Dennis Koch Vahidin Jeleskovic; Zahid I. Younas |
| Abstract: | This paper introduces a unique and valuable research design aimed at analyzing Bitcoin price volatility. To achieve this, a range of models from the Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model classes are considered and their out-of-sample forecasting performance is thoroughly examined. The paper provides insights into the rationale behind the recommendation for a two-stage estimation approach, emphasizing the separate estimation of coefficients in the mean and variance equations. The results presented in this paper indicate that Stochastic Volatility models, particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin price volatility. Moreover, the study suggests that in certain situations, persistent simple GARCH models may even outperform Markov-Switching GARCH models in predicting the variance of Bitcoin log returns. These findings offer valuable guidance for risk management experts, highlighting the potential advantages of SARV models in managing and forecasting Bitcoin price volatility. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03393&r=rmg |
| By: | Cristina Chinazzo; Vahidin Jeleskovic |
| Abstract: | This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's crucial to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme moneyness or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02049&r=rmg |
| By: | Elisa Al\`os; Eulalia Nualart; Makar Pravosud |
| Abstract: | In this paper we study short-time behavior of the at-the-money implied volatility for Inverse and Quanto Inverse European options with fixed strike price. The asset price is assumed to follow a general stochastic volatility process. Using techniques of the Malliavin calculus such as the anticipating Ito's formula we first compute the level of the implied volatility of the option when the maturity converges to zero. Then, we find a short maturity asymptotic formula for the skew of the implied volatility that depends on the roughness of the volatility model. We apply our general results to the SABR and fractional Bergomi models, and provide some numerical simulations that confirm the accurateness of the asymptotic formula for the skew. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.00539&r=rmg |
| By: | Jean-Gabriel Lauzier; Liyuan Lin; Ruodu Wang |
| Abstract: | We analyze the problem of optimally sharing risk using allocations that exhibit counter-monotonicity, the most extreme form of negative dependence. Counter-monotonic allocations take the form of either "winner-takes-all" lotteries or "loser-loses-all" lotteries, and we respectively refer to these (normalized) cases as jackpot or scapegoat allocations. Our main theorem, the counter-monotonic improvement theorem, states that for a given set of random variables that are either all bounded from below or all bounded from above, one can always find a set of counter-monotonic random variables such that each component is greater or equal than its counterpart in the convex order. We show that Pareto optimal allocations, if they exist, must be jackpot allocations when all agents are risk seeking. We essentially obtain the opposite when all agents have discontinuous Bernoulli utility functions, as scapegoat allocations maximize the probability of being above the discontinuity threshold. We also consider the case of rank-dependent expected utility (RDU) agents and find conditions which guarantee that RDU agents prefer jackpot allocations. We provide an application for the mining of cryptocurrencies and show that in contrast to risk-averse miners, RDU miners with small computing power never join a mining pool. Finally, we characterize the competitive equilibria with risk-seeking agents, providing a first and second fundamental theorem of welfare economics where all equilibrium allocations are jackpot allocations. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03328&r=rmg |
| By: | Tahir Choulli; Emmanuel Lepinette |
| Abstract: | In this paper, we consider the discrete-time setting, and the market model described by (S, F, T)$. Herein F is the ``public" flow of information which is available to all agents overtime, S is the discounted price process of d-tradable assets, and T is an arbitrary random time whose occurrence might not be observable via F. Thus, we consider the larger flow G which incorporates F and makes T an observable random time. This framework covers the credit risk theory setting, the life insurance setting and the setting of employee stock option valuation. For the stopped model (S^T, G) and for various vulnerable claims, based on this model, we address the super-hedging pricing valuation problem and its intrinsic Immediate-Profit arbitrage (IP hereafter for short). Our first main contribution lies in singling out the impact of change of prior and/or information on conditional essential supremum, which is a vital tool in super-hedging pricing. The second main contribution consists of describing as explicit as possible how the set of super-hedging prices expands under the stochasticity of T and its risks, and we address the IP arbitrage for (S^T, G) as well. The third main contribution resides in elaborating as explicit as possible pricing formulas for vulnerable claims, and singling out the various informational risks in the prices' dynamics. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05713&r=rmg |
| By: | Xolani Sibande; Alistair Milne |
| Abstract: | This study investigates the impact of the Basel III capital requirement on the supply of bank credit in South Africa. The literature offers greatly varying estimates of the impact of bank capital requirements on loan supply. Using a specification closely modelled on a related study of Peru by Fang et al. (2020), we report panel regressions using monthly balance sheet data for the four biggest banks in South Africa. We distinguish between three different categories of bank lending for household and corporate borrowers and report complementary local projection estimates to capture dynamic impacts. We find little evidence that the introduction of higher capital requirements under Basel III has reduced the supply of bank credit in South Africa. We surmise that this is mainly due to the large banks being well capitalised and operating with capital buffers that are larger than regulatory minimum requirements. |
| Date: | 2024–01–29 |
| URL: | http://d.repec.org/n?u=RePEc:rbz:wpaper:11055&r=rmg |
| By: | Hoang Nguyen; Audron\.e Virbickait\.e; M. Concepci\'on Aus\'in; Pedro Galeano |
| Abstract: | In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systematic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03443&r=rmg |
| By: | Ilke Aydogan (IÉSEG School Of Management [Puteaux]); Loïc Berger (CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]); Valentina Bosetti (Bocconi University [Milan, Italy], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]) |
| Abstract: | We report the results of two experiments designed to better understand the mechanisms driving decision-making under ambiguity. We elicit individual preferences over different sources of uncertainty, entailing different degrees of complexity, from subjects with different sophistication levels. We show that (1) ambiguity aversion is robust to sophistication, but the strong relationship previously reported between attitudes toward ambiguity and compound risk is not. (2) Ellsberg ambiguity attitude can be partly explained by attitudes toward complexity for less sophisticated subjects only. Overall, regardless of the subject's sophistication level, the main driver of Ellsberg ambiguity attitude is a specific treatment of unknown probabilities. |
| Date: | 2023–07–24 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04370668&r=rmg |
| By: | Zhang Chern Lee; Wei Yun Tan; Hoong Khen Koo; Wilson Pang |
| Abstract: | Our article is focused on the application of Markowitz Portfolio Theory and the Single Index Model on 10-year historical monthly return data for 10 stocks included in FTSE Bursa Malaysia KLCI, which is also our market index, as well as a risk-free asset which is the monthly fixed deposit rate. We will calculate the minimum variance portfolio and maximum Sharpe portfolio for both the Markowitz model and Single Index model subject to five different constraints, with the results presented in the form of tables and graphs such that comparisons between the different models and constraints can be made. We hope this article will help provide useful information for future investors who are interested in the Malaysian stock market and would like to construct an efficient investment portfolio. Keywords: Markowitz Portfolio Theory, Single Index Model, FTSE Bursa Malaysia KLCI, Efficient Portfolio |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05264&r=rmg |
| By: | Braun, Alexander; Braun, Julia; Weigert, Florian |
| Abstract: | We examine if extreme weather exposure impacts firms' cost of equity. Motivated by a consumption-based asset pricing model with heterogeneous agents, we reveal the existence of an extreme weather risk premium in the cross-section of stock returns. In the period from 1995 to 2019, domestic U.S. stocks with the most negative sensitivity to thunderstorm losses earned excess returns of 6.5% p.a. over those with the most positive sensitivity. This premium can neither be explained by risk factors from standard asset pricing models nor by firm characteristics. Our results reveal a novel link between climate risk and firm value. |
| Keywords: | Extreme Weather Risk, Climate Risk, Cost of Equity, Empirical Asset Pricing |
| JEL: | C12 G01 G11 G12 G17 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:281206&r=rmg |
| By: | Yue Chen; Mohan Li |
| Abstract: | When analyzing the components influencing the stock prices, it is commonly believed that economic activities play an important role. More specifically, asset prices are more sensitive to the systematic economic news that impose a pervasive effect on the whole market. Moreover, the investors will not be rewarded for bearing idiosyncratic risks as such risks are diversifiable. In the paper Economic Forces and the Stock Market 1986, the authors introduced an attribution model to identify the specific systematic economic forces influencing the market. They first defined and examined five classic factors from previous research papers: Industrial Production, Unanticipated Inflation, Change in Expected Inflation, Risk Premia, and The Term Structure. By adding in new factors, the Market Indices, Consumptions and Oil Prices, one by one, they examined the significant contribution of each factor to the stock return. The paper concluded that the stock returns are exposed to the systematic economic news, and they are priced with respect to their risk exposure. Also, the significant factors can be identified by simply adopting their model. Driven by such motivation, we conduct an attribution analysis based on the general framework of their model to further prove the importance of the economic factors and identify the specific identity of significant factors. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.04132&r=rmg |
| By: | Christoph Moehr |
| Abstract: | This paper sets out a framework for the valuation of insurance liabilities that is intended to be economically realistic, elementary, reasonably practically applicable, and as a special case to provide a basis for the valuation in regulatory solvency systems such as Solvency II and the SST. The valuation framework is based on the cost of producing the liabilities to an insurance company that is subject to solvency regulation (regulatory solvency capital requirements) and insolvency laws (consequences of failure) in finite discrete time. Starting from the replication approach of classical no-arbitrage theory, the framework additionally considers the nature and cost of capital (expressed by a ``financiability condition"), that the liabilities may be required to be fulfilled only ``in sufficiently many cases" (expressed by a ``fulfillment condition"), production using ``fully illiquid" assets in addition to tradables, and the asymmetry between assets and liabilities. We identify necessary and sufficient conditions on the capital investment under which the framework recovers the market prices of tradables, investigate extending production to take account of insolvency, implications of using illiquid assets in the production, and show how Solvency II and SST valuation can be derived with specific assumptions. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.00263&r=rmg |
| By: | Smart, Shanike J. (Binghamton University, New York); Polachek, Solomon (Binghamton University, New York) |
| Abstract: | We assess whether the COVID-19 vaccine induces COVID-19 risky behavior (e.g., going to bars and restaurants) and thus reduces vaccine efficacy. A key empirical challenge is the endogeneity bias when comparing risk-taking by vaccination status since people choose whether to get vaccinated. To address this bias, we exploit rich survey panel data on individuals followed before and after vaccine availability over 14 months in an event study fixed effects model with individual, time, sector, and county-by-time fixed effects and inverse propensity weights. We find evidence that vaccinated persons, regardless of the timing of vaccination, increase their risk-taking by increasing engagement in some risk-taking activities. The evidence is consistent with the "lulling effect". While vaccine availability may reduce the risk of contracting COVID-19, it also contributes to further spread of the virus by incentivizing risk-taking in the short term. |
| Keywords: | vaccine, risk-taking, COVID-19, lulling effect |
| JEL: | I1 I12 I13 I18 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16707&r=rmg |
| By: | Fruet Dias, Gustavo; Papailias, Fotis; Scherrer, Cristina |
| Abstract: | We investigate information processing in the stochastic process driving stock’s volatility (volatility discovery). We apply fractionally cointegration techniques to decompose the estimates of the market-specific integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component. The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identification of the integrated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-fill asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identifies a distinct information process than that based on the price discovery analysis. |
| Keywords: | double asymptotics; fractionally cointegrated vector autoregressive model; high-frequency data; long memory; market microstructure; price discovery; realized measures |
| JEL: | C1 J1 |
| Date: | 2023–12–15 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:121363&r=rmg |
| By: | Stephen Boyd; Kasper Johansson; Ronald Kahn; Philipp Schiele; Thomas Schmelzer |
| Abstract: | More than seventy years ago Harry Markowitz formulated portfolio construction as an optimization problem that trades off expected return and risk, defined as the standard deviation of the portfolio returns. Since then the method has been extended to include many practical constraints and objective terms, such as transaction cost or leverage limits. Despite several criticisms of Markowitz's method, for example its sensitivity to poor forecasts of the return statistics, it has become the dominant quantitative method for portfolio construction in practice. In this article we describe an extension of Markowitz's method that addresses many practical effects and gracefully handles the uncertainty inherent in return statistics forecasting. Like Markowitz's original formulation, the extension is also a convex optimization problem, which can be solved with high reliability and speed. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05080&r=rmg |
| By: | Yue Chen; Xingyi Andrew; Salintip Supasanya |
| Abstract: | Historically, the economic recession often came abruptly and disastrously. For instance, during the 2008 financial crisis, the SP 500 fell 46 percent from October 2007 to March 2009. If we could detect the signals of the crisis earlier, we could have taken preventive measures. Therefore, driven by such motivation, we use advanced machine learning techniques, including Random Forest and Extreme Gradient Boosting, to predict any potential market crashes mainly in the US market. Also, we would like to compare the performance of these methods and examine which model is better for forecasting US stock market crashes. We apply our models on the daily financial market data, which tend to be more responsive with higher reporting frequencies. We consider 75 explanatory variables, including general US stock market indexes, SP 500 sector indexes, as well as market indicators that can be used for the purpose of crisis prediction. Finally, we conclude, with selected classification metrics, that the Extreme Gradient Boosting method performs the best in predicting US stock market crisis events. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.06172&r=rmg |