nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒10‒26
33 papers chosen by

  1. Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation By Cheng Peng; Young Shin Kim
  2. Volterra mortality model: Actuarial valuation and risk management with long-range dependence By Ling Wang; Mei Choi Chiu; Hoi Ying Wong
  3. A Cost-Benefit Analysis of Capital Requirements Adjusted for Model Risk By Walter Farkas; Fulvia Fringuellotti; Radu Tunaru
  4. Tail-risk protection: Machine Learning meets modern Econometrics By Bruno Spilak; Wolfgang Karl H\"ardle
  5. Capital Regulations and the Management of Credit Commitments during Crisis Times By Pelzl, Paul; Valderrama, Maria Teresa
  6. Fears for COVID-19: The crash risk of stock market By Zhifeng Liu; Toan Luu Duc Huynh; Peng-Fei Dai
  7. Banks, Non Banks, and Lending Standards By R. Matthew Darst; Ehraz Refayet; Alexandros Vardoulakis
  8. Simulation-based optimisation of the timing of loan recovery across different portfolios By Arno Botha; Conrad Beyers; Pieter de Villiers
  9. Liquidity, Interbank Network Topology and Bank Capital By Aref Mahdavi Ardekani
  10. CoVaR with volatility clustering, heavy tails and non-linear dependence By Michele Leonardo Bianchi; Giovanni De Luca; Giorgia Rivieccio
  11. Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary By Timo Dimitriadis; Xiaochun Liu; Julie Schnaitmann
  12. A q Theory of Internal Capital Markets By Min Dai; Xavier Giroud; Wei Jiang; Neng Wang
  13. Using Machine Learning and Alternative Data to Predict Movements in Market Risk By Thomas Dierckx; Jesse Davis; Wim Schoutens
  14. Mean-variance portfolio selection with tracking error penalization By William Lefebvre; Gregoire Loeper; Huy\^en Pham
  15. Markov Decision Processes with Recursive Risk Measures By Nicole B\"auerle; Alexander Glauner
  16. Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging By Zongwu Cai; Chaoqun Ma; Xianhua Mi
  17. Robust Asymptotic Growth in Stochastic Portfolio Theory under Long-Only Constraints By David Itkin; Martin Larsson
  18. Oil Price Volatility and Stock Returns: Evidence from Three Oil-price Wars By Mushtaq Hussain Khan; Junaid Ahmed; Mazhar Mughal
  19. The Dark Side of the Bank Levy By Marcin BORSUK; Oskar KOWALEWSKI; Jianping QI
  20. Tail behavior of stopped L\'evy processes with Markov modulation By Brendan K. Beare; Won-Ki Seo; Alexis Akira Toda
  21. Implied Basket Correlation Dynamics By Wolfgang Karl H\"ardle; Elena Silyakova
  22. Pricing Cryptocurrency Options By Ai Jun Hou; Weining Wang; Cathy Y. H. Chen; Wolfgang Karl H\"ardle
  23. Generalized distance to a simplex and a new geometrical method for portfolio optimization By Fr\'ed\'eric Butin
  24. Capital Flows in Risky Times: Risk-on/Risk-off and Emerging Market Tail Risk By Anusha Chari; Karlye Dilts Stedman; Christian Lundblad
  25. Allocating Losses: Bail-ins, Bailouts and Bank Regulation By Todd Keister; Yuliyan Mitkov
  26. On the Stationarity of Futures Hedge Ratios By Degiannakis, Stavros; Floros, Christos; Salvador, Enrique; Vougas, Dimitrios
  27. A Generalised Stochastic Volatility in Mean VAR. An Updated Algorithm By Haroon Mumtaz
  28. Balancing Cryptoassets and Gold: A Weighted-Risk-Contribution Index for the Alternative Asset Space By Aikaterini Koutsouri; Francesco Poli; Elise Alfieri; Michael Petch; Walter Distaso; William Knottenbelt
  29. Short dated smile under Rough Volatility: asymptotics and numerics By Peter K. Friz; Paul Gassiat; Paolo Pigato
  30. Entrepreunial Orientation : Open Innovation Concept and Risk Management Measurement By mosse, Michelle veren
  31. A Horserace of Volatility Models for Cryptocurrency: Evidence from Bitcoin Spot and Option Markets By Yeguang Chi; Wenyan Hao
  32. Model-driven statistical arbitrage on LETF option markets By Sergey Nasekin; Wolfgang Karl H\"ardle
  33. Variance risk premia for agricultural commodities By Xi, Wenwen; Hayes, Dermot; Lence, Sergio Horacio

  1. By: Cheng Peng; Young Shin Kim
    Abstract: We propose a Markov regime switching GARCH model with multivariate normal tempered stable innovation to accommodate fat tails and other stylized facts in returns of financial assets. The model is used to simulate sample paths as input for portfolio optimization with risk measures, namely, conditional value at risk and conditional drawdown. The motivation is to have a portfolio that avoids left tail events by combining models that incorporates fat tail with optimization that focuses on tail risk. In-sample test is conducted to demonstrate goodness of fit. Out-of-sample test shows that our approach yields higher performance measured by Sharpe-like ratios than the market and equally weighted portfolio in recent years which includes some of the most volatile periods in history. We also find that suboptimal portfolios with higher return constraints tend to outperform optimal portfolios.
    Date: 2020–09
  2. By: Ling Wang (The Chinese University of Hong Kong); Mei Choi Chiu (The Education University of Hong Kong); Hoi Ying Wong (The Chinese University of Hong Kong)
    Abstract: While abundant empirical studies support the long-range dependence (LRD) of mortality rates, the corresponding impact on mortality securities are largely unknown due to the lack of appropriate tractable models for valuation and risk management purposes. We propose a novel class of Volterra mortality models that incorporate LRD into the actuarial valuation, retain tractability, and are consistent with the existing continuous-time affine mortality models. We derive the survival probability in closed-form solution by taking into account of the historical health records. The flexibility and tractability of the models make them useful in valuing mortality-related products such as death benefits, annuities, longevity bonds, and many others, as well as offering optimal mean-variance mortality hedging rules. Numerical studies are conducted to examine the effect of incorporating LRD into mortality rates on various insurance products and hedging efficiency.
    Date: 2020–09
  3. By: Walter Farkas (University of Zurich - Department of Banking and Finance; Swiss Finance Institute; ETH Zürich); Fulvia Fringuellotti (Federal Reserve Banks - Federal Reserve Bank of New York); Radu Tunaru (University of Sussex)
    Abstract: Capital adequacy is the key microprudential and macroprudential tool of banking regulation. Financial models of capital adequacy are subject to errors, which may prevent from estimating a sufficient capital base to absorb bank losses during economic downturns. In this paper, we propose a general method to account for model risk in capital requirements calculus related to market risk. We then evaluate and compare our capital requirements values with those obtained under Basel 2.5 and the new Basel 4 regulation. Capital requirements adjusted for model risk perform well in containing losses generates in normal and stressed times. In addition, they are as conservative as Basel 4 capital requirements, but they exhibit less fluctuations over time.
    Keywords: Basel framework, capital requirements, cost-benefit analysis, model risk
    JEL: D81 G17 G18
    Date: 2020–10
  4. By: Bruno Spilak; Wolfgang Karl H\"ardle
    Abstract: Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms has raised the necessity to display and understand the functionality of ML tools. In this paper, we present a dynamic tail risk protection strategy that targets a maximum predefined level of risk measured by Value-At-Risk while controlling for participation in bull market regimes. We propose different weak classifiers, parametric and non-parametric, that estimate the exceedance probability of the risk level from which we derive trading signals in order to hedge tail events. We then compare the different approaches both with statistical and trading strategy performance, finally we propose an ensemble classifier that produces a meta tail risk protection strategy improving both generalization and trading performance.
    Date: 2020–10
  5. By: Pelzl, Paul (Dept. of Business and Management Science, Norwegian School of Economics); Valderrama, Maria Teresa (Oesterreichische Nationalbank)
    Abstract: Drawdowns on credit commitments by firms reduce a bank’s capital buffer. Exploiting Austrian credit register data and the 2008-09 financial crisis as exogenous shock to bank health, we provide novel evidence that capital-constrained banks manage this concern by substantially cutting partly or fully unused credit commitments. Controlling for a bank’s capital position, we further find that also larger liquidity problems induce banks to cut such commitments. These results show that banks manage both capital and liquidity risk posed by undrawn credit commitments in periods of financial distress, but thereby reduce liquidity insurance to firms exactly when they need it most.
    Keywords: Capital Regulations; Credit Commitments; Financial Crisis
    JEL: E51 G01 G21 G28 G32
    Date: 2020–10–15
  6. By: Zhifeng Liu; Toan Luu Duc Huynh; Peng-Fei Dai
    Abstract: This paper investigates the impact of COVID-19 epidemic on the Chinese stock market crash risk. We first estimate conditional skewness of the return distribution from the GARCH-S model as the proxy of the equity market crash risk for the Shanghai Exchange Stock Market. Then, we construct a fear index for COVID-19 using the data from Baidu Index. Our findings show that the conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the epidemic increases the crash risk of stock market. Furthermore, we find that the fear sentiment also exacerbates the crash risk. In particular, the fear sentiment plays a significant role in the impact of COVID-19 on the crash risk. When the fear sentiment among people is high, the stock market crash risk is affected by the epidemic more seriously. Evidence from the daily deaths and global cases shows the robustness.
    Date: 2020–09
  7. By: R. Matthew Darst; Ehraz Refayet; Alexandros Vardoulakis
    Abstract: We study how competition between banks and non-banks affects lending standards. Banks have private information about some borrowers and are subject to capital requirements to mitigate risk-taking incentives from deposit insurance. Non-banks are uninformed and market forces determine their capital structure. We show that lending standards monotonically increase in bank capital requirements. Intuitively, higher capital requirements raise banks’ skin in the game and screening out bad projects assures positive expected lending returns. Non-banks enter the market when capital requirements are sufficiently high, but do not cause a deterioration in lending standards. Optimal capital requirements trade-off inefficient lending to bad projects under loose standards with inefficient collateral liquidation under tight standards.
    Keywords: Lending standards; Credit cycles; Asymmetric information; Non-banks; Regulation
    JEL: G01 G21 G28
    Date: 2020–10–09
  8. By: Arno Botha; Conrad Beyers; Pieter de Villiers
    Abstract: A novel procedure is presented for the objective comparison and evaluation of a bank's decision rules in optimising the timing of loan recovery. This procedure is based on finding a delinquency threshold at which the financial loss of a loan portfolio (or segment therein) is minimised. Our procedure is an expert system that incorporates the time value of money, costs, and the fundamental trade-off between accumulating arrears versus forsaking future interest revenue. Moreover, the procedure can be used with different delinquency measures (other than payments in arrears), thereby allowing an indirect comparison of these measures. We demonstrate the system across a range of credit risk scenarios and portfolio compositions. The computational results show that threshold optima can exist across all reasonable values of both the payment probability (default risk) and the loss rate (loan collateral). In addition, the procedure reacts positively to portfolios afflicted by either systematic defaults (such as during an economic downturn) or episodic delinquency (i.e., cycles of curing and re-defaulting). In optimising a portfolio's recovery decision, our procedure can better inform the quantitative aspects of a bank's collection policy than relying on arbitrary discretion alone.
    Date: 2020–09
  9. By: Aref Mahdavi Ardekani (Centre d'Economie de la Sorbonne)
    Abstract: By applying the interbank network simulation, this paper examines whether the causal relationship between capital and liquidity is influenced by bank positions in the interbank network. While existing literature highlights the causal relationship that moves from liquidity to capital, the question of how interbank network characteristics affect this relationship remains unclear. Using a sample of commercial banks from 28 European countries, this paper suggests that bank's interconnectedness within interbank loan and deposit networks affects their decisions to set higher or lower regulatory capital ratios when facing higher iliquidity. This study provides support for the need to implement minimum liquidity ratios to complement capital ratios, as stressed by the Basel Committee on Banking Regulation and Supervision. This paper also highlights the need for regulatory authorities to consider the network characteristics of banks
    Keywords: Interbank network topology; Bank regulatory capital; Liquidity risk; Basel III
    JEL: G21 G28 L14
    Date: 2020–10
  10. By: Michele Leonardo Bianchi; Giovanni De Luca; Giorgia Rivieccio
    Abstract: In this paper we estimate the conditional value-at-risk by fitting different multivariate parametric models capturing some stylized facts about multivariate financial time series of equity returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is got by AR-GARCH dynamics of the GJR type, the other stylized facts are captured through non-Gaussian multivariate models and copula functions. The CoVaR$^{\leq}$ is computed on the basis on the multivariate normal model, the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic model (MGH) and four possible copula functions. These risk measure estimates are compared to the CoVaR$^{=}$ based on the multivariate normal GARCH model. The comparison is conducted by backtesting the competitor models over the time span from January 2007 to March 2020. In the empirical study we consider a sample of listed banks of the euro area belonging to the main or to the additional global systemically important banks (GSIBs) assessment sample.
    Date: 2020–09
  11. By: Timo Dimitriadis; Xiaochun Liu; Julie Schnaitmann
    Abstract: We propose forecast encompassing tests for the Expected Shortfall (ES) jointly with the Value at Risk (VaR) based on flexible link (or combination) functions. Our setup allows testing encompassing for convex forecast combinations and for link functions which preclude crossings of the combined VaR and ES forecasts. As the tests based on these link functions involve parameters which are on the boundary of the parameter space under the null hypothesis, we derive and base our tests on nonstandard asymptotic theory on the boundary. Our simulation study shows that the encompassing tests based on our new link functions outperform tests based on unrestricted linear link functions for one-step and multi-step forecasts. We further illustrate the potential of the proposed tests in a real data analysis for forecasting VaR and ES of the S&P 500 index.
    Date: 2020–09
  12. By: Min Dai; Xavier Giroud; Wei Jiang; Neng Wang
    Abstract: We propose a tractable model of dynamic investment, division sales (spinoffs), financing, and risk management for a multi-division firm that faces costly external finance. The model highlights the importance of considering the intertwined nature of the different policies. Our main results are as follows: (1) risk management considerations prescribe the allocation of resources based not only on the divisions' productivity -- as in standard models of ''winner picking'' -- but also their risk; (2) firms may choose to voluntarily spin off productive divisions to increase liquidity; (3) diversification can reduce firm value especially in low liquidity states, as it increases the cost of a spinoff and hampers liquidity management; (4) with corporate socialism, liquidity is less valuable since it is less costly to replenish the firm's liquidity through a spinoff; and (5) division-level investment is set such that the ratio between marginal q and the marginal cost of investing in each division equals the marginal value of cash.
    JEL: D92 G3 L25
    Date: 2020–10
  13. By: Thomas Dierckx; Jesse Davis; Wim Schoutens
    Abstract: Using machine learning and alternative data for the prediction of financial markets has been a popular topic in recent years. Many financial variables such as stock price, historical volatility and trade volume have already been through extensive investigation. Remarkably, we found no existing research on the prediction of an asset's market implied volatility within this context. This forward-looking measure gauges the sentiment on the future volatility of an asset, and is deemed one of the most important parameters in the world of derivatives. The ability to predict this statistic may therefore provide a competitive edge to practitioners of market making and asset management alike. Consequently, in this paper we investigate Google News statistics and Wikipedia site traffic as alternative data sources to quantitative market data and consider Logistic Regression, Support Vector Machines and AdaBoost as machine learning models. We show that movements in market implied volatility can indeed be predicted through the help of machine learning techniques. Although the employed alternative data appears to not enhance predictive accuracy, we reveal preliminary evidence of non-linear relationships between features obtained from Wikipedia page traffic and movements in market implied volatility.
    Date: 2020–09
  14. By: William Lefebvre (LPSM); Gregoire Loeper (BNPP CIB GM Lab); Huy\^en Pham (LPSM)
    Abstract: This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in case of misspecified parameters, by "fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean-Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.
    Date: 2020–09
  15. By: Nicole B\"auerle; Alexander Glauner
    Abstract: In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state and action spaces and unbounded cost under both finite and infinite planning horizons. Our optimality criterion is based on the recursive application of static risk measures. This is motivated by recursive utilities in the economic literature, has been studied before for the entropic risk measure and is extended here to an axiomatic characterization of suitable risk measures. We derive a Bellman equation and prove the existence of Markovian optimal policies. For an infinite planning horizon, the model is shown to be contractive and the optimal policy to be stationary. Moreover, we establish a connection to distributionally robust MDPs, which provides a global interpretation of the recursively defined objective function. Monotone models are studied in particular.
    Date: 2020–10
  16. By: Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Chaoqun Ma (School of Business, Hunan University, Changsha, Hunan 410082, China); Xianhua Mi (School of Business, Hunan University, Changsha, Hunan 410082, China)
    Abstract: Heterogeneity, volatility persistence, leverage effect and fat right tails are the most documented stylized features of realized volatility (RV), which introduce substantial difficulties in econometric modeling that requires some rigid distributional assumptions. To accommodate these features without making these assumptions, we study the quantile forecasting of RV by proposing five novel dynamic model averaging strategies designed to combine individual quantile models, termed as dynamic quantile model averaging (DQMA). The empirical results of analyzing high-frequency price data of the S&P 500 index clearly indicate that the stylized facts of RV can be captured by different quantiles, with stronger effects at high-level quantiles. Therefore, DQMA can not only reduce the risk of model uncertainty but also generate more accurate and robust out-of-sample quantile forecasts than those of individual heterogeneous autoregressive quantile models.
    Keywords: Dynamic moving averaging; Model uncertainty; Fat tails; Heterogeneity; Quantile regression; Realized volatility; Time-varying parameters.
    JEL: C12 C13 C14 C23
    Date: 2020–09
  17. By: David Itkin; Martin Larsson
    Abstract: We consider the problem of maximizing the asymptotic growth rate of an investor under drift uncertainty in the setting of stochastic portfolio theory (SPT). As in the work of Kardaras and Robertson we take as inputs (i) a Markovian volatility matrix $c(x)$ and (ii) an invariant density $p(x)$ for the market weights, but we additionally impose long-only constraints on the investor. Our principal contribution is proving a uniqueness and existence result for the class of concave functionally generated portfolios and developing a finite dimensional approximation, which can be used to numerically find the optimum. In addition to the general results outlined above, we propose the use of a broad class of models for the volatility matrix $c(x)$, which can be calibrated to data and, under which, we obtain explicit formulas of the optimal unconstrained portfolio for any invariant density.
    Date: 2020–09
  18. By: Mushtaq Hussain Khan (University of Azad Jammu & Kashmir, Muzaffarabad.); Junaid Ahmed (Pakistan Institute of Development Economics, Islamabad.); Mazhar Mughal (Pau Business School, France.)
    Abstract: This study examines how crude oil price volatility affected the stock returns of major global oil and gas corporations during three major oil-price wars that took place between October 1991 and June 2020. Episodes considered include the 1998 Saudi Arabia—Venezuela war, the 2014-16 conflict and the 2020 Saudi Arabia—Russia war in a time of unprecedented crisis caused by the COVID-19 pandemic. Our findings reveal a significant evidence for volatility persistence and leverage effects in oil price during the three oil-price wars. These findings are consistent for WTI as well as Brent crude oil specifications. Though the persistence of volatility is similar to that of the previous two oilprice wars, the 2020 Saudi Arabia—Russia oil-price war has higher volatility spikes than the previous two wars. Besides, oil price shocks have a significant and positive effect on the returns of oil and gas companies. These findings provide information on how volatility in global oil prices is also sensitive to irregular events such as price wars between oil producers. This information can be important for economic agents contemplating shorter hedges by managing risks during times of high volatility.
    Keywords: Crude Oil; Oil and Gas Corporations; Oil-price Wars; Stock Returns; Volatility
    JEL: C32 G12 Q40 Q43
    Date: 2020
  19. By: Marcin BORSUK (European Central Bank, Frankfurt, Germany); Oskar KOWALEWSKI (IESEG School of Management & LEM-CNRS 9221); Jianping QI (Muma College of Business, University of South Florida, Tampa, USA)
    Abstract: We examine the consequences of imposing a high tax levy on bank assets. Employing unique supervisory bank-level data, we exploit different channels through which the new tax may impair the stability of the banking sector. We find that following the introduction of the levy, banks increase the cost of loans and decrease their overall availability to the real economy. We also document that changes in banks’ loan portfolios are strongly related to bank-specific profitability and capital adequacy ratios. Furthermore, our evidence supports the view that banks engage in risk shifting by increasing the risk level of their loan portfolios, attempting to recover from the lower return on equity as the tax reduces their overall profits.
    JEL: G21 H22 L13
    Date: 2020–08
  20. By: Brendan K. Beare; Won-Ki Seo; Alexis Akira Toda
    Abstract: This article concerns the tail probabilities of a light-tailed Markov-modulated L\'evy process stopped at a state-dependent Poisson rate. The tails are shown to decay exponentially at rates given by the unique positive and negative roots of the spectral abscissa of a certain matrix-valued function. We illustrate the use of our results with an application to the stationary distribution of wealth in a simple economic model in which agents with constant absolute risk aversion are subject to random mortality and income fluctuation.
    Date: 2020–09
  21. By: Wolfgang Karl H\"ardle; Elena Silyakova
    Abstract: Equity basket correlation can be estimated both using the physical measure from stock prices, and also using the risk neutral measure from option prices. The difference between the two estimates motivates a so-called "dispersion strategy''. We study the performance of this strategy on the German market and propose several profitability improvement schemes based on implied correlation (IC) forecasts. Modelling IC conceals several challenges. Firstly the number of correlation coefficients would grow with the size of the basket. Secondly, IC is not constant over maturities and strikes. Finally, IC changes over time. We reduce the dimensionality of the problem by assuming equicorrelation. The IC surface (ICS) is then approximated from the implied volatilities of stocks and the implied volatility of the basket. To analyze the dynamics of the ICS we employ a dynamic semiparametric factor model.
    Date: 2020–09
  22. By: Ai Jun Hou; Weining Wang; Cathy Y. H. Chen; Wolfgang Karl H\"ardle
    Abstract: Cryptocurrencies, especially Bitcoin (BTC), which comprise a new digital asset class, have drawn extraordinary worldwide attention. The characteristics of the cryptocurrency/BTC include a high level of speculation, extreme volatility and price discontinuity. We propose a pricing mechanism based on a stochastic volatility with a correlated jump (SVCJ) model and compare it to a flexible co-jump model by Bandi and Ren\`o (2016). The estimation results of both models confirm the impact of jumps and co-jumps on options obtained via simulation and an analysis of the implied volatility curve. We show that a sizeable proportion of price jumps are significantly and contemporaneously anti-correlated with jumps in volatility. Our study comprises pioneering research on pricing BTC options. We show how the proposed pricing mechanism underlines the importance of jumps in cryptocurrency markets.
    Date: 2020–09
  23. By: Fr\'ed\'eric Butin
    Abstract: Risk aversion plays a significant and central role in investors' decisions in the process of developing a portfolio. In this framework of portfolio optimization we determine the portfolio that possesses the minimal risk by using a new geometrical method. For this purpose, we elaborate an algorithm that enables us to compute any generalized Euclidean distance to a standard simplex. With this new approach, we are able to treat the case of portfolio optimization without short-selling in its entirety, and we also recover in geometrical terms the well-known results on portfolio optimization with allowed short-selling. Then, we apply our results in order to determine which convex combination of the CAC 40 stocks possesses the lowest risk: not only we get a very low risk compared to the index, but we also get a return rate that is almost three times better than the one of the index.
    Date: 2020–09
  24. By: Anusha Chari; Karlye Dilts Stedman; Christian Lundblad
    Abstract: This paper characterizes the implications of risk-on/risk-off shocks for emerging market capital flows and returns. We document that these shocks have important implications not only for the median of emerging markets flows and returns but also for the left tail. Further, while there are some differences in the effects across bond vs. equity markets and flows vs. asset returns, the effects associated with the worst realizations are generally larger than that on the median realization. We apply our methodology to the COVID-19 shock to examine the pattern of flow and return realizations: the sizable risk-off nature of this shock engenders reactions that reside deep in the left tail of most relevant emerging market quantities.
    JEL: F21 F3 G15
    Date: 2020–10
  25. By: Todd Keister; Yuliyan Mitkov
    Abstract: We study the interaction between a government’s bailout policy and banks’ willingness to impose losses on (or “bail in”) their investors. The government has limited commitment and may choose to bail out banks facing large losses. The anticipation of this bailout undermines a bank’s private incentive to impose a bail-in. In the resulting equilibrium, bail-ins are too small and bailouts are too large. Some banks may also face a run by informed investors, creating further distortions and leading to larger bailouts. We show how a regulator with limited information can raise welfare and improve financial stability by imposing a system-wide, mandatory bail-in at the onset of a crisis. In some situations, allowing banks to choose between meeting a minimum bail-in and opting out can raise welfare further.
    Keywords: Bank bailouts, moral hazard, financial stability, banking regulation
    JEL: E61 G18 G28
    Date: 2020–09
  26. By: Degiannakis, Stavros; Floros, Christos; Salvador, Enrique; Vougas, Dimitrios
    Abstract: Stationarity of hedge ratios can be viewed as a first step for portfolio hedging since it represents that the sensitivity of spot and futures returns follow a process whose main characteristics do not depend on time. However, we provide evidence that the hedge ratios of the main European stock indices are better described as a combination of two different mean-reverting stationary processes, which depend on the state of the market. Also, when analysing the dynamics of hedge ratios at intraday level, results display a similar picture suggesting that intraday dynamics of the hedge between spot and futures are driven mainly by market participants with similar perspectives of the investment horizon.
    Keywords: Futures, Hedge Ratios, Intra-day Data, Multivariate Volatility Modelling, Regime-Switching, Stationarity.
    JEL: C32 C58 G13 G15
    Date: 2020–08–01
  27. By: Haroon Mumtaz (Queen Mary University of London)
    Abstract: In this note we present an updated algorithm to estimate the VAR with stochastic volatility proposed in Mumtaz (2018). The model is re-written so that some of the Metropolis Hastings steps are avoided.
    Keywords: VAR, Stochastic volatility in mean, error covariance
    JEL: C3 C11 E3
    Date: 2020–07–05
  28. By: Aikaterini Koutsouri (Imperial College London); Francesco Poli (Imperial College London); Elise Alfieri (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA [2020-....] - Université Grenoble Alpes [2020-....]); Michael Petch; Walter Distaso (Imperial College London); William Knottenbelt (Imperial College London)
    Abstract: Bitcoin is foremost amongst the emerging asset class known as cryptoassets. Two noteworthy characteristics of the returns of non-stablecoin cryptoassets are their high volatility, which brings with it a high level of risk, and their high intraclass correlation, which limits the benefits that can be had by diversifying across multiple cryptoassets. Yet cryptoassets exhibit no correlation with gold, a highly-liquid yet scarce asset which has proved to function as a safe haven during crises affecting traditional financial systems. As exemplified by Shannon's Demon, a lack of correlation between assets opens the door to principled risk control through so-called volatility harvesting involving periodic rebalancing. In this paper we propose an index which combines a basket of five cryp-toassets with an investment in gold in a way that aims to improve the risk profile of the resulting portfolio while preserving its independence from mainstream financial asset classes such as stocks, bonds and fiat currencies. We generalise the theory of Equal Risk Contribution to allow for weighting according to a desired level of contribution to volatility. We find a crypto-gold weighting based on Weighted Risk Contribution to be historically more effective in terms of Sharpe Ratio than several alternative asset allocation strategies including Shannon's Demon. Within the crypto-basket, whose constituents are selected and rebalanced monthly, we find an Equal Weighting scheme to be more effective in terms of the same metric than a market capitalisation weighting.
    Date: 2019–05–06
  29. By: Peter K. Friz; Paul Gassiat; Paolo Pigato
    Abstract: In [Precise Asymptotics for Robust Stochastic Volatility Models; Ann. Appl. Probab. 2020] we introduce a new methodology to analyze large classes of (classical and rough) stochastic volatility models, with special regard to short-time and small noise formulae for option prices, using the framework [Bayer et al; A regularity structure for rough volatility; Math. Fin. 2020]. We investigate here the fine structure of this expansion in large deviations and moderate deviations regimes, together with consequences for implied volatility. We discuss computational aspects relevant for the practical application of these formulas. We specialize such expansions to prototypical rough volatility examples and discuss numerical evidence.
    Date: 2020–09
  30. By: mosse, Michelle veren
    Abstract: Entrepreneurial Orientation is an orientation part of entrepreneurship which consists of a process of making strategies and policies that form the basis of entrepreneurship (Rauch, Wiklund, Lumpkin & Frese). Entrepreneurial Orientation is an important thing as a basis for strength in an organization to improve the course of the entrepreneurial process and can lead to a pattern of business behavior that is applied if you want to maintain a business. There are 5 dimensions of entrepreneurial orientation, namely Innovation, fgProactiveness, Risktaking (Miller, 1983), Autonomy Orientation and Competitive Aggressiveness according to (Lumpkin & Dess, 1996). A company is said to be able to apply an entrepreneurial orientation if the company has characteristics such as first in market product innovation, has the courage to take risks, is proactive in making innovations.
    Date: 2020–10–01
  31. By: Yeguang Chi; Wenyan Hao
    Abstract: We test various volatility models using the Bitcoin spot price series. Our models include HIST, EMA ARCH, GARCH, and EGARCH, models. Both of our in-sample-fit and out-of-sample-forecast results suggest that GARCH and EGARCH models perform much better than other models. Moreover, the EGARCH model's asymmetric term is positive and insignificant, which suggests that Bitcoin prices lack the asymmetric volatility response to past returns. Finally, we formulate an option trading strategy by exploiting the volatility spread between the GARCH volatility forecast and the option's implied volatility. We show that a simple volatility-spread trading strategy with delta-hedging can yield robust profits.
    Date: 2020–10
  32. By: Sergey Nasekin; Wolfgang Karl H\"ardle
    Abstract: In this paper, we study the statistical properties of the moneyness scaling transformation by Leung and Sircar (2015). This transformation adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the discrepancy between the IV smiles for levered and unlevered ETF options. We construct bootstrap uniform confidence bands which indicate that the implied volatility smiles are statistically different after moneyness scaling has been performed. An empirical application shows that there are trading opportunities possible on the LETF market. A statistical arbitrage type strategy based on a dynamic semiparametric factor model is presented. This strategy presents a statistical decision algorithm which generates trade recommendations based on comparison of model and observed LETF implied volatility surface. It is shown to generate positive returns with a high probability. Extensive econometric analysis of LETF implied volatility process is performed including out-of-sample forecasting based on a semiparametric factor model and uniform confidence bands' study. It provides new insights into the latent dynamics of the implied volatility surface. We also incorporate Heston stochastic volatility into the moneyness scaling method for better tractability of the model.
    Date: 2020–09
  33. By: Xi, Wenwen; Hayes, Dermot; Lence, Sergio Horacio
    Abstract: We study the variance risk premium (i.e., the difference between historical realized variance and the variance swap rate) in corn and soybean markets from 2010 through 2016. Variance risk is negatively priced for both commodities, but is more statistically significant for soybean than for corn. There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the variance swap rate. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log variance swap rate. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns. The latter finding suggests that the variance risk is unspanned by commodity futures, i.e., it is an independent source of risk. The empirical results also suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15%. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.
    Date: 2019–01–01

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.