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on Risk Management |
| By: | Yujuan Qiu |
| Abstract: | This thesis evaluates most of the extreme mixture models and methods that have appended in the literature and implements them in the context of finance and insurance. The paper also reviews and studies extreme value theory, time series, volatility clustering, and risk measurement methods in detail. Comparing the performance of extreme mixture models and methods on different simulated distributions shows that the method based on kernel density estimation does not have an absolute superior or close to the best performance, especially for the estimation of the extreme upper or lower tail of the distribution. Preprocessing time series data using a generalized autoregressive conditional heteroskedasticity model (GARCH) and applying extreme value mixture models on extracted residuals from GARCH can improve the goodness of fit and the estimation of the tail distribution. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.05933 |
| By: | Maysam Khodayari Gharanchaei; Prabhu Prasad Panda; Xilin Chen |
| Abstract: | This paper focuses on the developing of high-dimensional risk models to construct portfolios of securities in the US stock exchange. Investors seek to gain the highest profits and lowest risk in capital markets. We have developed various risk models and for each model different investment strategies are tested. Out of sample tests are performed on a long-term horizon from 1970 until 2023. |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.01550 |
| By: | Maciej Wysocki (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning) |
| Abstract: | This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies using the Black-Scholes-Merton (BSM) model and the Variance-Gamma (VG) model, emphasizing varying moneyness levels and different sizing methods based on delta and the VIX Index. The study employs 1-minute data of S&P500 index options and index quotes spanning from 2018 to 2023. The analysis benchmarks hedged strategies against buy-and-hold and naked option-writing strategies, with a focus on risk-adjusted performance metrics including transaction costs. Portfolio delta approximations are derived using implied volatility for the BSM model and market-calibrated parameters for the VG model. Key findings reveal that system atic option-writing strategies can potentially yield superior returns compared to buy-and-hold benchmarks. The BSM model generally provided better hedging outcomes than the VG model, although the VG model showed profitability in certain naked strategies as a tool for position sizing. In terms of rehedging frequency, we found that intraday heding in 130-minute intervals provided both reliable protection against adverse market movements and a satisfactory returns profile. |
| Keywords: | S&P500 Index options, Option Pricing Models, Black-Scholes-Merton model, Variance-Gamma model, Implied Volatility, Volatility Risk Premium, Volatility Spreads, Dynamic Hedging |
| JEL: | C4 C14 C45 C53 C58 G13 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2024-14 |
| By: | Dimitris Korobilis; Maximilian Schröder |
| Abstract: | We propose a multi country quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series.The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the effects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implement joint risk scenario analysis |
| Keywords: | quantileVAR; MCMC; variational Bayes; dynamic factor model |
| JEL: | C11 C32 E31 E32 E37 E66 |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:gla:glaewp:2023_07 |
| By: | Yuji Shinozaki (Deputy Director, Institute for Monetary and Economic Studies, Bank of Japan (currently, Associate Professor, Musashino University, E-mail:y-shino@musashino-u.ac.jp)) |
| Abstract: | The application of machine learning to the field of finance has recently become the subject of active discussions. In particular, the deep learning is expected to significantly advance the techniques of hedging and calibration. As these two techniques play a central role in financial engineering and mathematical finance, the application to them attracts attentions of both practitioners and researchers. Deep hedging, which applies deep learning to hedging, is expected to make it possible to analyze how factors such as transaction costs affect hedging strategies. Since the impact of these factors was difficult to be assessed quantitatively due to the computational costs, deep hedging opens possibilities not only for refining and automating hedging operations of derivatives but also for broader applications in risk management. Deep calibration, which applies deep learning to calibration, is expected to make the parameter optimization calculation, which is an essential procedure in derivative pricing and risk management, faster and more stable. This paper provides an overview of the existing literature and suggests future research directions from both practical and academic perspectives. Specifically, the paper shows the implications of deep learning to existing theoretical frameworks and practical motivations in finance and identifies potential future developments that deep learning can bring about and the practical challenges. |
| Keywords: | Financial engineering, Mathematical finance, Derivatives, Hedging, Calibration, Numerical optimization |
| JEL: | C63 G12 G13 |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:24-e-02 |
| By: | Battulga Gankhuu |
| Abstract: | In this paper, we developed the Merton's structural model for public companies under an assumption that liabilities of the companies are observed. Using Campbell and Shiller's approximation method, we obtain formulas of risk-neutral equity and liability values and default probabilities for the public companies. Also, the paper provides ML estimators of suggested model's parameters. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.18121 |
| By: | Mario Cerrato; Hormoz Ramian; Shengfeng Mei |
| Abstract: | We show that European firms, at the peak of the COVID-19 shock in 2020:Q2, went into a “panic borrowing” status and drew down €87bn in a very short period. We show that firms that drew down credit lines had less stringent solvency and liquidity constraints. Our study exploits the implications of the social distancing policies to corporate operations across Europe. The novel aspect of our study is that we focus on shocks unrelated to firms’ fundamentals and investigate how firms manage their cash flow risk. It is an important novel aspect of this study as a large part of the literature has studied cash flow risk management follow ingendogenous shocks due to bad management decisions. In doing so, we use COVID-19 infection data and proxies for social distancing policies in Europe as a natural laboratory. Finally, we show that European firms during the pandemic crisis increased drawdowns, on average, by 3.35 percentage points in response to an unexpected one percentage point fall in their cash flows, butonly when firms’earnings are negative.This result is driven by the lockdown policies introduced in Europe. |
| Keywords: | Corporate credit lines, cashholding, investment, default risk |
| JEL: | G21 G32 G33 |
| Date: | 2023–02 |
| URL: | https://d.repec.org/n?u=RePEc:gla:glaewp:2023_05 |
| By: | Dan Pirjol |
| Abstract: | The short maturity limit $T\to 0$ for the implied volatility of an Asian option in the Black-Scholes model is determined by the large deviations property for the time-average of the geometric Brownian motion. In this note we derive the subleading $O(T)$ correction to this implied volatility, using an asymptotic expansion for the Hartman-Watson distribution. The result is used to compute subleading corrections to Asian options prices in a small maturity expansion, sharpening the leading order result obtained using large deviations theory. We demonstrate good numerical agreement with precise benchmarks for Asian options pricing in the Black-Scholes model. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.05142 |
| By: | Shubham Singh |
| Abstract: | The study examines whether broader market factors and the Fama-French three-factor model can effectively analyze the idiosyncratic risk and return characteristics of Bitcoin. By incorporating Fama-french factors, the explanatory power of these factors on Bitcoin's excess returns over various moving average periods is tested. The analysis aims to determine if equity market factors are significant in explaining and modeling systemic risk in Bitcoin. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.19401 |
| By: | Stewart, Shamar L.; Isengildina Massa, Olga |
| Keywords: | Risk And Uncertainty, Demand And Price Analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343936 |
| By: | Nabil Daher (EconomiX - Université Paris Nanterre) |
| Abstract: | This presentation explores the impact of natural disasters on developing countries' GDP growth tail-risk. Using quantile local projections on data for 75 developing economies from 1970–2021, my results reveal that natural disasters lead to a persistent decrease at the 10th percentile of economic growth. In addition, agricultural and industrial growth at the 10th percentile experience signi |
| Date: | 2024–06–29 |
| URL: | https://d.repec.org/n?u=RePEc:boc:fsug24:12 |
| By: | Robert J. Barro (Dept. of Economics, Harvard University); Tao Jin (Dept. of Economics, Harvard University) |
| Abstract: | The coefficient of relative risk aversion is a key parameter for analyses of behavior toward risk, but good estimates of this parameter do not exist. A promising place for reliable estimation is rare macroeconomic disasters, which have a major influence on the equity premium. The premium depends on the probability and size distribution of disasters, gauged by proportionate declines in per capita consumption or gross domestic product. Long-term national-accounts data for 36 countries provide a large sample of disasters of magnitude 10% or more. A power-law density provides a good fit to the size distribution, and the upper-tail exponent, α, is estimated to be around 4. A higher α signifies a thinner tail and, therefore, a lower equity premium, whereas a higher coefficient of relative risk aversion, γ, implies a higher premium. The premium is finite if α > γ. The observed premium of 5% generates an estimated γ close to 3, with a 95% confidence interval of 2 to 4. The results are robust to uncertainty about the values of the disaster probability and the equity premium, and can accommodate seemingly paradoxical situations in which the equity premium may appear to be infinite. |
| Keywords: | Power law, rare disaster, equity premium, risk aversion |
| Date: | 2023 |
| URL: | https://d.repec.org/n?u=RePEc:cuf:wpaper:634 |
| By: | Julian Kozlowski |
| Abstract: | An analysis of inflation expectations suggests that financial market participants see a 50% probability that CPI inflation could be higher than 2.5% over the next five years. |
| Keywords: | inflation; inflation expectations; Consumer Price Index (CPI); financial markets |
| Date: | 2024–07–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:98546 |
| By: | Xue, Huidan; Du, Yuxuan |
| Keywords: | Demand And Price Analysis, Risk And Uncertainty, Agricultural And Food Policy |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343639 |
| By: | Behn, Markus; Forletta, Marco; Reghezza, Alessio |
| Abstract: | Using granular data from the European corporate credit register, we examine how increases in macroprudential capital buffer requirements since the pandemic have affected bank lending behaviour in the euro area. Our findings reveal that, for the average bank, the buffer requirement increases did not have a statistically significant impact on lending to non-financial corporations. Furthermore, while we document relatively slower loan growth for banks with less capital headroom, also these banks did not decrease lending in absolute terms in response to higher requirements. These findings are robustin various specifications and emerge for both loan growth at the bank-firm level and the propensity to establish new bank-firm relationships. At the firm level, we document some heterogeneity depending on firm type and firm size. Firms with a single bank relationship and small and micro enterprises experienced a relative reduction in lending following buffer increases, although substitution effects mitigated real effects at the firm level. Overall, the results suggest that the pronounced macroprudential tightening since late 2021 did not exert substantial negative effects on credit supply.Hence, activating releasable capital buffers at an early stage of the cycle appears to be a robust policy strategy, since the costs of doing so are expected to be low. JEL Classification: E5, E51, G18, G21 |
| Keywords: | bank lending, capital buffers, credit supply, macroprudential policy |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242951 |
| By: | Alex Gershkov (Department of Economics and the Federmann Center for the Study of Rationality, The Hebrew University of Jerusalem & University of Surrey); Benny Moldovanu (Department of Economics, University of Bonn); Philipp Strack (Department of Economics, Yale University); Mengxi Zhang (Department of Economics, University of Bonn) |
| Abstract: | We use the tools of mechanism design, combined with the theory of risk measures, to analyze a model where a cash constrained owner of an asset with stochastic returns raises capital from a population of investors that differ in their risk aversion and budget constraints. The distribution of the asset's cash flow is assumed here to be common-knowledge: no agent has private information about it. The issuer partitions and sells the asset's cash flow into several asset-backed securities, one for each type of investor. The optimal partition conforms to the commonly observed practice of tranching (e.g., senior debt, junior debt and equity) where senior claims are paid before the subordinate ones. The holders of more senior/junior tranches are determined by the relative risk appetites of the different types of investors and of the issuer, with the more risk-averse agents holding the more senior tranches. Tranching endogenously arises here in an optimal mechanism because of simple economic forces: the differences in risk appetites among agents, and in the budget constraints they face. |
| Keywords: | Security Design, Risk Aversion, Tranching, Pooling |
| JEL: | D82 G00 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:325 |
| By: | Hadachek, Jeffrey; Ma, Meilin |
| Keywords: | Risk And Uncertainty, Agricultural And Food Policy, Agribusiness |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343748 |
| By: | Zhou, Mengfei; Merel, Pierre |
| Keywords: | Risk And Uncertainty, Environmental Economics And Policy, Consumer/ Household Economics |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343790 |