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on Risk Management |
| By: | Svetlana Litvinova; Mervyn J Silvapulle |
| Abstract: | In risk management areas such as reinsurance, the need often arises to construct a confidence interval for a quantile in the tail of the distribution. While different methods are available for this purpose, doubts have been raised about the validity of full-sample bootstrap. In this paper, we first obtain some general results on the validity of fullsample bootstrap for the tail quantile process. This opens the possibility of developing bootstrap methods based on tail statistics. Second, we develop a bootstrap method for constructing confidence intervals for high-quantiles of heavy-tailed distributions and show that it is consistent. In our simulation study, the bootstrap method for constructing confidence intervals for high quantiles performed overall better than the data tilting method, but none was uniformly the best; the data tilting method appears to be currently the preferred choice. Since the two methods are based on quite different approaches, we recommend that both methods be used side by side in applications. |
| Keywords: | Full-sample bootstrap, intermediate order statistic, Hill estimator, extreme value index, tail empirical process, tail quantile process |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2018-12&r=rmg |
| By: | Schwenke, Eric; Davis, Todd |
| Abstract: | This poster reports the return to on-farm and commercial storage for Western Kentucky corn and soybeans for the 2000 to 2016 crops. The risk management potential provided by a storage hedge with the July corn and soybean futures contract is demonstrated. |
| Date: | 2018–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:ags:saea18:266702&r=rmg |
| By: | Helder Rojas; David Dias |
| Abstract: | In this article, we are interested in evaluating the resilience of the financial portfolios under extreme economic conditions. Therefore, we use empirical measures that characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described using the Bayesian approach, using the prior information contained in the impact measures. In addition, we illustrate the use of the estimated models from the credit risk data of a portfolio. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.07401&r=rmg |
| By: | Ramadiah, Amanah; Caccioli, Fabio; Fricke, Daniel |
| Abstract: | Financial networks are an important source of systemic risk, but often only partial network information is available. In this paper, we use data on bank-firm credit relationships in Japan and conduct a horse race between different network reconstruction methods in terms of their ability to reproduce the actual credit networks. We then compare the different reconstruction methods in terms of their implied systemic risk levels. In most instances we find that the observed credit network significantly displays the highest systemic risk level. Lastly, we explore different policies to improve the robustness of the system. JEL Classification: G11, G20, G21, G28, G32 |
| Keywords: | aggregation level, bipartite credit network, network reconstruction, stress testing, systemic risk |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkwps:201884&r=rmg |
| By: | Soumyatanu Mukherjee; Udo Broll |
| Abstract: | This paper presents a mean-variance decision making approach in the context of a risk-averse exporting firm, for analysing its optimal production and exporting decision in the portfolio of sales towards domestic and foreign markets, under unfair background risk, such as greater chance of loss for the export credit insurance (possibly offered under non-proportional reimbursement), or unprecedented negative externalities imposed by the partner country’s government on the home country’s export policies. Then this paper traces out the comparative static responses of optimal export sales owing to the changes in distribution, size, or in the dependence structure of the background risk. Adaptation of the mean-variance decision-theoretic model helps obtaining all the results in terms of monotonicity and curvature properties of the marginal willingness of substitution between risk and return, with simple yet intuitive interpretations. |
| Keywords: | Exports; Unfair background risk; Decision under risk; Mean-variance model; Risk aversion. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:not:notgep:18/11&r=rmg |
| By: | Kei Nakagawa; Takumi Uchida; Tomohisa Aoshima |
| Abstract: | We propose to represent a return model and risk model in a unified manner with deep learning, which is a representative model that can express a nonlinear relationship. Although deep learning performs quite well, it has significant disadvantages such as a lack of transparency and limitations to the interpretability of the prediction. This is prone to practical problems in terms of accountability. Thus, we construct a multifactor model by using interpretable deep learning. We implement deep learning as a return model to predict stock returns with various factors. Then, we present the application of layer-wise relevance propagation (LRP) to decompose attributes of the predicted return as a risk model. By applying LRP to an individual stock or a portfolio basis, we can determine which factor contributes to prediction. We call this model a deep factor model. We then perform an empirical analysis on the Japanese stock market and show that our deep factor model has better predictive capability than the traditional linear model or other machine learning methods. In addition , we illustrate which factor contributes to prediction. |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1810.01278&r=rmg |
| By: | Fischer, Thomas (Department of Economics, Lund University); Lundtofte , Frederik (Department of Economics, Lund University) |
| Abstract: | We apply the Atkinson (1970) inequality index to time series of asset returns to offer a novel measure of financial risk consistent with expected-utility theory. This measure is converted to a certainty-equivalent return serving as a performance measure. We extend the Atkinson index to HARA utility and derive closed-form solutions to our measures for a number of preference-return combinations. Further, we establish relationships between risk aversion and the weights assigned to the cumulants of the return distribution for our performance measure. Using data from hedge funds and asset-pricing anomalies, we find that our performance measure contains additional, economically meaningful information. |
| Keywords: | risk; performance; non-Gaussian distributions; cumulants; hedge funds |
| JEL: | G11 |
| Date: | 2018–10–03 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lunewp:2018_025&r=rmg |
| By: | Van Tassel, Peter (Federal Reserve Bank of New York) |
| Abstract: | This paper provides empirical evidence that volatility markets are integrated through the time-varying term structure of variance risk premia. These risk premia predict the returns from selling volatility for different horizons, maturities, and products, including variance swaps, straddles, and VIX futures. In addition, the paper derives a closed-form relationship between the prices of variance swaps and VIX futures. While tightly linked, VIX futures exhibit deviations of varying significance from the no-arbitrage prices and bounds implied by the variance swap market. The paper examines these pricing errors and their relationship to VIX futures’ return predictability. |
| Keywords: | variance swaps; term structure; variance risk premium; VIX futures; options; return predictability |
| JEL: | C58 G12 G13 |
| Date: | 2018–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:867&r=rmg |
| By: | Gong, Xuche; Feng, Hongli; Hennessy, David A. |
| Keywords: | Risk and Uncertainty, Ag Finance and Farm Management, Food and Agricultural Policy Analysis |
| Date: | 2018–06–20 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea18:274479&r=rmg |
| By: | Ferriani, Fabrizio; Natoli, Filippo; Veronese, Giovanni; Zeni, Federica |
| Abstract: | The advent of shale oil in the United States triggered a structural transformation in the oil market. We show, both theoretically and empirically, that this process has relevant consequences on oil risk premia. We construct a consumption-based model with shale producers interacting with financial speculators in the futures market. Compared to conventionals, shale producers have a more flexible technology, but higher risk aversion and additional costs due to their reliance on external finance. Our model helps to explain the observed pattern of aggregate hedging by US firms in the last decade. The empirical analysis shows that the hedging pressure of shale producers has become more relevant than that of conventional producers in explaining the oil futures risk premium. |
| Keywords: | shale oil, futures, risk premium, hedging, speculation, limits to arbitrage |
| JEL: | G00 G13 G32 Q43 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:89097&r=rmg |
| By: | Simon Oh; Jessica A. Wachter |
| Abstract: | This paper evaluates skewness in the cross-section of stock returns in light of predictions from a well-known class of models. Cross-sectional skewness in monthly returns far exceeds what the standard lognormal model of returns would predict. However, skewness in long-run returns substantially understates what the lognormal model would predict. Nonstationary share dynamics imply a breakdown in the distinction between market and idiosyncratic risk in the lognormal model. We present an alternative model that matches the skewness in the data and implies stationary wealth shares. In this model, idiosyncratic risk is the primary driver of growth in the economy. |
| JEL: | G12 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25113&r=rmg |
| By: | Massimo Guidolin; Alexei G. Orlov |
| Abstract: | We report systematic, out-of-sample evidence on the benefits to an already well diversified investor that may derive from further diversification into various hedge fund strategies. We investigate dynamic strategic asset allocation decisions that take into account investors’ preferences as well as return predictability. Our results suggest that not all hedge fund strategies benefit a long-term investor who is already well diversified across stocks, government and corporate bonds, and REITs. Only strategies whose payoffs are highly nonlinear (e.g., fixed income relative value and convertible arbitrage), and therefore not easily replicable, constitute viable options. Most of the realized economic value fails to result from a mean-variance type of improvement but comes instead from an improvement in realized higher-moment properties of optimal portfolios. Medium to highly risk-averse investors benefit the most from this alternative asset class. |
| Keywords: | Strategic asset allocation, hedge fund strategies, predictive regressions, out-of-sample performance, certainty equivalent return |
| JEL: | G11 G17 G12 C53 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp1887&r=rmg |
| By: | Julián Andrada-Félixa (Department of Quantitative Methods in Economics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.); Adrian Fernandez-Perez (Department of Finance, Auckland University of Technology, Auckland, New Zealand.); Fernando Fernández-Rodríguez (Department of Quantitative Methods in Economics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.); Simón Sosvilla-Rivero (Complutense Institute for International Studies, Universidad Complutense de Madrid. Madrid, Spain.) |
| Abstract: | This paper examines the interconnection between four implied volatility indices representative of the investors' consensus view of expected stock market volatility at different maturities during the period January 3, 2011-May 4, 2018. To this end, we first perform a static analysis to measure the total volatility connectedness in the entire period using a framework proposed by Diebold and Yilmaz (2014). Second, we apply a dynamic analysis to evaluate both the net directional connectedness for each market using the TVP-VAR connectedness approach developed by Antonakakis and Gabauer (2017). Our results suggest that a 72.27%, of the total variance of the forecast errors is explained by shocks across the examined investor time horizons, indicating that the remainder 27.73% of the variation is due to idiosyncratic shocks. Furthermore, we find that volatility connectedness varies over time, with a surge during periods of increasing economic and financial instability. Finally, we also document a superior performance of the TVP-VAR approach to connectedness respect to the original one proposed by Diebold and Yilmaz (2014). |
| Keywords: | Implied volatility indices, Financial market Linkages, Connectedness, Vector Autoregression, Variance Decomposition. JEL classification:C53, E44, F31, G15 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201818&r=rmg |
| By: | Misha Perepelitsa |
| Abstract: | We describe a simple model for speculative trading based on adaptive behavior of economic agents.The adaptive behavior is expressed through a feedback mechanism for changing agents' stock-to-bond ratios, depending on the past performance of their portfolios.The stock price is set according to the demand-supply for the asset derived from the agents' target risk levels. Using the methodology of agent-based modeling we show that agents, acting endogenously and adaptively, create a persistent price bubble. The price dynamics generated by the trading process does not reveal any singularities, however the process is accompanied by growing aggregated risk that indicates increasing likelihood of a crash. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.09601&r=rmg |
| By: | Inoue, Hitoshi; Nakashima, Kiyotaka; Takahashi, Koji |
| Abstract: | We examine the reason that there have coexisted the two opposing views on distressed banks' lending behavior in Japan's post-bubble period: the one is the stagnant lending in a capital crunch and the other is the forbearance lending to low-quality borrowers. To this end, we address the measurement problem for bank balance sheet risk. We identify the credit supply and allocation effects of bank capital in the bank loan equation specified at loan level, thereby finding that the ``parallel worlds'', or the two opposing views, emerge because the regulatory capital does not reflect the actual condition of increased risk on bank balance sheet, while the market value of capital does. By uncovering banks' engagement in patching-up of the regulatory capital in the Japan's post-bubble period, we show that lowly market capitalized banks that had difficulty in building up adequate equity capital for their risk exposure decreased the overall supply of credits. The parallels world can emerge whenever banks are allowed to overvalue assets with their discretion, as in Japan' post-bubble period. |
| Keywords: | bank capital structure; capital crunch; forbearance lending; loan-level data; uncertainty; bank risk taking. |
| JEL: | G01 G21 G28 |
| Date: | 2018–07–26 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:89088&r=rmg |
| By: | Carlos Carvalho (University of Texas at Austin); Jared D. Fisher (University of Texas at Austin); Davide Pettenuzzo (Brandeis University, Department of Economics) |
| Abstract: | We introduce a simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian Dynamic Linear Models of West and Harrison (1997), and it can objectively determine, through a fully automated procedure, both the optimal set of regressors to include in the predictive system and the degree to which the model coefficients, volatilities, and covariances should vary over time. When applied to a portfolio of five stock and bond returns, we find that our method leads to large forecast gains, both in statistical and economic terms. In particular, we find that relative to a standard no-predictability benchmark, the optimal combination of predictors, stochastic volatility, and time-varying covariances increases the annualized certainty equivalent returns of a leverage-constrained power utility investor by more than 500 basis points. |
| Keywords: | Optimal asset allocation, Bayesian econometrics, Dynamic Linear models |
| JEL: | C11 C22 G11 G12 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:brd:wpaper:123&r=rmg |
| By: | Nikolai Dokuchaev |
| Abstract: | The paper studies pricing of insurance products focusing on the pricing of annuities under uncertainty. This pricing problem is crucial for financial decision making and was studied intensively, however, many open questions still remain. In particular, there is a so-called "annuity puzzle" related to certain inconsistency of existing financial theory with the empirical observations for the annuities market. The paper suggests a pricing method based on the risk minimization such that both producer and customer seek to minimize the mean square hedging error accepted as a measure of risk. This leads to two different versions of the pricing problem: the selection of the annuity price given the rate of regular payments, and the selection of the rate of payments given the annuity price. It appears that solutions of these two problems are different. This can contribute to explanation for the "annuity puzzle". |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.08960&r=rmg |
| By: | Damian Damianov; Cheng Yan; Xiangdong Wang |
| Abstract: | Following the financial crisis, a voluminous literature has developed that aims to shed light on the endogenous relationship between mortgage default risk and house prices. In this paper we contribute to this literature by using measures of mortgage default risk reflecting different stages of the household default decision: from early online searches to actual default, to the resale of the foreclosed home. We use a Panel Vector Autoregressive (PVAR) model to examine the impact of these default risk measures on two segments of residential real estate markets (top and bottom price tiers) from 92 metropolitan areas in 25 US states. We find that the default risk derived from households’ Google searches has the strongest negative impact on high value homes while the percentage of home foreclosed and the foreclosure resales have the strongest negative impact on the prices of low value homes. These results hold for both judicial and non-judicial foreclosure states as well as ‘recourse’ states. In ‘non-recourse’ states the number of homes foreclosed has the strongest negative impact on high value homes, which we interpret as evidence in support of the ”double trigger hypothesis.” That is, households default not only because they are in financial distress but also because they end up with a negative equity in their homes considering current house prices. |
| Keywords: | Foreclosure; House Prices; Mortgage Default Risk |
| JEL: | R3 |
| Date: | 2018–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2018_163&r=rmg |
| By: | Nalan Basturk (Maastricht University); Agnieszka Borowska (Tinbergen Institute and VU University Amsterdam); Stefano Grassi (University of Rome, Tor Vergata); Lennart Hoogerheide (Tinbergen Institute and VU University Amsterdam); Herman K. van Dijk (Tinbergen Institute, Erasmus University Rotterdam and Norges Bank) |
| Abstract: | A dynamic asset-allocation model is specified in probabilistic terms as a combination of return distributions resulting from multiple pairs of dynamic models and portfolio strategies based on momentum patterns in US industry returns. The nonlinear state space representation of the model allows efficient and robust simulation-based Bayesian inference using a novel non-linear filter. Combination weights can be crosscorrelated and correlated over time using feedback mechanisms. Diagnostic analysis gives insight into model and strategy misspecification. Empirical results show that a smaller flexible model-strategy combination performs better in terms of expected return and risk than a larger basic model-strategy combination. Dynamic patterns in combination weights and diagnostic learning provide useful signals for improved modelling and policy, in particular, from a risk-management perspective. |
| Date: | 2018–10–08 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2018_10&r=rmg |
| By: | Stephen Roulac; Alastair Adair; Stanley McGreal |
| Abstract: | Extending award-winning research prior papers were recognized with best paper awards at the ERES meeting and two ARES meetings - into the key drivers that create and destroy value in real estate development and deal making, this study considers the assessment as of 2016 of research initially conducted a decade ago to examine the commonalities and divergences between different approaches to development and deal-making. Through analyzing the relative contributions that major categories of activities and specific sub tasks make to creating and destroying values in real estate development and deal making, insights emerge that can guide important strategic decisions. In particular, this research isolates those tasks that are of common relative importance and development in deal making in contributing to value being created and value being destroyed. And, the research explores those tasks whose relative importance diverges in the value creation and value destruction process for development and deal making ventures. The results of this research can provide guidance for opportunistic strategies to create value and to mitigate against value migration and loss. These findings have powerful applications to entrepreneurial firms engaged in deal making activities, to firms engaged in real estate development, to investment managers committing capital to both deal making initiatives and development ventures, and to investors. |
| Keywords: | Dealmaking; Development; Professional Development; Strategy; Value |
| JEL: | R3 |
| Date: | 2018–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2018_340&r=rmg |