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on Risk Management |
| By: | Aadi Gupta; Priya Gulati; Siddhartha P. Chakrabarty |
| Abstract: | In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit derivative based on the idea of a Credit Default Swap, to hedge against an event of default. The results on the test set are presented using various performance measures. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.05136&r=rmg |
| By: | Fernando Moreno-Pino; Stefan Zohren |
| Abstract: | Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions to forecast day-ahead volatility by using high-frequency data. We show that the dilated convolutional filters are ideally suited to extract relevant information from intraday financial data, thereby naturally mimicking (via a data-driven approach) the econometric models which incorporate realised measures of volatility into the forecast. This allows us to take advantage of the abundance of intraday observations, helping us to avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate DeepVol's performance. The reported empirical results suggest that the proposed deep learning-based approach learns global features from high-frequency data, achieving more accurate predictions than traditional methodologies, yielding to more appropriate risk measures. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.04797&r=rmg |
| By: | Arthur Seibold; Sebastian Seitz; Sebastian Siegloch |
| Abstract: | Public disability insurance (DI) programs in many countries face pressure to reduce their generosity in order to remain sustainable. In this paper, we investigate the welfare effects of giving a larger role to private insurance markets in the face of public DI cuts. Exploiting a unique reform that abolished one part of the German public DI system for younger workers, we find that despite significant crowding-in effects, overall private DI take-up remains modest. We do not find any evidence of adverse selection on unpriced risk. On the contrary, private DI tends to be concentrated among high-income, high-education and low-risk individuals. Using a revealed preferences approach, we estimate individual DI valuations, a key input for welfare calculations. We find that observed willingness-to-pay of many individuals is low, such that providing DI partly via a private insurance market with choice improves welfare. However, we show that distributional concerns as well as individual risk misperceptions can provide grounds for justifying a full public DI mandate. |
| Keywords: | disability insurance, social insurance, mandate, privatization, risk-based selection, welfare |
| JEL: | H55 G22 G52 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9979&r=rmg |
| By: | Herings, Jean-Jacques (Tilburg University, School of Economics and Management); Zhan, Yang |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:a8d79048-2351-4e73-97ce-91a84f770e82&r=rmg |
| By: | Joel P. Villarino; \'Alvaro Leitao; Jos\'e A. Garc\'ia-Rodr\'iguez |
| Abstract: | The goal of this work is to develop deep learning numerical methods for solving option XVA pricing problems given by non-linear PDE models. A novel strategy for the treatment of the boundary conditions is proposed, which allows to get rid of the heuristic choice of the weights for the different addends that appear in the loss function related to the training process. It is based on defining the losses associated to the boundaries by means of the PDEs that arise from substituting the related conditions into the model equation itself. Further, automatic differentiation is employed to obtain accurate approximation of the partial derivatives. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.02175&r=rmg |
| By: | Fehrle, Daniel |
| JEL: | C22 C23 E31 E44 G11 N10 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc22:264044&r=rmg |
| By: | Carsten Chong; Marc Hoffmann; Yanghui Liu; Mathieu Rosenbaum; Gr\'egoire Szymanski |
| Abstract: | Rough volatility models have gained considerable interest in the quantitative finance community in recent years. In this paradigm, the volatility of the asset price is driven by a fractional Brownian motion with a small value for the Hurst parameter $H$. In this work, we provide a rigorous statistical analysis of these models. To do so, we establish minimax lower bounds for parameter estimation and design procedures based on wavelets attaining them. We notably obtain an optimal speed of convergence of $n^{-1/(4H+2)}$ for estimating $H$ based on n sampled data, extending results known only for the easier case $H>1/2$ so far. We therefore establish that the parameters of rough volatility models can be inferred with optimal accuracy in all regimes. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.01214&r=rmg |
| By: | Petrovich, Beatrice; Carattini, Stefano; Wüstenhagen, Rolf |
| Abstract: | Households are key actors in decarbonizing our economy, especially when it comes to investments in a decentralized energy system, such as solar photovoltaics (PV). The phasing-out of feed-in tariffs, and unexpected policy changes in the wake of an increasingly polarized climate debate, require residential PV investors to bear new risks. Conducting a discrete choice experiment coupled with a randomized informational treatment among potential residential solar investors in Switzerland, we test whether policy and market risks deter households from investing in solar. We find that salient policy risk reduces households' intention to invest in solar, especially for risk-averse individuals. Conversely, households seem less sensitive to market risk: residential solar investors accept volatile revenues, as long as a price floor for excess electricity sold to the grid is guaranteed. Our study suggests that keeping perceived policy uncertainty low is more important for residential solar investors than fully hedging against electricity market risk. |
| Keywords: | discrete choice experiment; information asymmetries; market risk; policy risk; residential solar investors; risk preferences |
| JEL: | D81 O33 Q42 |
| Date: | 2021–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:108405&r=rmg |
| By: | Yan Dolinsky |
| Abstract: | In this paper we develop the duality theory for the exponential utility maximization problem where trading is subject to quadratic transaction costs and the investor is required to liquidate her position at the maturity date. As an application of the constructed duality theory, we treat utility based hedging in the Bachelier model. For Europeans contingent claims with quadratic payoff we compute explicitly the optimal trading strategy. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.03917&r=rmg |
| By: | Wenfeng He; Xiaoling Mei; Wei Zhong; Huanjun Zhu |
| Abstract: | Multiperiod portfolio choice is the central problem in active asset management. Multi- period dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this paper, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state predictors. Specifically, in the first step, we propose the new Risk-Premium Projected- PCA (RP-PPCA) method to reduce the dimension of tradable assets. This method achieves Dimension Reduction (DR) by estimating latent factors with explanatory power in both time series variation and expected return in high-dimension-low-sample-size data. In the second step, we use dynamic programming to solve the multiperiod portfolio choice problem, and in each recursive step, we adopt an Adjusted semiparametric Model Averaging (AMA) method to avoid the curse of dimensionality associated with a large set of state variables while re- maining computationally efficient. Thus, we name this two-step approach DRAMA, which stands for a combination of a new dimension reduction method and an adjusted semipara- metric model averaging method. Analytically, we show that the portfolios constructed by the DRAMA are approximately optimal under mild assumptions. Moreover, our numerical results based on empirical data from US stock markets show that the proposed portfolios have both excellent in-sample and out-of-sample performances. |
| Date: | 2022–10–26 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:002608&r=rmg |
| By: | Darke, Matthew James (University of Warwick) |
| Abstract: | Developments in Artificial Intelligence and Machine Learning technologies have had massive implications for labour automation. This paper builds on the task-based methodology first adopted by Frey and Osborne (2013) to predict how the risk of automation evolved in the UK labour between 2012 and 2017 using data from the UK Skills and Employment Survey. The analysis accounts for technological progress, making use of two sets of experts’ assessments for 70 occupations. The probability of automation is predicted for each individual using a set of self-reported job skills. It finds that the proportion of jobs at high-risk from automation has risen from 10.6% to 23.4%, and that this is largely due to better technology rather than changing job skill requirements. It also identifies sectors experiencing the greatest increase in automation risk between the two periods and, in contrast, those which appear complementary to technology, drawing on occupational case studies as evidence. |
| Keywords: | Employment ; Skills Demand ; Technology JEL Classification: J01 ; J21 ; J24 ; J62 ; O33 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkesp:41&r=rmg |
| By: | Filippo Ippolito; Alessandro Villa |
| Abstract: | We revisit the relation between equity returns and financial leverage through the lens of a dynamic trade-off model with costly capital structure rebalancing. The model predicts that expected equity returns depend on whether a firm's leverage is above or below its target leverage. We provide empirical evidence in support of the model predictions. Controlling for leverage, overlevered (underlevered) firms earn higher (lower) returns. A quantitative version of our model reproduces key facts about capital structure rebalancing and equity returns for U.S. corporations. Overall, our results indicate that financial flexibility crucially affects the link between leverage and equity returns. |
| Keywords: | Leverage; Cross Section of Returns; Dynamic Capital Structure; Financial Frictions |
| JEL: | G12 G32 |
| Date: | 2022–01–08 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedhwp:94919&r=rmg |
| By: | Alturki,Sultan Abdulaziz M; Hibbert,Ann Marie |
| Abstract: | The paper examines the impact of oil shocks on sovereign credit default swaps (CDS) for the G10 countries and major oil-exporting countries. The results show that oil demand shocks have a uniformly negative impact on CDS spreads. In contrast, oil supply shocks increase the spreads of the G10 countries, but reduce the spreads of oil-exporting countries. Using quantile regressions, the findings show that oil demand shocks affect spreads across the conditional distribution, while oil supply shocks mostly influence the upper quantiles of spread changes. Furthermore, a two-state Markov-switching modeling confirms a significant non-linearity in the impact of oil shocks. |
| Keywords: | Oil&Gas,Energy and Environment,Energy Demand,Energy and Mining,Financial Sector Policy,Public Finance Decentralization and Poverty Reduction,Public Sector Economics,Financial Crisis Management&Restructuring |
| Date: | 2021–02–16 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:9546&r=rmg |