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on Risk Management |
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Issue of 2022‒03‒07
forty-nine papers chosen by |
| By: | Koichi Miyamoto |
| Abstract: | Finance is one of the promising field for industrial application of quantum computing. In particular, quantum algorithms for calculation of risk measures such as the value at risk and the conditional value at risk of a credit portfolio have been proposed. In this paper, we focus on another problem in credit risk management, calculation of risk contributions, which quantify the concentration of the risk on subgroups in the portfolio. Based on the recent quantum algorithm for simultaneous estimation of multiple expected values, we propose the method for credit risk contribution calculation. We also evaluate the query complexity of the proposed method and see that it scales as $\widetilde{O}\left(\sqrt{N_{\rm gr}}/\epsilon\right)$ on the subgroup number $N_{\rm gr}$ and the accuracy $\epsilon$, in contrast with the classical method with $\widetilde{O}\left(\log(N_{\rm gr})/\epsilon^2\right)$ complexity. This means that, for calculation of risk contributions of finely divided subgroups, the advantage of the quantum method is reduced compared with risk measure calculation for the entire portfolio. Nevertheless, the quantum method can be advantageous in high-accuracy calculation, and in fact yield less complexity than the classical method in some practically plausible setting. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11394&r= |
| By: | Jan Rosenzweig |
| Abstract: | We look at optimal liability-driven portfolios in a family of fat-tailed and extremal risk measures, especially in the context of pension fund and insurance fixed cashflow liability profiles, but also those arising in derivatives books such as delta one books or options books in the presence of stochastic volatilities. In the extremal limit, we recover a new tail risk measure, Extreme Deviation (XD), an extremal risk measure significantly more sensitive to extremal returns than CVaR. Resulting optimal portfolios optimize the return per unit of XD, with portfolio weights consisting of a liability hedging contribution, and a risk contribution seeking to generate positive risk-adjusted return. The resulting allocations are analyzed qualitatively and quantitatively in a number of different limits. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.10846&r= |
| By: | Eric C. K. Cheung; Oscar Peralta; Jae-Kyung Woo |
| Abstract: | In this paper, a class of multivariate matrix-exponential affine mixtures with matrix-exponential marginals is proposed. The class is shown to possess various attractive properties such as closure under size-biased Esscher transform, order statistics, residual lifetime and higher order equilibrium distributions. This allows for explicit calculations of various actuarial quantities of interest. The results are applied in a wide range of actuarial problems including multivariate risk measures, aggregate loss, large claims reinsurance, weighted premium calculations and risk capital allocation. Furthermore, a multiplicative background risk model with dependent risks is considered and its capital allocation rules are provided as well. We finalize by discussing a calibration scheme based on complete data and potential avenues of research. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11122&r= |
| By: | Juan Ignacio Pe\~na; Rosa Rodriguez; Silvia Mayoral |
| Abstract: | This paper compares the in-sample and out-of-sample performance of several models for computing the tail risk of one-month and one-year electricity futures contracts traded in the NordPool, French, German, and Spanish markets in 2008-2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk (VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model with Student-t distribution is the best-performing specification with 88% cases in which the Fisher test accepts the model, with a success rate of 94% in the left tail and of 81% in the right tail. The model passes the test of model adequacy in the 100% of the cases in the NordPool and German markets, but only in the 88% and 63% of the cases in the Spanish and French markets. With ES, this model passes the test of model adequacy in 100% of cases in all markets. Historical Simulation and Quantile Regression-based approaches misestimate tail risks. The right-hand tail of the returns is more difficult to model than the left-hand tail and therefore financial regulators and the administrators of futures markets should take these results into account when setting additional regulatory capital requirements and margin account regulations to short positions. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.01732&r= |
| By: | Matthew F. Tomlinson; David Greenwood; Marcin Mucha-Kruczynski |
| Abstract: | Dynamic extreme value analysis offers a promising approach to the forecasting of the extreme tail events that often dominate systemic risk. We extend the two-tailed peaks-over-threshold (2T-POT) Hawkes model as a tool for dynamic quantile forecasting for both the left and right tails of a univariate time series; this is applied to the daily log-returns of six large cap indices using a wide range of exceedance thresholds (from the 1.25% to 25.00% mirrored quantiles). Out-of-sample convergence tests find that the 2T-POT Hawkes model offers more accurate and reliable forecasting of next step ahead extreme left- and right-tail quantiles -- as measured by (mirrored) value-at-risk and expected shortfall at the 2.5% coverage level and below -- compared against GARCH-type models. Quantitatively similar asymmetries in the parameters of the Hawkes arrival process are found across all six indices, adding further empirical support to a temporal leverage effect in which the impact of losses is not only greater but also more immediate. Our results suggest that asymmetric Hawkes-type arrival dynamics are a better approximation of the true data generating process for extreme daily log-returns than GARCH-type variance dynamics and, therefore, that the 2T-POT Hawkes model presents a better performing alternative to GARCH-type models. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.01043&r= |
| By: | Marcin Pitera; Thorsten Schmidt |
| Abstract: | While the estimation of risk is an important question in the daily business of banking and insurance, many existing plug-in estimation procedures suffer from an unnecessary bias. This often leads to the underestimation of risk and negatively impacts backtesting results, especially in small sample cases. In this article we show that the link between estimation bias and backtesting can be traced back to the dual relationship between risk measures and the corresponding performance measures, and discuss this in reference to value-at-risk, expected shortfall and expectile value-at-risk. Motivated by the consistent underestimation of risk by plug-in procedures, we propose a new algorithm for bias correction and show how to apply it for generalized Pareto distributions to the i.i.d.\ setting and to a GARCH(1,1) time series. In particular, we show that the application of our algorithm leads to gain in efficiency when heavy tails or heteroscedasticity exists in the data. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.10454&r= |
| By: | Shi Bo |
| Abstract: | This paper aims to characterize the typical factual characteristics of financial market returns and volatility and address the problem that the tail characteristics of asset returns have been not sufficiently considered, as an attempt to more effectively avoid risks and productively manage stock market risks. Thus, in this paper, the fat-tailed distribution and the leverage effect are introduced into the SV model. Next, the model parameters are estimated through MCMC. Subsequently, the fat-tailed distribution of financial market returns is comprehensively characterized and then incorporated with extreme value theory to fit the tail distribution of standard residuals. Afterward, a new financial risk measurement model is built, which is termed the SV-EVT-VaR-based dynamic model. With the use of daily S&P 500 index and simulated returns, the empirical results are achieved, which reveal that the SV-EVT-based models can outperform other models for out-of-sample data in backtesting and depicting the fat-tailed property of financial returns and leverage effect. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09434&r= |
| By: | Wan-Chien Chiu; Juan Ignacio Pe\~na; Chih-Wei Wang |
| Abstract: | We model systemic risk using a common factor that accounts for market-wide shocks and a tail dependence factor that accounts for linkages among extreme stock returns. Specifically, our theoretical model allows for firm-specific impacts of infrequent and extreme events. Using data on the four sectors of the U.S. financial industry from 1996 to 2011, we uncover two key empirical findings. First, disregarding the effect of the tail dependence factor leads to a downward bias in the measurement of systemic risk, especially during weak economic times. Second, when these measures serve as leading indicators of the St. Louis Fed Financial Stress Index, measures that include a tail dependence factor offer better forecasting ability than measures based on a common factor only. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02276&r= |
| By: | Martin Guth |
| Abstract: | Since the Great Financial Crisis (GFC), the use of stress tests as a tool for assessing the resilience of financial institutions to adverse financial and economic developments has increased significantly. One key part in such exercises is the translation of macroeconomic variables into default probabilities for credit risk by using macrofinancial linkage models. A key requirement for such models is that they should be able to properly detect signals from a wide array of macroeconomic variables in combination with a mostly short data sample. The aim of this paper is to compare a great number of different regression models to find the best performing credit risk model. We set up an estimation framework that allows us to systematically estimate and evaluate a large set of models within the same environment. Our results indicate that there are indeed better performing models than the current state-of-the-art model. Moreover, our comparison sheds light on other potential credit risk models, specifically highlighting the advantages of machine learning models and forecast combinations. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.03110&r= |
| By: | Hwai-Chung Ho |
| Abstract: | The study of long-horizon returns has received a great deal of attention in recent years (see, for example, Boudoukh, Richardson, and Whitelaw (2008), Neuberger (2012) and Lee (2013), Fama and French (2018)). While most of the discussions are concerned with some practical issues in investment, few have touched the important aspect on risk management. The approach adopted in this article is to predict the future distribution of the returns of a fixed long-horizon by which the risk measures of interest that come in the form of a distributional functional such as the value at risk (VaR) and the conditional tail expectation (CTE) can be easily derived. The characteristic feature of our approach which requires no specification of the volatility dynamics nor parametric assumptions of the shock distribution extends the work by Ho et al. (2016) and Ho ( 2017) to a more general volatility dynamics that includes both the widely-used SV model and the GARCH model (Bollerslev, 1986) as special cases. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.07457&r= |
| By: | Schwaab, Bernd (European Central Bank); Zhang, Xin (Research Department, Central Bank of Sweden); Lucas, André (VU University Amsterdam) |
| Abstract: | We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model ina conditional framework to allow for time-variation in the tail shape parameters. The score-driven updates used improve the expected Kullback-Leibler divergence between the model and the true data generating process on every step even if the GPD only fits approximately and the model is mis-specified, as will be the case in any finite sample. This is confirmed in simulations. Using the model, we find that Eurosystem sovereign bond purchases during the euro area sovereign debt crisis had a beneficial impact on extreme upper tail quantiles, leaning against the risk of extremely adverse market out comes while active. |
| Keywords: | dynamic tail risk; observation-driven models; extreme value theory; Eu ropean Central Bank (ECB); Securities Markets Programme (SMP) |
| JEL: | C22 G11 |
| Date: | 2020–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:rbnkwp:0399&r= |
| By: | Meutia, Annissa |
| Abstract: | Manajemen risiko rantai pasokan telah mengumpulkan fokus yang meningkat dari manajer rantai pasokan karena dampak merugikan yang dapat ditimbulkan oleh gangguan atau gangguan rantai pasokan terhadap kinerja rantai pasokan. Gangguan rantai pasokan dapat mengakibatkan berbagai masalah seperti waktu tunggu yang lama, kehabisan stok, ketidakmampuan untuk memenuhi permintaan pelanggan, dan peningkatan biaya |
| Date: | 2022–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:3zgsw&r= |
| By: | Abdoulaye Sy (CERDI - Centre d'Études et de Recherches sur le Développement International - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne); Catherine Araujo-Bonjean (CERDI - Centre d'Études et de Recherches sur le Développement International - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne); Marie-Eliette Dury (CERDI - Centre d'Études et de Recherches sur le Développement International - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne); Nourddine Azzaoui (LMBP - Laboratoire de Mathématiques Blaise Pascal - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne); Arnaud Guillin (LMBP - Laboratoire de Mathématiques Blaise Pascal - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne) |
| Abstract: | A critical stage in drought hazard assessment is the definition of a drought event, and the measure of its intensity. Actually, the classical approach imposes to all climatic region the same set of thresholds for drought severity classification, hence resulting in a loss of information on rare events in the distribution tails, which are precisely the most important to catch in risk analysis. In order to better assess extreme events, we resort to an extreme value mixture model with a normal distribution for the bulk and a Generalized Pareto distribution for the upper and lower tails, to estimate the intensity of extreme droughts and their occurrence probability. Compare to the standard approach to drought hazard, which relies on a standardized precipitation index and a classification of drought intensity established from the cumulative standard normal distribution function, our approach allows the drought threshold and the occurrence probability of drought to depend on the specific characteristics of each precipitation distribution. An application to the West Africa region shows that the accuracy of our mixture model is higher than that of the standard model. The mixture performs better at modelling the lowest percentiles and specifically the return level of the centennial drought, which is generally overestimated in the standard approach. |
| Keywords: | Mixture model,Generalized pareto distribution,Drought,Extreme value theory |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cdiwps:hal-03297023&r= |
| By: | Wolf, Elias |
| Abstract: | This paper proposes a Skewed Stochastic Volatility (SSV) model to model time varying, asymmetric forecast distributions to estimate Growth at Risk as introduced in Adrian, Boyarchenko, and Giannone's (2019) seminal paper "Vulnerable Growth". In contrary to their semi-parametric approach, the SSV model enables researchers to capture the evolution of the densities parametrically to conduct statistical tests and compare different models. The SSV-model forms a non-linear, non-gaussian state space model that can be estimated using Particle Filtering and MCMC algorithms. To remedy drawbacks of standard Bootstrap Particle Filters, I modify the Tempered Particle Filter of Herbst and Schorfheide's (2019) to account for stochastic volatility and asymmetric measurement densities. Estimating the model based on US data yields conditional forecast densities that closely resemble the findings by Adrian et al. (2019). Exploiting the advantages of the proposed model, I find that the estimated parameter values for the effect of financial conditions on the variance and skewness of the conditional distributions are statistically significant and in line with the intuition of the results found in the existing literature. |
| Keywords: | Growth at Risk,Macro Finance,Bayesian Econometrics,Particle Filters |
| JEL: | C10 E32 E58 G01 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fubsbe:20222&r= |
| By: | Jori Hoencamp; Shashi Jain; Drona Kandhai |
| Abstract: | We present a semi-static hedging algorithm for callable interest rate derivatives under an affine, multi-factor term-structure model. With a traditional dynamic hedge, the replication portfolio needs to be updated continuously through time as the market moves. In contrast, we propose a semi-static hedge that needs rebalancing on just a finite number of instances. We show, taking as an example Bermudan swaptions, that callable interest rate derivatives can be replicated with an options portfolio written on a basket of discount bonds. The static portfolio composition is obtained by regressing the target option's value using an interpretable, artificial neural network. Leveraging on the approximation power of neural networks, we prove that the hedging error can be arbitrarily small for a sufficiently large replication portfolio. A direct, a lower bound, and an upper bound estimator for the risk-neutral Bermudan swaption price is inferred from the hedging algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically demonstrate the hedging and pricing performance through several numerical experiments. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.01027&r= |
| By: | Jaydip Sen; Sidra Mehtab; Abhishek Dutta; Saikat Mondal |
| Abstract: | Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02728&r= |
| By: | Muhammad Abubakr Naeem; Sitara Karim; Tooraj Jamasb; Rabindra Nepal |
| Abstract: | The current study examines the risk transmission between green markets and commodities spanning 3 January 2011 to 20 June 2021. We use two novel methodologies of volatility transmission using dynamic conditional correlation (DCC-GARCH) and the other time-varying parameters vector autoregression (TVP-VAR) technique of connectedness. We found parallel results of risk transmission between green markets and commodities using these measures of connectedness. Results demonstrate that green markets and commodities form a weakly knitted sphere of connectedness where intra-group clustering dominates the inter-group connectedness. Clean energy markets and precious metals form two distinct groups of connectedness for respective markets. However, crude oil, natural gas and wheat remained indifferent to the shocks highlighting their potential to serve as diversifiers due to their low risk bearing features. Further, time-varying dynamics emphasize the occurrence of sizable events that disrupted the operations of green and commodity markets, accentuating the attention of investors, portfolio managers, and financial market participants. Intense spillovers shaped the overall connectedness of the network where green markets (commodities) are fashioned in positive (negative) risk spillovers. Finally, we propose recommendations for policymakers, regulators, investors, portfolio managers, and market participants to devise policies and investment goals to shield their investments from unexpected circumstances. |
| Keywords: | Green markets, Commodities, DCC-GARCH, TVP-VAR, Volatility transmission |
| JEL: | G10 G11 G19 Q01 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2022-18&r= |
| By: | Karel Janda; Ladislav Kristoufek; Binyi Zhang |
| Abstract: | This paper aims to empirically investigate the dynamic connectedness between oil prices and stock returns of clean energy-related and technology companies in China and U.S. financial markets. We apply three multivariate GARCH model specifications (CCC, DCC and ADCC) to investigate the return and volatility spillovers among price and return series. We use rolling window analysis to forecast out-of-sample one-step-ahead dynamic conditional correlations and time-varying optimal hedge ratios. Our results suggest that Invesco China Technology ETF (CQQQ) is the best asset to hedge Chinese clean energy stocks followed by WTI, ECO, and PSE. Our results are reasonably robust to the choice of different model refits and forecast length of rolling window analysis. Our empirical findings provide investors and policymakers with the systematic understanding of return and volatility connectedness between China and U.S. clean energy stock markets. |
| Keywords: | Clean energy, Hedge effectiveness, Rolling window analysis |
| JEL: | C22 G11 Q41 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2022-17&r= |
| By: | Zhiqin Zou (China University of Petroleum); Arash Farnoosh (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles); Tom Mcnamara (Rennes School of Business) |
| Abstract: | In order to implement or maintain a green supply chain (GSC) that produces goods and services responsibly and sustainably, supply chain managers should use tools that allow for the efficient identification, quantification, and mitigation of the ever‐present risks. The objective of the present research is to identify the risk factors associated with the processes involved in GSC management. Based on an analysis of the characteristics of GSC risk, the authors put forward a list of risk design principles and a risk criteria evaluation system for a GSC. Gray relation analysis method was then used to clarify the degree of connection between certain supply chain risk factors and select key risk factors. Finally, Back Propagation Artificial Neural Network (BP‐ANN) method was used to determine the risk level associated with a GSC. The determination of risk level will help companies to develop effective strategic management initiatives in a GSC environment. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03181313&r= |
| By: | Seungki Min; Ciamac C. Moallemi; Costis Maglaras |
| Abstract: | We consider a liquidation problem in which a risk-averse trader tries to liquidate a fixed quantity of an asset in the presence of market impact and random price fluctuations. The trader encounters a trade-off between the transaction costs incurred due to market impact and the volatility risk of holding the position. Our formulation begins with a continuous-time and infinite horizon variation of the seminal model of Almgren and Chriss (2000), but we define as the objective the conditional value-at-risk (CVaR) of the implementation shortfall, and allow for dynamic (adaptive) trading strategies. In this setting, we are able to derive closed-form expressions for the optimal liquidation strategy and its value function. Our results yield a number of important practical insights. We are able to quantify the benefit of adaptive policies over optimized static policies. The relevant improvement depends only on the level of risk aversion: for moderate levels of risk aversion, the optimal dynamic policy outperforms the optimal static policy by 5-15%, and outperforms the optimal volume weighted average price (VWAP) policy by 15-25%. This improvement is achieved through dynamic policies that exhibit "aggressiveness-in-the-money": trading is accelerated when price movements are favorable, and is slowed when price movements are unfavorable. From a mathematical perspective, our analysis exploits the dual representation of CVaR to convert the problem to a continuous-time, zero-sum game. We leverage the idea of the state-space augmentation, and obtain a partial differential equation describing the optimal value function, which is separable and a special instance of the Emden-Fowler equation. This leads to a closed-form solution. As our problem is a special case of a linear-quadratic-Gaussian control problem with a CVaR objective, these results may be interesting in broader settings. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11962&r= |
| By: | Jaydip Sen; Saikat Mondal; Sidra Mehtab |
| Abstract: | Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector. An LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns. The predicted and the actual returns indicate a very high-level accuracy of the LSTM model. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02723&r= |
| By: | Anindya Goswami; Kedar Nath Mukherjee; Irvine Homi Patalwala; Sanjay N. S |
| Abstract: | In the regime switching extension of Black-Scholes-Merton model of asset price dynamics, one assumes that the volatility coefficient evolves as a hidden pure jump process. Under the assumption of Markov regime switching, we have considered the locally risk minimizing price of European vanilla options. By pretending these prices or their noisy versions as traded prices, we have first computed the implied volatility (IV) of the underlying asset. Then by performing several numerical experiments we have investigated the dependence of IV on the time to maturity (TTM) and strike price of the vanilla options. We have observed a clear dependence that is at par with the empirically observed stylized facts. Furthermore, we have experimentally validated that IV time series, obtained from contracts with moneyness and TTM varying in particular narrow ranges, can recover the transition instances of the hidden Markov chain. Such regime recovery has also been proved in a theoretical setting. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.10304&r= |
| By: | Panggabean, Angelita Nauli |
| Abstract: | Kebutuhan pasar yang semakin tinggi dan persaingan yang semakin ketat, menuntut perusahaan untuk selalu berusaha mengembangkan perusahaannya dengan menghadirkan produk baru di pasar. Dari sinilah, dapat dilihat bahwa bahwa manajemen risiko proyek dalam menghadirkan produk baru, sangatlah penting dan perlu diberi perhatian. Jika risiko proyek tidak dikendalikan dengan baik, maka akan menyebabkan ganguan dalam proses proyek itu sendiri. Tujuan dari karya tulis ini adalah untuk mengetahui pentingnya penerapan manajemen risiko pada industri otomotif. Metode yang digunakan penulis adalah mengumpulkan dan mempelajari jurnal-jurnal dari internet untuk mencapai kesimpulan. Kesimpulannya, bahwa manajemen risiko merupakan alat manajemen penting bagi para manajer proyek dalam meningkatkan keberhasilan proyek. |
| Date: | 2022–01–13 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:eqwa8&r= |
| By: | Isuru Ratnayake; V. A. Samaranayake |
| Abstract: | This paper introduces a Threshold Asymmetric Conditional Autoregressive Range (TACARR) formulation for modeling the daily price ranges of financial assets. It is assumed that the process generating the conditional expected ranges at each time point switches between two regimes, labeled as upward market and downward market states. The disturbance term of the error process is also allowed to switch between two distributions depending on the regime. It is assumed that a self-adjusting threshold component that is driven by the past values of the time series determines the current market regime. The proposed model is able to capture aspects such as asymmetric and heteroscedastic behavior of volatility in financial markets. The proposed model is an attempt at addressing several potential deficits found in existing price range models such as the Conditional Autoregressive Range (CARR), Asymmetric CARR (ACARR), Feedback ACARR (FACARR) and Threshold Autoregressive Range (TARR) models. Parameters of the model are estimated using the Maximum Likelihood (ML) method. A simulation study shows that the ML method performs well in estimating the TACARR model parameters. The empirical performance of the TACARR model was investigated using IBM index data and results show that the proposed model is a good alternative for in-sample prediction and out-of-sample forecasting of volatility. Key Words: Volatility Modeling, Asymmetric Volatility, CARR Models, Regime Switching. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.03351&r= |
| By: | Carmina Fjellstr\"om |
| Abstract: | Performance forecasting is an age-old problem in economics and finance. Recently, developments in machine learning and neural networks have given rise to non-linear time series models that provide modern and promising alternatives to traditional methods of analysis. In this paper, we present an ensemble of independent and parallel long short-term memory (LSTM) neural networks for the prediction of stock price movement. LSTMs have been shown to be especially suited for time series data due to their ability to incorporate past information, while neural network ensembles have been found to reduce variability in results and improve generalization. A binary classification problem based on the median of returns is used, and the ensemble's forecast depends on a threshold value, which is the minimum number of LSTMs required to agree upon the result. The model is applied to the constituents of the smaller, less efficient Stockholm OMX30 instead of other major market indices such as the DJIA and S&P500 commonly found in literature. With a straightforward trading strategy, comparisons with a randomly chosen portfolio and a portfolio containing all the stocks in the index show that the portfolio resulting from the LSTM ensemble provides better average daily returns and higher cumulative returns over time. Moreover, the LSTM portfolio also exhibits less volatility, leading to higher risk-return ratios. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.08218&r= |
| By: | Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | We examine the power of global economic conditions (GECON) in forecasting the daily return volatility of various international Real Estate Investment Trusts (REITs) indices. To this end, we use the GARCH-MIDAS framework due to the mixed frequencies of the variables under study and given its merit of circumventing the problems of information loss due to data aggregation and biases through data disaggregation. The results show evidence of forecast gains in the model that accommodates GECON, and significant in-sample forecastability where improvements in global economic conditions lower the risk associated with the international REITs particularly in the US and emerging markets. Further analysis shows the possibility of gaining higher returns on REITs by exploiting the information contents of GECON. A robustness analysis indicates that other measures of global economic conditions such as Global Weakness Index (GWI) and Global Intensity Index (GII) contain lower forecasting power than GECON but with significant improvements in their forecast outcomes when combined with the latter using the principal components analysis. Consequently, monitoring the global economic dynamics via GECON as well as other indices (GWI and GII) is crucial for optimal investment decisions. |
| Keywords: | REITs volatility, global economic conditions, mixed data analysis, GARCH-MIDAS model, forecasting |
| JEL: | C32 C53 E32 R30 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202211&r= |
| By: | Kasde, Fiona Ramadhita |
| Abstract: | Dalam industry real estat terdapat berbagai risiko, misalnya dalam proyek yang berskala besar, kompleks, dan rentan terhadap waktu. Pihak-pihak yang terlibat dalam proyek real estat mau tidak mau harus menghadapi berbagai risiko. Oleh karena itu, dalam proyeknya, pihak yang terlibat harus waspada terhadap risiko yang mungkin akan mempengaruhi kemajuan konstruksi dan membawa konsekuensi serius. Di berbagai negara, manajemen risiko diakui sebagai contributor yang signifikan terhadap efisiensi dan efektivitas suatu organisasi. Untuk mengejar keunggulan tersebut, organisasi telah mulai mengembangkan serta menerapkan strategi yang berfokus pada tujuan, visi, misi, dan nilai-nilai inti. Berdasarkan penelitian terdahulu, ditemukan bahwa strategi manajemen risiko dan kinerja perusahaan memiliki korelasi yang positif dan kuat. |
| Date: | 2022–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:h4db5&r= |
| By: | maulana, ahmad |
| Abstract: | Artikel ilmiah ini memberikan gambaran tentang apa risikonya dan bagaimana melindunginya. E-commerce (perdagangan elektronik). E-commerce adalah sel dinamis dari teknologi, aplikasi dan proses. Sebuah bisnis yang menghubungkan bisnis dan konsumen. dengan komunitas luar negeri melalui e-commerce Perdagangan barang, jasa dan informasi dalam bentuk elektronik. Meskipun e-niaga Anda dapat mengurangi biaya menjalankan bisnis Anda Untuk meningkatkan layanan pelanggan, tetapi sistem e-commerce ini dan semuanya Infrastruktur pendukung sudah tersedia bagi pihak-pihak yang tidak bertanggung jawab. Itu juga rentan terhadap kesalahan yang dapat terjadi dalam berbagai cara. kerusakan tinggi Itu bisa terjadi padaelemen apa pun yang terkait dengan sistem ini, baik itu sistem perdagangan. perdagangan, lembaga keuangan, penyedia layanan, dan bahkan konsumen. Buatan manusia, tidak ada yang sempurna. Untuk itu, studi tentang keamanan diperlukan. |
| Date: | 2022–01–09 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:b5agj&r= |
| By: | Krzysztof Rusek; Piotr Bory{\l}o; Piotr Jaglarz; Fabien Geyer; Albert Cabellos; Piotr Cho{\l}da |
| Abstract: | We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated with the Barab\'asi-Albert model. Even though, the obtained test results show that we can precisely model the penalties in a wide range of various existing topologies. GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study. In practice, the whole design operation is limited by 4ms on modern hardware. This way, we can gain as much as over 12,000 times in the speed improvement. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12263&r= |
| By: | Pratama, Muhammad Andika Rizki |
| Abstract: | Mempertimbangkan kebutuhan pasar dan daya tarik pelanggan, pembuat mobil selalu berusaha mendefinisikan proyek baru dan menghadirkan produk dengan kemampuan baru di pasar. Itulah sebabnya sebagian besar penelitian pengembangan perusahaan mobil difokuskan pada definisi proyek baru. Pada prinsipnya, manajemen risiko proyek di perusahaan mobil sangat penting dan dengan demikian diberi perhatian khusus. Ada berbagai teori dan metode pengendalian risiko proyek. Namun, karena ada kesadaran lengkap tentang masalah terkait FMEA (Mode Kegagalan dan Analisis Efek) di perusahaan otomotif karena pembentukan sistem manajemen mutu, analisis risiko proyek menggunakan metode FMEA untuk mengendalikan risiko proyek industri otomotif disajikan dalam makalah ini dengan contoh nyata. Untuk tujuan ini, tabel indikator FMEA dirancang dan disajikan secara proporsional untuk manajemen risiko proyek. Hasil penelitian ini menunjukkan bahwa menggunakan mode kegagalan dan analisis efek untuk manajemen risiko proyek memastikan deteksi kelemahan proyek dan menyediakan model praktis untuk identifikasi dan pengurangan risiko proyek. |
| Date: | 2022–01–26 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:b2eua&r= |
| By: | Prendergast, Michael |
| Abstract: | This paper describes a methodology for estimating safe withdrawal rates during retirement that is based on a retiree’s age, risk tolerance and investment strategy, and then provides results obtained from using that methodology. The estimates are generated by a three-step process. In the first step, Monte Carlo simulations of future inflation rates, 10-year treasury rates, corporate bond rates (AAA and BAA), the S&P 500 index values and S&P 500 dividend yields are performed. In the second step, portfolio composition and withdrawal rate combinations are evaluated against each of the Monte Carlo simulations in order to calculate portfolio longevity likelihood tables, which are tables that show the likelihood that a portfolio will survive a certain number of years for a given withdrawal rate. In the third and final step, portfolio longevity tables are compared with standard mortality tables in order to estimate the likelihood that the portfolio outlasts the retiree. This three-step approach was then applied using both a Monte Carlo random walk model and an ARIMA/GARCH model based upon over 100 years of monthly historical data. The end result was estimates of the likelihood of portfolio survival to mortality for over 500,000 retiree age/sex/portfolio/withdrawal rate combinations, each combination supported by at least 10,000 Monte Carlo economic simulation points per model. Both models are supported by 100 years of historical data. The first model is a random walk with step sizes determined by bootstrapping, and the second is an ARIMA/GARCH regression model. This data is analyzed to predict safe withdrawal rates and portfolio composition strategies appropriate for the late 2021 economic environment. |
| Date: | 2022–01–20 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:jd2xg&r= |
| By: | Yichen Feng; Jean-Pierre Fouque; Ruimeng Hu; Tomoyuki Ichiba |
| Abstract: | We analyze the systemic risk for disjoint and overlapping groups (e.g., central clearing counterparties (CCP)) by proposing new models with realistic game features. Specifically, we generalize the systemic risk measure proposed in [F. Biagini, J.-P. Fouque, M. Frittelli, and T. Meyer-Brandis, Finance and Stochastics, 24(2020), 513--564] by allowing individual banks to choose their preferred groups instead of being assigned to certain groups. We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor. We also provide an explicit solution for the risk allocation of the individual banks, and study the existence and uniqueness of Nash equilibrium both theoretically and numerically. The developed numerical algorithm can simulate scenarios of equilibrium, and we apply it to study the bank-CCP structure with real data and show the validity of the proposed model. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.00662&r= |
| By: | Carla Zoe Cremer; Luke Kemp |
| Abstract: | Studying potential global catastrophes is vital. The high stakes of existential risk studies (ERS) necessitate serious scrutiny and self-reflection. We argue that existing approaches to studying existential risk are not yet fit for purpose, and perhaps even run the risk of increasing harm. We highlight general challenges in ERS: accommodating value pluralism, crafting precise definitions, developing comprehensive tools for risk assessment, dealing with uncertainty, and accounting for the dangers associated with taking exceptional actions to mitigate or prevent catastrophes. The most influential framework for ERS, the 'techno-utopian approach' (TUA), struggles with these issues and has a unique set of additional problems: it unnecessarily combines the study of longtermism and longtermist ethics with the study of extinction, relies on a non-representative moral worldview, uses ambiguous and inadequate definitions, fails to incorporate insights from risk assessment in relevant fields, chooses arbitrary categorisations of risk, and advocates for dangerous mitigation strategies. Its moral and empirical assumptions might be particularly vulnerable to securitisation and misuse. We suggest several key improvements: separating the study of extinction ethics (ethical implications of extinction) and existential ethics (the ethical implications of different societal forms), from the analysis of human extinction and global catastrophe; drawing on the latest developments in risk assessment literature; diversifying the field, and; democratising its policy recommendations. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11214&r= |
| By: | Khalid El-Awady |
| Abstract: | There is a massive underserved market for small business lending in the US with the Federal Reserve estimating over \$650B in unmet annual financing needs. Assessing the credit risk of a small business is key to making good decisions whether to lend and at what terms. Large corporations have a well-established credit assessment ecosystem, but small businesses suffer from limited publicly available data and few (if any) credit analysts who cover them closely. We explore the applicability of (DL-based) large corporate credit risk models to small business credit rating. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.08276&r= |
| By: | William Schueller; Christian Diem; Melanie Hinterplattner; Johannes Stangl; Beate Conrady; Markus Gerschberger; Stefan Thurner |
| Abstract: | The Covid-19 pandemic drastically emphasized the fragility of national and international supply networks (SNs),leading to significant supply shortages of essential goods for people, such as food and medical equipment. Severe disruptions that propagate along complex SNs can expose the population of entire regions or even countries to these risks. A lack of both, data and quantitative methodology, has hitherto hindered us to empirically quantify the vulnerability of the population to disruptions. Here we develop a data-driven simulation methodology to locally quantify actual supply losses for the population that result from the cascading of supply disruptions. We demonstrate the method on a large food SN of a European country including 22,938 business premises, 44,355 supply links and 116 local administrative districts. We rank the business premises with respect to their criticality for the districts' population with the proposed systemic risk index, SRIcrit, to identify around 30 premises that -- in case of their failure -- are expected to cause critical supply shortages in sizable fractions of the population. The new methodology is immediately policy relevant as a fact-driven and generalizable crisis management tool. This work represents a starting point for quantitatively studying SN disruptions focused on the well-being of the population. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.13325&r= |
| By: | Julio Guerrero; Giuseppe Orlando |
| Abstract: | In this paper, we show that a time-dependent local stochastic volatility (SLV) model can be reduced to a system of autonomous PDEs that can be solved using the Heat kernel, by means of the Wei-Norman factorization method and Lie algebraic techniques. Then, we compare the results of traditional Monte Carlo simulations with the explicit solutions obtained by said techniques. This approach is new in the literature and, in addition to reducing a non-autonomous problem into an autonomous one, allows for reduced time in numerical computations. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11241&r= |
| By: | Igor Nesiolovskiy |
| Abstract: | This paper proposes a method for ranking the investment attractiveness of exchange-traded stocks where investment risk is not related to the volatility indicator but instead is related to the indicator of compression of the time series of price changes. The article describes in detail the ranking algorithm, provides an example of ranking the shares of all companies included in the Dow Jones stock index. The paper additionally compares the results of ranking these stocks by volatility and compression and also shows the strengths of the second indicator, which is formed using the method of binary-ternary compression of historical financial data. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11507&r= |
| By: | Nurul Izzaty Hasanah Azhar (UiTM - Universiti Teknologi MARA [Shah Alam]); Norziana Lokman (UiTM - Universiti Teknologi MARA [Shah Alam]); Md. Mahmudul Alam (UUM - Universiti Utara Malaysia); Jamaliah Said (UiTM - Universiti Teknologi MARA [Shah Alam]) |
| Abstract: | Predicting the sustainability of a business is crucial to prevent financial losses among shareholders and investors. This study attempts to evaluate the Altman model for predicting corporate failure in distressed and non-distressed Malaysian companies based on the data of financially troubled companies which are classified as Practice Note 17 (PN17) and matching similar non-PN17 companies during the period 2013 to 2017. This study utilizes panel ordinal and panel random effects regressions. Findings show that the liquidity, profitability, leverage, solvency, and efficiency ratios are negatively significantly associated with corporate failure and bankruptcy. The leverage ratio is determined to be the strongest indicator of bankruptcy, followed by profitability, liquidity, solvency, and efficiency ratios. The findings will help companies' management bodies implement suitable strategies to prevent further financial leakage, thereby ensuring continuous and sustainable return on investment and profits for investors and shareholders. |
| Keywords: | Corporate Failure,Financial Distress,PN17 companies,Ratio analysis,Z-Score |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03520192&r= |
| By: | Giuseppe Brandi; T. Di Matteo |
| Abstract: | Pricing derivatives goes back to the acclaimed Black and Scholes model. However, such a modeling approach is known not to be able to reproduce some of the financial stylized facts, including the dynamics of volatility. In the mathematical finance community, it has therefore emerged a new paradigm, named rough volatility modeling, that represents the volatility dynamics of financial assets as a fractional Brownian motion with Hurst exponent very small, which indeed produces rough paths. At the same time, prices' time series have been shown to be multiscaling, characterized by different Hurst scaling exponents. This paper assesses the interplay, if present, between price multiscaling and volatility roughness, defined as the (low) Hurst exponent of the volatility process. In particular, we perform extensive simulation experiments by using one of the leading rough volatility models present in the literature, the rough Bergomi model. A real data analysis is also carried out in order to test if the rough volatility model reproduces the same relationship. We find that the model is able to reproduce multiscaling features of the prices' time series when a low value of the Hurst exponent is used but it fails to reproduce what the real data say. Indeed, we find that the dependency between prices' multiscaling and the Hurst exponent of the volatility process is diametrically opposite to what we find in real data, namely a negative interplay between the two. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.10466&r= |
| By: | Nikolas Michael; Mihai Cucuringu; Sam Howison |
| Abstract: | We investigate the use of the normalized imbalance between option volumes corresponding to positive and negative market views, as a predictor for directional price movements in the spot market. Via a nonlinear analysis, and using a decomposition of aggregated volumes into five distinct market participant classes, we find strong signs of predictability of excess market overnight returns. The strongest signals come from Market-Maker volumes. Among other findings, we demonstrate that most of the predictability stems from high-implied-volatility option contracts, and that the informational content of put option volumes is greater than that of call options. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09319&r= |
| By: | Peng Wu; Jean-Fran\c{c}ois Muzy; Emmanuel Bacry |
| Abstract: | We introduce a family of random measures $M_{H,T} (d t)$, namely log S-fBM, such that, for $H>0$, $M_{H,T}(d t) = e^{\omega_{H,T}(t)} d t$ where $\omega_{H,T}(t)$ is a Gaussian process that can be considered as a stationary version of a $H$-fractional Brownian motion. Moreover, when $H \to 0$, one has $M_{H,T}(d t) \to {\tilde M}_{T}(d t)$ (in the weak sense) where ${\tilde M}_{T}(d t)$ is the celebrated log-normal multifractal random measure (MRM). Thus, this model allows us to consider, within the same framework, the two popular classes of multifractal ($H = 0$) and rough volatility ($0 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09516&r= |
| By: | Ihsan Chaoubi; Camille Besse; H\'el\`ene Cossette; Marie-Pier C\^ot\'e |
| Abstract: | Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.13267&r= |
| By: | Roberto Casarin (University of Ca' Foscari of Venice); Stefano Grassi (University of Rome Tor Vergata); Francesco Ravazzolo (BI Norwegian Business School); Herman van Dijk (Erasmus University Rotterdam) |
| Abstract: | A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets. Given the representation of the probability model in extended nonlinear state-space form, efficient simulation-based Bayesian inference is proposed using parallel sequential clustering as well as nonlinear filtering, implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual stock returns of daily observations over a period that includes the Covid-19 crisis period. Evidence on the quantification of predictive accuracy, uncertainty and risk, in particular, in the tails, may provide useful information for investment fund management. Information on dynamic cluster composition, weight patterns and model set incompleteness give also valuable signals for improved modelling and policy specification. |
| Keywords: | Density Combination, Large Set of Predictive Densities, Dynamic Factor Models, Nonlinear state-space, Bayesian Inference |
| JEL: | C11 C15 C53 E37 |
| Date: | 2022–02–14 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20220013&r= |
| By: | Anton J. Heckens; Thomas Guhr |
| Abstract: | Complex systems are usually non-stationary and their dynamics is often dominated by collective effects. Collectivity, defined as coherent motion of the whole system or of some of its parts, manifests itself in the time-dependent structures of covariance and correlation matrices. The largest eigenvalue corresponds to the collective motion of the system as a whole, while the other large, isolated, eigenvalues indicate collectivity in parts of the system. In the case of finance, these are industrial sectors. By removing the collective motion of the system as a whole, the latter effects are much better revealed. We measure a remaining collectivity to which we refer as average sector collectivity. We identify collective signals around the Lehman Brothers crash and after the dot-com bubble burst. For the Lehman Brother crash, we find a potential precursor. We analyze 213 US stocks over a period of more than 30 years from 1990 to 2021. We plot the average sector collectivity versus the collectivity corresponding to the largest eigenvalue to study the whole market trajectory in a two dimensional space spanned by both collectivities. Therefore, we capture the average sector collectivity in a much more precise way. Additionally, we observe that larger values in the average sector collectivity are often accompanied by trend shifts in the mean covariances and mean correlations. As of 2015/2016 the collectivity in the US stock markets changed fundamentally. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.00297&r= |
| By: | Yawovi Mawussé Isaac Amedanou (CERDI - Centre d'Études et de Recherches sur le Développement International - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne) |
| Abstract: | This paper aims to show that there is a great interest for countries to rely on Public-Private Partnerships (PPPs) as a tool for financing the economy, especially in times of debt. First, we conceptualize through game theory a better risk management between the public and private sectors in case of co-investment. Second, building on Iossa & Martimort (2009), we demonstrate that PPPs investments produce greater economic and social gains than pure public investments by providing incentives and transferring risks to the private sector. The implications of the model are diverse: financing the provision of public infrastructure through PPPs allows for sharing the associated risks, improves the quality and reduce the costs of the provision of public goods. The model has been empirically tested on 14 Sub-Saharan African countries over the period 1990 − 2017. The impact of PPP investments is significantly higher than that of pure public investments. The evidence also shows that the positive impact of PPP investments strengthens economic growth as the public debt grows to a point where there is no longer any significant pro-growth impact. |
| Keywords: | Public-private partnership,Pure public investment,Cooperatie game,Risk management,Economic growth,Public debt,Fiscal constraints |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03545244&r= |
| By: | Zhe Wang; Nicolas Privault; Claude Guet |
| Abstract: | We present an algorithm for the calibration of local volatility from market option prices through deep self-consistent learning, by approximating market option prices and local volatility using deep neural networks. Our method uses the initial-boundary value problem of the underlying Dupire's partial differential equation solved by the parameterized option prices to bring corrections to the parameterization in a self-consistent way. By exploiting the differentiability of the neural networks, we can evaluate Dupire's equation locally at each maturity-strike pair; while by exploiting their continuity, we sample maturity-strike pairs uniformly from a given domain, going beyond the discrete points where the options are quoted. For comparison with existing approaches, the proposed method is tested on both synthetic and market option prices, which shows an improved performance in terms of repricing error, no violation of the no-arbitrage constraints, and smoothness of the calibrated local volatility. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.07880&r= |
| By: | Shuo Sun; Rundong Wang; Xu He; Junlei Zhu; Jian Li; Bo An |
| Abstract: | Reinforcement learning (RL) techniques have shown great success in quantitative investment tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating values. However, it is hard to apply existing RL methods to intraday trading due to the following three limitations: 1) overlooking micro-level market information (e.g., limit order book); 2) only focusing on local price fluctuation and failing to capture the overall trend of the whole trading day; 3) neglecting the impact of market risk. To tackle these limitations, we propose DeepScalper, a deep reinforcement learning framework for intraday trading. Specifically, we adopt an encoder-decoder architecture to learn robust market embedding incorporating both macro-level and micro-level market information. Moreover, a novel hindsight reward function is designed to provide the agent a long-term horizon for capturing the overall price trend. In addition, we propose a risk-aware auxiliary task by predicting future volatility, which helps the agent take market risk into consideration while maximizing profit. Finally, extensive experiments on two stock index futures and four treasury bond futures demonstrate that DeepScalper achieves significant improvement against many state-of-the-art approaches. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09058&r= |
| By: | Meng-Chen Hsieh; Clifford Hurvich; Philippe Soulier |
| Abstract: | We develop and justify methodology to consistently test for long-horizon return predictability based on realized variance. To accomplish this, we propose a parametric transaction-level model for the continuous-time log price process based on a pure jump point process. The model determines the returns and realized variance at any level of aggregation with properties shown to be consistent with the stylized facts in the empirical finance literature. Under our model, the long-memory parameter propagates unchanged from the transaction-level drift to the calendar-time returns and the realized variance, leading endogenously to a balanced predictive regression equation. We propose an asymptotic framework using power-law aggregation in the predictive regression. Within this framework, we propose a hypothesis test for long horizon return predictability which is asymptotically correctly sized and consistent. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.00793&r= |
| By: | Raphael Auer; Bruce Muneaki Iwadate; Andreas Schrimpf; Alexander F. Wagner |
| Abstract: | Although real integration conceptually plays an important role for the comovement of international equity markets, documenting this link empirically has proven challenging. We construct a new dataset of theory-guided, relevant measures of bilateral trade in final and intermediate goods and services. With these measures, we provide evidence of a strong link between changes in real integration – in particular global value chains – and equity market comovement. This also holds when controlling for financial openness and other factors that could confound the role of real openness. These results suggest that supply chain disruptions, for instance due to political tensions and the COVID-19 crisis, might also affect the interconnections between stock markets via rippling through the global production network. |
| Keywords: | financial integration, global value chains, international asset pricing, international trade, real integration, spillovers, stock market comovement, supply chains. |
| JEL: | F10 F36 F65 G10 G12 G15 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1003&r= |